WorldWideScience

Sample records for lesion segmentation based

  1. Automatic segmentation of psoriasis lesions

    Science.gov (United States)

    Ning, Yang; Shi, Chenbo; Wang, Li; Shu, Chang

    2014-10-01

    The automatic segmentation of psoriatic lesions is widely researched these years. It is an important step in Computer-aid methods of calculating PASI for estimation of lesions. Currently those algorithms can only handle single erythema or only deal with scaling segmentation. In practice, scaling and erythema are often mixed together. In order to get the segmentation of lesions area - this paper proposes an algorithm based on Random forests with color and texture features. The algorithm has three steps. The first step, the polarized light is applied based on the skin's Tyndall-effect in the imaging to eliminate the reflection and Lab color space are used for fitting the human perception. The second step, sliding window and its sub windows are used to get textural feature and color feature. In this step, a feature of image roughness has been defined, so that scaling can be easily separated from normal skin. In the end, Random forests will be used to ensure the generalization ability of the algorithm. This algorithm can give reliable segmentation results even the image has different lighting conditions, skin types. In the data set offered by Union Hospital, more than 90% images can be segmented accurately.

  2. Background based Gaussian mixture model lesion segmentation in PET

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    Soffientini, Chiara Dolores, E-mail: chiaradolores.soffientini@polimi.it; Baselli, Giuseppe [DEIB, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133 (Italy); De Bernardi, Elisabetta [Department of Medicine and Surgery, Tecnomed Foundation, University of Milano—Bicocca, Monza 20900 (Italy); Zito, Felicia; Castellani, Massimo [Nuclear Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milan 20122 (Italy)

    2016-05-15

    Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was

  3. A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

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    Liu, Hui; Liu, Yiping; Qiu, Tianshuang [Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024 (China); Zhao, Zuowei, E-mail: liuhui@dlut.edu.cn [Second Affiliated Hospital, Dalian Medical University, Dalian 116027 (China); Zhang, Lina [Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian 116027 (China)

    2014-08-15

    Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.

  4. Novel techniques for enhancement and segmentation of acne vulgaris lesions.

    Science.gov (United States)

    Malik, A S; Humayun, J; Kamel, N; Yap, F B-B

    2014-08-01

    More than 99% acne patients suffer from acne vulgaris. While diagnosing the severity of acne vulgaris lesions, dermatologists have observed inter-rater and intra-rater variability in diagnosis results. This is because during assessment, identifying lesion types and their counting is a tedious job for dermatologists. To make the assessment job objective and easier for dermatologists, an automated system based on image processing methods is proposed in this study. There are two main objectives: (i) to develop an algorithm for the enhancement of various acne vulgaris lesions; and (ii) to develop a method for the segmentation of enhanced acne vulgaris lesions. For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. Then, segmentation is performed by clustering the pixels based on Mahalanobis distance of each pixel from spectral models of acne vulgaris lesions. Two metrics are used to evaluate the enhancement of acne vulgaris lesions, i.e., contrast improvement factor (CIF) and image contrast normalization (ICN). The proposed algorithm is compared with two other methods. The proposed enhancement algorithm shows better result than both the other methods based on CIF and ICN. In addition, sensitivity and specificity are calculated for the segmentation results. The proposed segmentation method shows higher sensitivity and specificity than other methods. This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  5. A toolbox for multiple sclerosis lesion segmentation

    International Nuclear Information System (INIS)

    Roura, Eloy; Oliver, Arnau; Valverde, Sergi; Llado, Xavier; Cabezas, Mariano; Pareto, Deborah; Rovira, Alex; Vilanova, Joan C.; Ramio-Torrenta, Lluis

    2015-01-01

    Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images. Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image. The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches. Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities. (orig.)

  6. A toolbox for multiple sclerosis lesion segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Roura, Eloy; Oliver, Arnau; Valverde, Sergi; Llado, Xavier [University of Girona, Computer Vision and Robotics Group, Girona (Spain); Cabezas, Mariano; Pareto, Deborah; Rovira, Alex [Vall d' Hebron University Hospital, Magnetic Resonance Unit, Dept. of Radiology, Barcelona (Spain); Vilanova, Joan C. [Girona Magnetic Resonance Center, Girona (Spain); Ramio-Torrenta, Lluis [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, Multiple Sclerosis and Neuroimmunology Unit, Girona (Spain)

    2015-10-15

    Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images. Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image. The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches. Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities. (orig.)

  7. An ICA-based method for the segmentation of pigmented skin lesions in macroscopic images.

    Science.gov (United States)

    Cavalcanti, Pablo G; Scharcanski, Jacob; Di Persia, Leandro E; Milone, Diego H

    2011-01-01

    Segmentation is an important step in computer-aided diagnostic systems for pigmented skin lesions, since that a good definition of the lesion area and its boundary at the image is very important to distinguish benign from malignant cases. In this paper a new skin lesion segmentation method is proposed. This method uses Independent Component Analysis to locate skin lesions in the image, and this location information is further refined by a Level-set segmentation method. Our method was evaluated in 141 images and achieved an average segmentation error of 16.55%, lower than the results for comparable state-of-the-art methods proposed in literature.

  8. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    Directory of Open Access Journals (Sweden)

    Saurabh Jain

    2015-01-01

    Full Text Available The location and extent of white matter lesions on magnetic resonance imaging (MRI are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS. Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM and the appearance (hyperintense on FLAIR of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default

  9. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.

    Science.gov (United States)

    Agner, Shannon C; Xu, Jun; Madabhushi, Anant

    2013-03-01

    Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. On a cohort of 50 breast DCE-MRI studies, Pr

  10. Segmentation of multiple sclerosis lesions in MR images: a review

    International Nuclear Information System (INIS)

    Mortazavi, Daryoush; Kouzani, Abbas Z.; Soltanian-Zadeh, Hamid

    2012-01-01

    Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels. (orig.)

  11. Segmentation of multiple sclerosis lesions in MR images: a review

    Energy Technology Data Exchange (ETDEWEB)

    Mortazavi, Daryoush; Kouzani, Abbas Z. [Deakin University, School of Engineering, Geelong, Victoria (Australia); Soltanian-Zadeh, Hamid [Henry Ford Health System, Image Analysis Laboratory, Radiology Department, Detroit, MI (United States); University of Tehran, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, Tehran (Iran, Islamic Republic of); School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran (Iran, Islamic Republic of)

    2012-04-15

    Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels. (orig.)

  12. Validating PET segmentation of thoracic lesions-is 4D PET necessary?

    DEFF Research Database (Denmark)

    Nielsen, M. S.; Carl, J.

    2017-01-01

    Respiratory-induced motions are prone to degrade the positron emission tomography (PET) signal with the consequent loss of image information and unreliable segmentations. This phantom study aims to assess the discrepancies relative to stationary PET segmentations, of widely used semiautomatic PET...... segmentation methods on heterogeneous target lesions influenced by motion during image acquisition. Three target lesions included dual F-18 Fluoro-deoxy-glucose (FDG) tracer concentrations as high-and low tracer activities relative to the background. Four different tracer concentration arrangements were...... segmented using three SUV threshold methods (Max40%, SUV40% and 2.5SUV) and a gradient based method (GradientSeg). Segmentations in static 3D-PET scans (PETsta) specified the reference conditions for the individual segmentation methods, target lesions and tracer concentrations. The motion included PET...

  13. Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

    DEFF Research Database (Denmark)

    Puonti, Oula; Van Leemput, Koen

    2016-01-01

    In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....

  14. Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

    Science.gov (United States)

    Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

    2017-05-01

    In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

  15. Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI

    Science.gov (United States)

    Agner, Shannon C.; Xu, Jun; Rosen, Mark; Karthigeyan, Sudha; Englander, Sarah; Madabhushi, Anant

    2011-03-01

    Spectral embedding (SE), a graph-based manifold learning method, has previously been shown to be useful in high dimensional data classification. In this work, we present a novel SE based active contour (SEAC) segmentation scheme and demonstrate its applications in lesion segmentation on breast dynamic contrast enhance magnetic resonance imaging (DCE-MRI). In this work, we employ SE on DCE-MRI on a per voxel basis to embed the high dimensional time series intensity vector into a reduced dimensional space, where the reduced embedding space is characterized by the principal eigenvectors. The orthogonal eigenvector-based data representation allows for computation of strong tensor gradients in the spectrally embedded space and also yields improved region statistics that serve as optimal stopping criteria for SEAC. We demonstrate both analytically and empirically that the tensor gradients in the spectrally embedded space are stronger than the corresponding gradients in the original grayscale intensity space. On a total of 50 breast DCE-MRI studies, SEAC yielded a mean absolute difference (MAD) of 3.2+/-2.1 pixels and mean Dice similarity coefficient (DSC) of 0.74+/-0.13 compared to manual ground truth segmentation. An active contour in conjunction with fuzzy c-means (FCM+AC), a commonly used segmentation method for breast DCE-MRI, produced a corresponding MAD of 7.2+/-7.4 pixels and mean DSC of 0.58+/-0.32. In conjunction with a set of 6 quantitative morphological features automatically extracted from the SEAC derived lesion boundary, a support vector machine (SVM) classifier yielded an area under the curve (AUC) of 0.73, for discriminating between 10 benign and 30 malignant lesions; the corresponding SVM classifier with the FCM+AC derived morphological features yielded an AUC of 0.65.

  16. Robust segmentation of focal lesions on multi-sequence MRI in multiple sclerosis

    International Nuclear Information System (INIS)

    Garcia-Lorenzo, Daniel

    2010-01-01

    Multiple sclerosis (MS) affects around 80.000 people in France. Magnetic resonance imaging (MRI) is an essential tool for diagnosis of MS and MRI-derived surrogate markers such as MS lesion volumes are often used as measures in MS clinical trials for the development of new treatments. The manual segmentation of these MS lesions is a time-consuming task that shows high inter- and intra-rater variability. We developed an automatic work flow for the segmentation of focal MS lesions on MRI. The segmentation method is based on the robust estimation of a parametric model of the intensities of the brain; lesions are detected as outliers to the model. We proposed two methods to include spatial information in the segmentation using mean shift and graph cut. We performed a quantitative evaluation of our work flow using synthetic and clinical images of two different centers to verify its accuracy and robustness. (author)

  17. OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

    Science.gov (United States)

    Sweeney, Elizabeth M; Shinohara, Russell T; Shiee, Navid; Mateen, Farrah J; Chudgar, Avni A; Cuzzocreo, Jennifer L; Calabresi, Peter A; Pham, Dzung L; Reich, Daniel S; Crainiceanu, Ciprian M

    2013-01-01

    Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78

  18. Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

    Directory of Open Access Journals (Sweden)

    Sandra González-Villà

    2017-01-01

    Full Text Available In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI’12. The Dice similarity coefficient (DSC differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. However, the effects of the lesions do not follow the same pattern; the lesions either make the segmentation method underperform or surprisingly augment the segmentation accuracy. The obtained results show that FreeSurfer is the method most affected by the presence of lesions, with DSC differences (generated − healthy ranging from −0.11 ± 0.54 to 9.65 ± 9.87, whereas FIRST tends to be the most robust method when lesions are present (−2.40 ± 5.54 to 0.44 ± 0.94. Lesion location is not important for global strategies such as FreeSurfer or majority voting, where structure segmentation is affected wherever the lesions exist. On the other hand, FIRST is more affected when the lesions are overlaid or close to the structure of analysis. The most affected structure by the presence of lesions is the nucleus accumbens (from −1.12 ± 2.53 to 1.32 ± 4.00 for the left hemisphere and from −2.40 ± 5.54 to 9.65 ± 9.87 for the right hemisphere, whereas the

  19. Automated segmentation of pigmented skin lesions in multispectral imaging

    International Nuclear Information System (INIS)

    Carrara, Mauro; Tomatis, Stefano; Bono, Aldo; Bartoli, Cesare; Moglia, Daniele; Lualdi, Manuela; Colombo, Ambrogio; Santinami, Mario; Marchesini, Renato

    2005-01-01

    The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions. (note)

  20. A level set method for multiple sclerosis lesion segmentation.

    Science.gov (United States)

    Zhao, Yue; Guo, Shuxu; Luo, Min; Shi, Xue; Bilello, Michel; Zhang, Shaoxiang; Li, Chunming

    2018-06-01

    In this paper, we present a level set method for multiple sclerosis (MS) lesion segmentation from FLAIR images in the presence of intensity inhomogeneities. We use a three-phase level set formulation of segmentation and bias field estimation to segment MS lesions and normal tissue region (including GM and WM) and CSF and the background from FLAIR images. To save computational load, we derive a two-phase formulation from the original multi-phase level set formulation to segment the MS lesions and normal tissue regions. The derived method inherits the desirable ability to precisely locate object boundaries of the original level set method, which simultaneously performs segmentation and estimation of the bias field to deal with intensity inhomogeneity. Experimental results demonstrate the advantages of our method over other state-of-the-art methods in terms of segmentation accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features

    International Nuclear Information System (INIS)

    Mahmood, F.H.

    2012-01-01

    Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer.The calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years. This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two to stages performed using the minimum distance (M D) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLC M for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from A L-llwiya Hospital in Baghdad, Iraq.The proposed technique shows better results.

  2. Large deep neural networks for MS lesion segmentation

    Science.gov (United States)

    Prieto, Juan C.; Cavallari, Michele; Palotai, Miklos; Morales Pinzon, Alfredo; Egorova, Svetlana; Styner, Martin; Guttmann, Charles R. G.

    2017-02-01

    Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

  3. Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

    Science.gov (United States)

    Lu, Kongkuo; Hall, Christopher S.

    2014-03-01

    Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

  4. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

    Directory of Open Access Journals (Sweden)

    Sergi Valverde

    2015-01-01

    Full Text Available Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM and white matter (WM using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.

  5. Independent histogram pursuit for segmentation of skin lesions

    DEFF Research Database (Denmark)

    Gomez, D.D.; Butakoff, C.; Ersbøll, Bjarne Kjær

    2008-01-01

    In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (HIP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first estima...... to deal with different types of dermatological lesions. The boundary detection precision using k-means segmentation was close to 97%. The proposed algorithm can be easily combined with the majority of classification algorithms....

  6. Exploring DeepMedic for the purpose of segmenting white matter hyperintensity lesions

    Science.gov (United States)

    Lippert, Fiona; Cheng, Bastian; Golsari, Amir; Weiler, Florian; Gregori, Johannes; Thomalla, Götz; Klein, Jan

    2018-02-01

    DeepMedic, an open source software library based on a multi-channel multi-resolution 3D convolutional neural network, has recently been made publicly available for brain lesion segmentations. It has already been shown that segmentation tasks on MRI data of patients having traumatic brain injuries, brain tumors, and ischemic stroke lesions can be performed very well. In this paper we describe how it can efficiently be used for the purpose of detecting and segmenting white matter hyperintensity lesions. We examined if it can be applied to single-channel routine 2D FLAIR data. For evaluation, we annotated 197 datasets with different numbers and sizes of white matter hyperintensity lesions. Our experiments have shown that substantial results with respect to the segmentation quality can be achieved. Compared to the original parametrization of the DeepMedic neural network, the timings for training can be drastically reduced if adjusting corresponding training parameters, while at the same time the Dice coefficients remain nearly unchanged. This enables for performing a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics board which makes this library also very interesting for research purposes on low-end GPU hardware.

  7. A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

    Science.gov (United States)

    Wu, T. Y.; Lin, S. F.

    2013-10-01

    Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

  8. Interactive lesion segmentation with shape priors from offline and online learning.

    Science.gov (United States)

    Shepherd, Tony; Prince, Simon J D; Alexander, Daniel C

    2012-09-01

    In medical image segmentation, tumors and other lesions demand the highest levels of accuracy but still call for the highest levels of manual delineation. One factor holding back automatic segmentation is the exemption of pathological regions from shape modelling techniques that rely on high-level shape information not offered by lesions. This paper introduces two new statistical shape models (SSMs) that combine radial shape parameterization with machine learning techniques from the field of nonlinear time series analysis. We then develop two dynamic contour models (DCMs) using the new SSMs as shape priors for tumor and lesion segmentation. From training data, the SSMs learn the lower level shape information of boundary fluctuations, which we prove to be nevertheless highly discriminant. One of the new DCMs also uses online learning to refine the shape prior for the lesion of interest based on user interactions. Classification experiments reveal superior sensitivity and specificity of the new shape priors over those previously used to constrain DCMs. User trials with the new interactive algorithms show that the shape priors are directly responsible for improvements in accuracy and reductions in user demand.

  9. Partial lesions of the intratemporal segment of the facial nerve: graft versus partial reconstruction.

    Science.gov (United States)

    Bento, Ricardo F; Salomone, Raquel; Brito, Rubens; Tsuji, Robinson K; Hausen, Mariana

    2008-09-01

    In cases of partial lesions of the intratemporal segment of the facial nerve, should the surgeon perform an intraoperative partial reconstruction, or partially remove the injured segment and place a graft? We present results from partial lesion reconstruction on the intratemporal segment of the facial nerve. A retrospective study on 42 patients who presented partial lesions on the intratemporal segment of the facial nerve was performed between 1988 and 2005. The patients were divided into 3 groups based on the procedure used: interposition of the partial graft on the injured area of the nerve (group 1; 12 patients); keeping the preserved part and performing tubulization (group 2; 8 patients); and dividing the parts of the injured nerve (proximal and distal) and placing a total graft of the sural nerve (group 3; 22 patients). Fracture of the temporal bone was the most frequent cause of the lesion in all groups, followed by iatrogenic causes (p lesion of the facial nerve is still questionable. Among these 42 patients, the best results were those from the total graft of the facial nerve.

  10. Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI

    Science.gov (United States)

    Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

    2017-03-01

    Intuitive segmentation-based CADx/radiomic features, calculated from the lesion segmentations of dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) have been utilized in the task of distinguishing between malignant and benign lesions. Additionally, transfer learning with pre-trained deep convolutional neural networks (CNNs) allows for an alternative method of radiomics extraction, where the features are derived directly from the image data. However, the comparison of computer-extracted segmentation-based and CNN features in MRI breast lesion characterization has not yet been conducted. In our study, we used a DCE-MRI database of 640 breast cases - 191 benign and 449 malignant. Thirty-eight segmentation-based features were extracted automatically using our quantitative radiomics workstation. Also, 2D ROIs were selected around each lesion on the DCE-MRIs and directly input into a pre-trained CNN AlexNet, yielding CNN features. Each method was investigated separately and in combination in terms of performance in the task of distinguishing between benign and malignant lesions. Area under the ROC curve (AUC) served as the figure of merit. Both methods yielded promising classification performance with round-robin cross-validated AUC values of 0.88 (se =0.01) and 0.76 (se=0.02) for segmentationbased and deep learning methods, respectively. Combination of the two methods enhanced the performance in malignancy assessment resulting in an AUC value of 0.91 (se=0.01), a statistically significant improvement over the performance of the CNN method alone.

  11. Automated Bayesian Segmentation of Microvascular White-Matter Lesions in the ACCORD-MIND Study

    International Nuclear Information System (INIS)

    Herskovits, E. H.; Bryan, R. N.; Yang, F.

    2008-01-01

    Purpose: Automatic brain-lesion segmentation has the potential to greatly expand the analysis of the relationships between brain function and lesion locations in large-scale epidemiologic studies, such as the ACCORD-MIND study. In this manuscript we describe the design and evaluation of a Bayesian lesion-segmentation method, with the expectation that our approach would segment white-matter brain lesions in MR images without user intervention. Materials and Methods: Each ACCORD-MIND subject has T1-weighted, T2-weighted, spin-density-weighted, and FLAIR sequences. The training portion of our algorithm first registers training images to a standard coordinate space; then, it collects statistics that capture signal-intensity information, and residual spatial variability of normal structures and lesions. The classification portion of our algorithm then uses these statistics to segment lesions in images from new subjects, without the need for user intervention. We evaluated this algorithm using 42 subjects with primarily white-matter lesions from the ACCORD-MIND project. Results: Our experiments demonstrated high classification accuracy, using an expert neuro radiologist as a standard. Conclusions: A Bayesian lesion-segmentation algorithm that collects multi-channel signal-intensity and spatial information from MR images of the brain shows potential for accurately segmenting brain lesions in images obtained from subjects not used in training. (authors)

  12. A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography.

    Science.gov (United States)

    Flores, Wilfrido Gómez; Pereira, Wagner Coelho de Albuquerque

    2017-01-01

    This paper presents an adaptive contrast enhancement method based on sigmoidal mapping function (SACE) used for improving the computerized segmentation of breast lesions on ultrasound. First, from the original ultrasound image an intensity variation map is obtained, which is used to generate local sigmoidal mapping functions related to distinct contextual regions. Then, a bilinear interpolation scheme is used to transform every original pixel to a new gray level value. Also, four contrast enhancement techniques widely used in breast ultrasound enhancement are implemented: histogram equalization (HEQ), contrast limited adaptive histogram equalization (CLAHE), fuzzy enhancement (FEN), and sigmoid based enhancement (SEN). In addition, these contrast enhancement techniques are considered in a computerized lesion segmentation scheme based on watershed transformation. The performance comparison among techniques is assessed in terms of both the quality of contrast enhancement and the segmentation accuracy. The former is quantified by the measure, where the greater the value, the better the contrast enhancement, whereas the latter is calculated by the Jaccard index, which should tend towards unity to indicate adequate segmentation. The experiments consider a data set with 500 breast ultrasound images. The results show that SACE outperforms its counterparts, where the median values for the measure are: SACE: 139.4, SEN: 68.2, HEQ: 64.1, CLAHE: 62.8, and FEN: 7.9. Considering the segmentation performance results, the SACE method presents the largest accuracy, where the median values for the Jaccard index are: SACE: 0.81, FEN: 0.80, CLAHE: 0.79, HEQ: 77, and SEN: 0.63. The SACE method performs well due to the combination of three elements: (1) the intensity variation map reduces intensity variations that could distort the real response of the mapping function, (2) the sigmoidal mapping function enhances the gray level range where the transition between lesion and background

  13. An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.

    Science.gov (United States)

    Liu, Yan; Cheng, H D; Huang, Jianhua; Zhang, Yingtao; Tang, Xianglong

    2012-10-01

    In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.

  14. Computer input devices: neutral party or source of significant error in manual lesion segmentation?

    Science.gov (United States)

    Chen, James Y; Seagull, F Jacob; Nagy, Paul; Lakhani, Paras; Melhem, Elias R; Siegel, Eliot L; Safdar, Nabile M

    2011-02-01

    Lesion segmentation involves outlining the contour of an abnormality on an image to distinguish boundaries between normal and abnormal tissue and is essential to track malignant and benign disease in medical imaging for clinical, research, and treatment purposes. A laser optical mouse and a graphics tablet were used by radiologists to segment 12 simulated reference lesions per subject in two groups (one group comprised three lesion morphologies in two sizes, one for each input device for each device two sets of six, composed of three morphologies in two sizes each). Time for segmentation was recorded. Subjects completed an opinion survey following segmentation. Error in contour segmentation was calculated using root mean square error. Error in area of segmentation was calculated compared to the reference lesion. 11 radiologists segmented a total of 132 simulated lesions. Overall error in contour segmentation was less with the graphics tablet than with the mouse (P Error in area of segmentation was not significantly different between the tablet and the mouse (P = 0.62). Time for segmentation was less with the tablet than the mouse (P = 0.011). All subjects preferred the graphics tablet for future segmentation (P = 0.011) and felt subjectively that the tablet was faster, easier, and more accurate (P = 0.0005). For purposes in which accuracy in contour of lesion segmentation is of the greater importance, the graphics tablet is superior to the mouse in accuracy with a small speed benefit. For purposes in which accuracy of area of lesion segmentation is of greater importance, the graphics tablet and mouse are equally accurate.

  15. An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

    Science.gov (United States)

    Nasir, Muhammad; Attique Khan, Muhammad; Sharif, Muhammad; Lali, Ikram Ullah; Saba, Tanzila; Iqbal, Tassawar

    2018-02-21

    Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods. © 2018 Wiley Periodicals, Inc.

  16. Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

    Science.gov (United States)

    Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

    2017-07-01

    To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American

  17. Lesion segmentation from multimodal MRI using random forest following ischemic stroke.

    Science.gov (United States)

    Mitra, Jhimli; Bourgeat, Pierrick; Fripp, Jurgen; Ghose, Soumya; Rose, Stephen; Salvado, Olivier; Connelly, Alan; Campbell, Bruce; Palmer, Susan; Sharma, Gagan; Christensen, Soren; Carey, Leeanne

    2014-09-01

    Understanding structure-function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain-behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be used to aid the development of post-stroke management strategies. Our approach uses Bayesian-Markov Random Field (MRF) classification to segment probable lesion volumes present on fluid attenuated inversion recovery (FLAIR) MRI. Thereafter, a random forest classification of the information from multimodal (T1-weighted, T2-weighted, FLAIR, and apparent diffusion coefficient (ADC)) MRI images and other context-aware features (within the probable lesion areas) was used to extract areas with high likelihood of being classified as lesions. The final segmentation of the lesion was obtained by thresholding the random forest probabilistic maps. The accuracy of the automated lesion delineation method was assessed in a total of 36 patients (24 male, 12 female, mean age: 64.57±14.23yrs) at 3months after stroke onset and compared with manually segmented lesion volumes by an expert. Accuracy assessment of the automated lesion identification method was performed using the commonly used evaluation metrics. The mean sensitivity of segmentation was measured to be 0.53±0.13 with a mean positive predictive value of 0.75±0.18. The mean lesion volume difference was observed to be 32.32%±21.643% with a high Pearson's correlation of r=0.76 (p<0.0001). The lesion overlap accuracy was measured in terms of Dice similarity coefficient with a mean of 0.60±0.12, while the contour

  18. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

    Science.gov (United States)

    Valverde, Sergi; Cabezas, Mariano; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Oliver, Arnau; Lladó, Xavier

    2017-07-15

    In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Multi-output decision trees for lesion segmentation in multiple sclerosis

    Science.gov (United States)

    Jog, Amod; Carass, Aaron; Pham, Dzung L.; Prince, Jerry L.

    2015-03-01

    Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

  20. ROLE OF HIGH RESOLUTION ULTRASONOGRAPHY IN THE EVALUATION OF POSTERIOR SEGMENT LESIONS OF THE EYE

    Directory of Open Access Journals (Sweden)

    Rashmi Nagaraju

    2015-01-01

    Full Text Available BACKGRO UND: The superficial location of the eye, its cystic composition, and the advent of high - frequency ultrasound make sonography ideal for imaging the eye. Ultrasonography is a simple, readily available, non - invasive, non - ionizing, highly accurate, real time and cost effective modality . OBJECTIVES: 1 To evaluate the accuracy of high resolution B - mode ultrasonography in the diagnosis of posterior segment lesions of eye as compared to ophthalmoscopic examination particularly in cases of opaque conducting media . 2 To evaluate sonographic appearances of various posterior segment lesions of the eye . MATERIALS AND METHODS: 1 A prospective study was carried out on 62 cases with suspected posterior segment lesions of eye. All patients clinically suspected to have p osterior segment lesions in the presence of opaque conducting media were included in the study. Cases suspected to have isolated anterior segmental and extra ocular lesions were excluded. 2 HRUS was performed with Philips IU22 using high frequency probe ( 5 to 17 MHz utilizing contact method. 3 Sonological diagnosis was made based on sonographic features such as location, morphology, echo pattern, color Doppler characteristics, kinetics of the lesion with eye movements and acoustic characteristics of the lesion. 4 Subsequent clinical, lab investigations, surgical and histopathological examinations were carried out as applicable and final diagnosis was made which was correlated with the sonological diagnosis. Sonological diagnosis was also compared with op hthalmoscopic diagnosis. STATISTICAL ANALYSES: The validities and diagnostic accuracies of high resolution ultrasound and ophthalmoscopic examinations were calculated and compared. RESULTS AND CONCLUSIONS: 1 Ultrasound was the initial imaging modality opt ed in most of the cases as it was readily available, simple and cost effective modality. It establishes the diagnosis in significant number of cases superseding the accuracy

  1. Segmenting Multiple Sclerosis Lesions using a Spatially Constrained K-Nearest Neighbour approach

    DEFF Research Database (Denmark)

    Lyksborg, Mark; Larsen, Rasmus; Sørensen, Per Soelberg

    2012-01-01

    We propose a method for the segmentation of Multiple Sclerosis lesions. The method is based on probability maps derived from a K-Nearest Neighbours classication. These are used as a non parametric likelihood in a Bayesian formulation with a prior that assumes connectivity of neighbouring voxels. ...

  2. DETECTION OF CANCEROUS LESION BY UTERINE CERVIX IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    P. Priya

    2014-02-01

    Full Text Available This paper works at segmentation of lesion observed in cervical cancer, which is the second most common cancer among women worldwide. The purpose of segmentation is to determine the location for a biopsy to be taken for diagnosis. Cervix cancer is a disease in which cancer cells are found in the tissues of the cervix. The acetowhite region is a major indicator of abnormality in the cervix image. This project addresses the problem of segmenting uterine cervix image into different regions. We analyze two algorithms namely Watershed, K-means clustering algorithm, Expectation Maximization (EM Image Segmentation algorithm. These segmentations methods are carried over for the colposcopic uterine cervix image.

  3. Detection of white spot lesions by segmenting laser speckle images using computer vision methods.

    Science.gov (United States)

    Gavinho, Luciano G; Araujo, Sidnei A; Bussadori, Sandra K; Silva, João V P; Deana, Alessandro M

    2018-05-05

    This paper aims to develop a method for laser speckle image segmentation of tooth surfaces for diagnosis of early stages caries. The method, applied directly to a raw image obtained by digital photography, is based on the difference between the speckle pattern of a carious lesion tooth surface area and that of a sound area. Each image is divided into blocks which are identified in a working matrix by their χ 2 distance between block histograms of the analyzed image and the reference histograms previously obtained by K-means from healthy (h_Sound) and lesioned (h_Decay) areas, separately. If the χ 2 distance between a block histogram and h_Sound is greater than the distance to h_Decay, this block is marked as decayed. The experiments showed that the method can provide effective segmentation for initial lesions. We used 64 images to test the algorithm and we achieved 100% accuracy in segmentation. Differences between the speckle pattern of a sound tooth surface region and a carious region, even in the early stage, can be evidenced by the χ 2 distance between histograms. This method proves to be more effective for segmenting the laser speckle image, which enhances the contrast between sound and lesioned tissues. The results were obtained with low computational cost. The method has the potential for early diagnosis in a clinical environment, through the development of low-cost portable equipment.

  4. A supervised framework for lesion segmentation and automated VLSM analyses in left hemispheric stroke

    Directory of Open Access Journals (Sweden)

    Dorian Pustina

    2015-05-01

    Full Text Available INTRODUCTION: Voxel-based lesion-symptom mapping (VLSM is conventionally performed using skill and knowledge of experts to manually delineate brain lesions. This process requires time, and is likely to have substantial inter-rater variability. Here, we propose a supervised machine learning framework for lesion segmentation capable of learning from a single modality and existing manual segmentations in order to delineate lesions in new patients. METHODS: Data from 60 patients with chronic stroke aphasia were utilized in the study (age: 59.7±11.5yrs, post-stroke interval: 5±2.9yrs, male/female ratio: 34/26. Using a single T1 image of each subject, additional features were created that provided complementary information, such as, difference from template, tissue segmentation, brain asymmetries, gradient magnitude, and deviances of these images from 80 age and gender matched controls. These features were fed into MRV-NRF (multi-resolution voxel-wise neighborhood random forest; Tustison et al., 2014 prediction algorithm implemented in ANTsR (Avants, 2015. The algorithm incorporates information from each voxel and its surrounding neighbors from all above features, in a hierarchy of random forest predictions from low to high resolution. The validity of the framework was tested with a 6-fold cross validation (i.e., train from 50 subjects, predict 10. The process was repeated ten times, producing ten segmentations for each subject, from which the average solution was binarized. Predicted lesions were compared to manually defined lesions, and VLSM models were built on 4 language measures: repetition and comprehension subscores from the WAB (Kertesz, 1982, WAB-AQ, and PNT naming accuracy (Roach, Schwartz, Martin, Grewal, & Brecher, 1996. RESULTS: Manual and predicted lesion size showed high correlation (r=0.96. Compared to manual lesions, the predicted lesions had a dice overlap of 0.72 (±0.14 STD, a case-wise maximum distance (Hausdorff of 21mm (±16

  5. FLAIR lesion segmentation: Application in patients with brain tumors and acute ischemic stroke

    International Nuclear Information System (INIS)

    Artzi, Moran; Aizenstein, Orna; Jonas-Kimchi, Tali; Myers, Vicki; Hallevi, Hen; Ben Bashat, Dafna

    2013-01-01

    Background: Lesion size in fluid attenuation inversion recovery (FLAIR) images is an important clinical parameter for patient assessment and follow-up. Although manual delineation of lesion areas considered as ground truth, it is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this study, an automatic methodology for FLAIR lesion segmentation is proposed, and its application in patients with brain tumors undergoing therapy; and in patients following stroke is demonstrated. Materials and methods: FLAIR lesion segmentation was performed in 57 magnetic resonance imaging (MRI) data sets obtained from 44 patients: 28 patients with primary brain tumors; 5 patients with recurrent-progressive glioblastoma (rGB) who were scanned longitudinally during anti-angiogenic therapy (18 MRI scans); and 11 patients following ischemic stroke. Results: FLAIR lesion segmentation was obtained in all patients. When compared to manual delineation, a high visual similarity was observed, with an absolute relative volume difference of 16.80% and 20.96% and a volumetric overlap error of 24.87% and 27.50% obtained for two raters: accepted values for automatic methods. Quantitative measurements of the segmented lesion volumes were in line with qualitative radiological assessment in four patients who received anti-anogiogenic drugs. In stroke patients the proposed methodology enabled identification of the ischemic lesion and differentiation from other FLAIR hyperintense areas, such as pre-existing disease. Conclusion: This study proposed a replicable methodology for FLAIR lesion detection and quantification and for discrimination between lesion of interest and pre-existing disease. Results from this study show the wide clinical applications of this methodology in research and clinical practice

  6. FLAIR lesion segmentation: Application in patients with brain tumors and acute ischemic stroke

    Energy Technology Data Exchange (ETDEWEB)

    Artzi, Moran, E-mail: artzimy@gmail.com [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv (Israel); Aizenstein, Orna, E-mail: ornaaize@gmail.com [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Jonas-Kimchi, Tali, E-mail: talijk@tlvmc.gov.il [Radiology Department, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Myers, Vicki, E-mail: vicki_myers@hotmail.com [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Hallevi, Hen, E-mail: hen.hallevi@gmail.com [Neurology Department, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Ben Bashat, Dafna, E-mail: dafnab@tlvmc.gov.il [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv (Israel)

    2013-09-15

    Background: Lesion size in fluid attenuation inversion recovery (FLAIR) images is an important clinical parameter for patient assessment and follow-up. Although manual delineation of lesion areas considered as ground truth, it is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this study, an automatic methodology for FLAIR lesion segmentation is proposed, and its application in patients with brain tumors undergoing therapy; and in patients following stroke is demonstrated. Materials and methods: FLAIR lesion segmentation was performed in 57 magnetic resonance imaging (MRI) data sets obtained from 44 patients: 28 patients with primary brain tumors; 5 patients with recurrent-progressive glioblastoma (rGB) who were scanned longitudinally during anti-angiogenic therapy (18 MRI scans); and 11 patients following ischemic stroke. Results: FLAIR lesion segmentation was obtained in all patients. When compared to manual delineation, a high visual similarity was observed, with an absolute relative volume difference of 16.80% and 20.96% and a volumetric overlap error of 24.87% and 27.50% obtained for two raters: accepted values for automatic methods. Quantitative measurements of the segmented lesion volumes were in line with qualitative radiological assessment in four patients who received anti-anogiogenic drugs. In stroke patients the proposed methodology enabled identification of the ischemic lesion and differentiation from other FLAIR hyperintense areas, such as pre-existing disease. Conclusion: This study proposed a replicable methodology for FLAIR lesion detection and quantification and for discrimination between lesion of interest and pre-existing disease. Results from this study show the wide clinical applications of this methodology in research and clinical practice.

  7. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

    Science.gov (United States)

    Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

    2016-05-01

    We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

  8. Efficacy of CTA portography for preoperatively determining the segmental location of focal hepatic lesions

    International Nuclear Information System (INIS)

    Nelson, R.C.; Chemzmar, J.L.; Sugarbaker, P.H.; Bernardino, M.E.

    1989-01-01

    Using cross-sectional images to localize tumors to specific hepatic segments before resection might be difficult because the transverse scissura, an important segmental landmark, is approximately parallel to the axial plane. Since this plane is parallel to the main right and left portal pedicles, the authors postulated that they could localize lesions above this plane to segments 2,4A,7, or 8, and below this plane to segments 3, 4B,5, or 6. The authors have retrospectively evaluated, in a blinded fashion, 24 hepatic masses in 13 patients who underwent CTA-portography and subsequent tumor resection. Using this scheme, they have accurately predicted the primary segmental location in 22 (92%) of 24 surgically confirmed lesions

  9. Comparison of Long-Term Clinical Outcomes of Lesions Exhibiting Focal and Segmental Peri-Stent Contrast Staining.

    Science.gov (United States)

    Tokuda, Takahiro; Yamawaki, Masahiro; Takahara, Mitsuyohi; Mori, Shinsuke; Makino, Kenji; Honda, Yosuke; Takafuji, Hiroya; Takama, Takuro; Tsutsumi, Masakazu; Sakamoto, Yasunari; Takimura, Hideyuki; Kobayashi, Norihiro; Araki, Motoharu; Hirano, Keisuke; Ito, Yoshiaki

    2016-03-18

    Peri-stent contrast staining (PSS) after metallic drug-eluting stent deployment is associated with target lesion revascularization and very late stent thrombosis. However, the type of PSS that influences the clinical outcomes is unknown. Therefore, we aimed to reveal which PSS type was influencing clinical outcomes. This study included 5580 de novo lesions of 4405 patients who were implanted with a first- or second-generation drug-eluting stent and who were evaluated using follow-up angiography within 12 months after stent implantation. We compared the clinical outcomes of patients divided into focal PSS and segmental PSS groups for 6 years after stent implantation. Total PSS was observed in 97 lesions (2.2%), of which 42 and 55 lesions were focal and segmental PSS, respectively. Baseline characteristics were similar between groups, except for intraoperative chronic total occlusion (segmental PSS=47.3% versus focal PSS=11.9%, P=0.0001). The incidence of segmental PSS tended to be higher in patients with a first-generation drug-eluting stent (83.6% versus 16.4%, P=0.05). The cumulative incidence of stent thrombosis in the 6 years of segmental PSS group was significantly higher than that of the focal PSS group (13.9% versus 0%, P=0.04). The cumulative incidence of overall target lesion revascularization for restenosis, excluding target lesion revascularization procedures for stent thrombosis, was significantly higher in the segmental PSS group (38.0% versus 0%, P=0.01). The incidence of segmental PSS tended to be higher in patients with a first-generation drug-eluting stent and appeared to be significantly associated with target lesion revascularization and stent thrombosis. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  10. Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard

    International Nuclear Information System (INIS)

    Jha, Abhinav K; Kupinski, Matthew A; Rodríguez, Jeffrey J; Stephen, Renu M; Stopeck, Alison T

    2012-01-01

    In many studies, the estimation of the apparent diffusion coefficient (ADC) of lesions in visceral organs in diffusion-weighted (DW) magnetic resonance images requires an accurate lesion-segmentation algorithm. To evaluate these lesion-segmentation algorithms, region-overlap measures are used currently. However, the end task from the DW images is accurate ADC estimation, and the region-overlap measures do not evaluate the segmentation algorithms on this task. Moreover, these measures rely on the existence of gold-standard segmentation of the lesion, which is typically unavailable. In this paper, we study the problem of task-based evaluation of segmentation algorithms in DW imaging in the absence of a gold standard. We first show that using manual segmentations instead of gold-standard segmentations for this task-based evaluation is unreliable. We then propose a method to compare the segmentation algorithms that does not require gold-standard or manual segmentation results. The no-gold-standard method estimates the bias and the variance of the error between the true ADC values and the ADC values estimated using the automated segmentation algorithm. The method can be used to rank the segmentation algorithms on the basis of both the ensemble mean square error and precision. We also propose consistency checks for this evaluation technique. (paper)

  11. Unsupervised segmentation of medical image based on difference of mutual information

    Institute of Scientific and Technical Information of China (English)

    L(U) Qingwen; CHEN Wufan

    2006-01-01

    In the scope of medical image processing, segmentation is important and difficult. There are still two problems which trouble us in this field. One is how to determine the number of clusters in an image and the other is how to segment medical images containing lesions. A new segmentation method called DDC, based on difference of mutual information (dMI) and pixon, is proposed in this paper. Experiments demonstrate that dMI shows one kind of intrinsic relationship between the segmented image and the original one and so it can be used to well determine the number of clusters. Furthermore, multi-modality medical images with lesions can be automatically and successfully segmented by DDC method.

  12. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

    Directory of Open Access Journals (Sweden)

    Liang Chen

    2017-01-01

    Full Text Available Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs: one is an ensemble of two DeconvNets (Noh et al., 2015, which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net, which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

  13. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

    Science.gov (United States)

    Chen, Liang; Bentley, Paul; Rueckert, Daniel

    2017-01-01

    Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

  14. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Science.gov (United States)

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  15. Clonal expansion of T-cell receptor beta gene segment in the retrocochlear lesions of EAE mice.

    Science.gov (United States)

    Cheng, K C; Lee, K M; Yoo, T J

    1998-01-01

    It has been reported that the T cell receptor V beta 8.2 (TcrbV8.2) gene segment is predominantly expressed in encephalomyelitic T cells responding to myelin basic protein (MBP) in experimental allergic encephalomyelitis (EAE) mice. We have demonstrated retrocochlear hearing loss in EAE mice in previous studies. Administration of a monoclonal antibody specific to the T cell receptor V beta 8 (TcrbV8) subfamily prevented both this type of hearing loss and the central nerve disease. In this study, we examined the role of the TcrbV8.2 gene segment in the retrocochlear lesions of EAE mice. A clonal expression of T cell receptor beta chain gene segment (TcrbV8.2-TcrbD2-TcrbJ2.7) was identified in the retrocochlear lesions. The TcrbV8.2 gene segment appears to recombine only with TcrbJ2.1 (32.1%) and TcrbJ2.7 (67.9%) gene segments. The TcrbJ2.7 gene segment has also been previously identified as the dominant TcrbJ gene in the lymph nodes of EAE mice. Only TcrbD2, with a length of 4 amino acids, was observed recombining with these TcrbV8.2 sequences. G and C nucleotides are predominantly expressed at the N regions between the V-D and D-J junctions. This dominant TcrbV gene segment (TcrbV8.2-TcrbD2-TcrbJ2.7) observed in the retrocochlear lesions has been identified in the MBP-specific T cells from the lymph nodes of EAE mice. These results suggest that a small subset of antigen-specific T cells migrate to, and expand at, the retrocochlear lesions, which leads to hearing loss.

  16. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Jean Marie Vianney Kinani

    2017-01-01

    Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

  17. Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

    Science.gov (United States)

    Kage, Andreas; Legal, Wolfgang; Kelm, Peter; Simon, Jörg; Bergen, Tobias; Münzenmayer, Christian; Benz, Michaela

    2012-03-01

    Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a challenging task as some kinds of abnormal tissues do not differ much in their appearance from their surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate. A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image contains besides the glowing of the suspicious lesions no more further visual information like for example the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment has to be done using white light illumination. Thereby, the spatial information of the lesion provided by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming procedure due to many switches between the modalities and increases the risk of mistreatment. We introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence image automatically once the image was classified as a fluorescence image. The area of the contour of the detected lesion is transferred to the corresponding white light image and provide the clinical expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert gets the spatial and the visual information of the lesion together in one image. This can save time and decrease the risk of an incomplete removal of a malign lesion.

  18. Parietal Epithelial Cells Participate in the Formation of Sclerotic Lesions in Focal Segmental Glomerulosclerosis

    Science.gov (United States)

    Smeets, Bart; Kuppe, Christoph; Sicking, Eva-Maria; Fuss, Astrid; Jirak, Peggy; van Kuppevelt, Toin H.; Endlich, Karlhans; Wetzels, Jack F.M.; Gröne, Hermann-Josef; Floege, Jürgen

    2011-01-01

    The pathogenesis of the development of sclerotic lesions in focal segmental glomerulosclerosis (FSGS) remains unknown. Here, we selectively tagged podocytes or parietal epithelial cells (PECs) to determine whether PECs contribute to sclerosis. In three distinct models of FSGS (5/6-nephrectomy + DOCA-salt; the murine transgenic chronic Thy1.1 model; or the MWF rat) and in human biopsies, the primary injury to induce FSGS associated with focal activation of PECs and the formation of cellular adhesions to the capillary tuft. From this entry site, activated PECs invaded the affected segment of the glomerular tuft and deposited extracellular matrix. Within the affected segment, podocytes were lost and mesangial sclerosis developed within the endocapillary compartment. In conclusion, these results demonstrate that PECs contribute to the development and progression of the sclerotic lesions that define FSGS, but this pathogenesis may be relevant to all etiologies of glomerulosclerosis. PMID:21719782

  19. A fully automatic, threshold-based segmentation method for the estimation of the Metabolic Tumor Volume from PET images: validation on 3D printed anthropomorphic oncological lesions

    Science.gov (United States)

    Gallivanone, F.; Interlenghi, M.; Canervari, C.; Castiglioni, I.

    2016-01-01

    18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) is a standard functional diagnostic technique to in vivo image cancer. Different quantitative paramters can be extracted from PET images and used as in vivo cancer biomarkers. Between PET biomarkers Metabolic Tumor Volume (MTV) has gained an important role in particular considering the development of patient-personalized radiotherapy treatment for non-homogeneous dose delivery. Different imaging processing methods have been developed to define MTV. The different proposed PET segmentation strategies were validated in ideal condition (e.g. in spherical objects with uniform radioactivity concentration), while the majority of cancer lesions doesn't fulfill these requirements. In this context, this work has a twofold objective: 1) to implement and optimize a fully automatic, threshold-based segmentation method for the estimation of MTV, feasible in clinical practice 2) to develop a strategy to obtain anthropomorphic phantoms, including non-spherical and non-uniform objects, miming realistic oncological patient conditions. The developed PET segmentation algorithm combines an automatic threshold-based algorithm for the definition of MTV and a k-means clustering algorithm for the estimation of the background. The method is based on parameters always available in clinical studies and was calibrated using NEMA IQ Phantom. Validation of the method was performed both in ideal (e.g. in spherical objects with uniform radioactivity concentration) and non-ideal (e.g. in non-spherical objects with a non-uniform radioactivity concentration) conditions. The strategy to obtain a phantom with synthetic realistic lesions (e.g. with irregular shape and a non-homogeneous uptake) consisted into the combined use of standard anthropomorphic phantoms commercially and irregular molds generated using 3D printer technology and filled with a radioactive chromatic alginate. The proposed segmentation algorithm was feasible in a

  20. Segmentation of skin lesions in chronic graft versus host disease photographs with fully convolutional networks

    Science.gov (United States)

    Wang, Jianing; Chen, Fuyao; Dellalana, Laura E.; Jagasia, Madan H.; Tkaczyk, Eric R.; Dawant, Benoit M.

    2018-02-01

    Chronic graft-versus-host disease (cGVHD) is a frequent and potentially life-threatening complication of allogeneic hematopoietic stem cell transplantation (HCT) and commonly affects the skin, resulting in distressing patient morbidity. The percentage of involved body surface area (BSA) is commonly used for diagnosing and scoring the severity of cGVHD. However, the segmentation of the involved BSA from patient whole body serial photography is challenging because (1) it is difficult to design traditional segmentation method that rely on hand crafted features as the appearance of cGVHD lesions can be drastically different from patient to patient; (2) to the best of our knowledge, currently there is no publicavailable labelled image set of cGVHD skin for training deep networks to segment the involved BSA. In this preliminary study we create a small labelled image set of skin cGVHD, and we explore the possibility to use a fully convolutional neural network (FCN) to segment the skin lesion in the images. We use a commercial stereoscopic Vectra H1 camera (Canfield Scientific) to acquire 400 3D photographs of 17 cGVHD patients aged between 22 and 72. A rotational data augmentation process is then applied, which rotates the 3D photos through 10 predefined angles, producing one 2D projection image at each position. This results in 4000 2D images that constitute our cGVHD image set. A FCN model is trained and tested using our images. We show that our method achieves encouraging results for segmenting cGVHD skin lesion in photographic images.

  1. Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

    DEFF Research Database (Denmark)

    de Sitter, Alexandra; Steenwijk, Martijn D; Ruet, Aurélie

    2017-01-01

    (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation......BACKGROUND AND PURPOSE: In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations......-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on 'unseen' center. RESULTS: Compared to the reference mean lesion volume (4.85 ± 7.29 mL), the methods displayed a mean difference of 1.60 ± 4.83 (Cascade), 2.31 ± 7.66 (LGA), 0.44 ± 4.68 (LPA), 1...

  2. Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation

    International Nuclear Information System (INIS)

    Street, Ethan; Hadjiiski, Lubomir; Sahiner, Berkman; Gujar, Sachin; Ibrahim, Mohannad; Mukherji, Suresh K.; Chan, Heang-Ping

    2007-01-01

    The authors have developed a semiautomatic system for segmentation of a diverse set of lesions in head and neck CT scans. The system takes as input an approximate bounding box, and uses a multistage level set to perform the final segmentation. A data set consisting of 69 lesions marked on 33 scans from 23 patients was used to evaluate the performance of the system. The contours from automatic segmentation were compared to both 2D and 3D gold standard contours manually drawn by three experienced radiologists. Three performance metric measures were used for the comparison. In addition, a radiologist provided quality ratings on a 1 to 10 scale for all of the automatic segmentations. For this pilot study, the authors observed that the differences between the automatic and gold standard contours were larger than the interobserver differences. However, the system performed comparably to the radiologists, achieving an average area intersection ratio of 85.4% compared to an average of 91.2% between two radiologists. The average absolute area error was 21.1% compared to 10.8%, and the average 2D distance was 1.38 mm compared to 0.84 mm between the radiologists. In addition, the quality rating data showed that, despite the very lax assumptions made on the lesion characteristics in designing the system, the automatic contours approximated many of the lesions very well

  3. Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

    Science.gov (United States)

    Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

    2018-04-01

    Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

  4. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

    Directory of Open Access Journals (Sweden)

    R. Guerrero

    2018-01-01

    Full Text Available White matter hyperintensities (WMH are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist. The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN

  5. [Segmentation of whole body bone SPECT image based on BP neural network].

    Science.gov (United States)

    Zhu, Chunmei; Tian, Lianfang; Chen, Ping; He, Yuanlie; Wang, Lifei; Ye, Guangchun; Mao, Zongyuan

    2007-10-01

    In this paper, BP neural network is used to segment whole body bone SPECT image so that the lesion area can be recognized automatically. For the uncertain characteristics of SPECT images, it is hard to achieve good segmentation result if only the BP neural network is employed. Therefore, the segmentation process is divided into three steps: first, the optimal gray threshold segmentation method is employed for preprocessing, then BP neural network is used to roughly identify the lesions, and finally template match method and symmetry-removing program are adopted to delete the wrongly recognized areas.

  6. Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity

    Science.gov (United States)

    He, Nana; Zhang, Xiaolong; Zhao, Juanjuan; Zhao, Huilan; Qiang, Yan

    2017-07-01

    While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.

  7. Pigmented skin lesion detection using random forest and wavelet-based texture

    Science.gov (United States)

    Hu, Ping; Yang, Tie-jun

    2016-10-01

    The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

  8. The method for detecting small lesions in medical image based on sliding window

    Science.gov (United States)

    Han, Guilai; Jiao, Yuan

    2016-10-01

    At present, the research on computer-aided diagnosis includes the sample image segmentation, extracting visual features, generating the classification model by learning, and according to the model generated to classify and judge the inspected images. However, this method has a large scale of calculation and speed is slow. And because medical images are usually low contrast, when the traditional image segmentation method is applied to the medical image, there is a complete failure. As soon as possible to find the region of interest, improve detection speed, this topic attempts to introduce the current popular visual attention model into small lesions detection. However, Itti model is mainly for natural images. But the effect is not ideal when it is used to medical images which usually are gray images. Especially in the early stages of some cancers, the focus of a disease in the whole image is not the most significant region and sometimes is very difficult to be found. But these lesions are prominent in the local areas. This paper proposes a visual attention mechanism based on sliding window, and use sliding window to calculate the significance of a local area. Combined with the characteristics of the lesion, select the features of gray, entropy, corner and edge to generate a saliency map. Then the significant region is segmented and distinguished. This method reduces the difficulty of image segmentation, and improves the detection accuracy of small lesions, and it has great significance to early discovery, early diagnosis and treatment of cancers.

  9. Accuracy of liver lesion assessment using automated measurement and segmentation software in biphasic multislice CT (MSCT)

    International Nuclear Information System (INIS)

    Puesken, M.; Juergens, K.U.; Edenfeld, A.; Buerke, B.; Seifarth, H.; Beyer, F.; Heindel, W.; Wessling, J.; Suehling, M.; Osada, N.

    2009-01-01

    Purpose: To assess the accuracy of liver lesion measurement using automated measurement and segmentation software depending on the vascularization level. Materials and Methods: Arterial and portal venous phase multislice CT (MSCT) was performed for 58 patients. 94 liver lesions were evaluated and classified according to vascularity (hypervascular: 13 hepatocellular carcinomas, 20 hemangiomas; hypovascular: 31 metastases, 3 lymphomas, 4 abscesses; liquid: 23 cysts). The RECIST diameter and volume were obtained using automated measurement and segmentation software and compared to corresponding measurements derived visually by two experienced radiologists as a reference standard. Statistical analysis was performed using the Wilcoxon test and concordance correlation coefficients. Results: Automated measurements revealed no significant difference between the arterial and portal venous phase in hypovascular (mean RECIST diameter: 31.4 vs. 30.2 mm; p = 0.65; κ = 0.875) and liquid lesions (20.4 vs. 20.1 mm; p = 0.1; κ = 0.996). The RECIST diameter and volume of hypervascular lesions were significantly underestimated in the portal venous phase as compared to the arterial phase (30.3 vs. 26.9 mm, p = 0.007, κ 0.834; 10.7 vs. 7.9 ml, p = 0.0045, κ = 0.752). Automated measurements for hypovascular and liquid lesions in the arterial and portal venous phase were concordant to the reference standard. Hypervascular lesion measurements were in line with the reference standard for the arterial phase (30.3 vs. 32.2 mm, p 0.66, κ = 0.754), but revealed a significant difference for the portal venous phase (26.9 vs. 32.1 mm; p = 0.041; κ = 0.606). (orig.)

  10. Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography.

    Science.gov (United States)

    Jha, Abhinav K; Mena, Esther; Caffo, Brian; Ashrafinia, Saeed; Rahmim, Arman; Frey, Eric; Subramaniam, Rathan M

    2017-01-01

    Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.

  11. Functional plasticity in the respiratory drive to thoracic motoneurons in the segment above a chronic lateral spinal cord lesion

    DEFF Research Database (Denmark)

    Ford, T W; Anissimova, Natalia P; Meehan, Claire Francesca

    2016-01-01

    A previous neurophysiological investigation demonstrated an increase in functional projections of expiratory bulbospinal neurons (EBSNs) in the segment above a chronic lateral thoracic spinal cord lesion that severed their axons. We have now investigated how this plasticity might be manifested...... in thoracic motoneurons by measuring their respiratory drive and the connections to them from individual EBSNs. In anesthetized cats, simultaneous recordings were made intracellularly from motoneurons in the segment above a left-side chronic (16 wk) lesion of the spinal cord in the rostral part of T8, T9......, or T10 and extracellularly from EBSNs in the right caudal medulla, antidromically excited from just above the lesion but not from below. Spike-triggered averaging was used to measure the connections between pairs of EBSNs and motoneurons. Connections were found to have a very similar distribution...

  12. Neutrophil to lymphocyte ratio is associated with proximal/middle segment of the LAD lesions in patients with ST segment elevation infarction

    Directory of Open Access Journals (Sweden)

    Ozlem Arican Ozluk

    2017-01-01

    Full Text Available Introduction: Neutrophil to lymphocyte ratio (NLR was revaled to have a close relation with atherosclerotic cardiovascular disease. The relationship between NLR and culprit plaque localization has never been studied. Aim of the study : To evaluate the association between NLR and unstable plaque localization of left anterior descending artery (LAD in anterior miyocardial infarction patients. Material and methods : Patients admitted to our hospital with acute anterior STEMI were included. Fifhy-eight patients who have single-vessel disease at LAD and their hematological parameters were analyzed retrospectively. Proximal segment of LAD lesions were groupped as Group I and mid segment of the LAD lesion groupped as Group II. The groups were compared according to their NLR and other parameters. Results : Between group I (n = 41, mean age 52.5 ±12.7 and group II (n = 17, mean ages 52.0 ±10.8; NLR, were significantly higher in group I compared to the group II (6.9 ±5.6 vs. 3.3 ±2.0, p = 0.01. In group I, left ventricular ejection fraction (LVEF was significantly lower (p = 0.02. In correlation analyzes, NLR was positively correlated with CK MB (r = 0.32, p = 0.01 and negatively correlated with LVEF (r = –0.28, p = 0.03. Conclusions : The present study demonstrated that anterior myocardial infarction patients with high NLR had a greater possibility having proximal culprit lesion on the LAD. Therefore NLR can be used as a useful tool to culprit plaque localization in patients with acute miyocardial infarction patients.

  13. A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography

    International Nuclear Information System (INIS)

    Timp, Sheila; Karssemeijer, Nico

    2004-01-01

    Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area A z under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in A z values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant

  14. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  15. Measurement of thermally ablated lesions in sonoelastographic images using level set methods

    Science.gov (United States)

    Castaneda, Benjamin; Tamez-Pena, Jose Gerardo; Zhang, Man; Hoyt, Kenneth; Bylund, Kevin; Christensen, Jared; Saad, Wael; Strang, John; Rubens, Deborah J.; Parker, Kevin J.

    2008-03-01

    The capability of sonoelastography to detect lesions based on elasticity contrast can be applied to monitor the creation of thermally ablated lesion. Currently, segmentation of lesions depicted in sonoelastographic images is performed manually which can be a time consuming process and prone to significant intra- and inter-observer variability. This work presents a semi-automated segmentation algorithm for sonoelastographic data. The user starts by planting a seed in the perceived center of the lesion. Fast marching methods use this information to create an initial estimate of the lesion. Subsequently, level set methods refine its final shape by attaching the segmented contour to edges in the image while maintaining smoothness. The algorithm is applied to in vivo sonoelastographic images from twenty five thermal ablated lesions created in porcine livers. The estimated area is compared to results from manual segmentation and gross pathology images. Results show that the algorithm outperforms manual segmentation in accuracy, inter- and intra-observer variability. The processing time per image is significantly reduced.

  16. Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET

    International Nuclear Information System (INIS)

    Bousse, Alexandre; Thomas, Benjamin A; Erlandsson, Kjell; Hutton, Brian F; Pedemonte, Stefano; Ourselin, Sébastien; Arridge, Simon

    2012-01-01

    In this paper we propose a segmented magnetic resonance imaging (MRI) prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images. The model assumes the existence of activity classes that behave like a hidden Markov random field (MRF) driven by the segmented MRI. We utilize a mean field approximation to compute the likelihood of the MRF. We tested our method on both simulated and clinical data (brain PET) and compared our results with PET images corrected with the re-blurred Van Cittert (VC) algorithm, the simplified Guven (SG) algorithm and the region-based voxel-wise (RBV) technique. We demonstrated our algorithm outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent (e.g. mis-segmentation, lesions, etc) with the PET image. (paper)

  17. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    Science.gov (United States)

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  18. Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing.

    Science.gov (United States)

    Lindig, Tobias; Kotikalapudi, Raviteja; Schweikardt, Daniel; Martin, Pascal; Bender, Friedemann; Klose, Uwe; Ernemann, Ulrike; Focke, Niels K; Bender, Benjamin

    2018-04-15

    Voxel-based morphometry is still mainly based on T1-weighted MRI scans. Misclassification of vessels and dura mater as gray matter has been previously reported. Goal of the present work was to evaluate the effect of multimodal segmentation methods available in SPM12, and their influence on identification of age related atrophy and lesion detection in epilepsy patients. 3D T1-, T2- and FLAIR-images of 77 healthy adults (mean age 35.8 years, 19-66 years, 45 females), 7 patients with malformation of cortical development (MCD) (mean age 28.1 years,19-40 years, 3 females), and 5 patients with left hippocampal sclerosis (LHS) (mean age 49.0 years, 25-67 years, 3 females) from a 3T scanner were evaluated. Segmentation based on T1-only, T1+T2, T1+FLAIR, T2+FLAIR, and T1+T2+FLAIR were compared in the healthy subjects. Clinical VBM results based on the different segmentation approaches for MCD and for LHS were compared. T1-only segmentation overestimated total intracranial volume by about 80ml compared to the other segmentation methods. This was due to misclassification of dura mater and vessels as GM and CSF. Significant differences were found for several anatomical regions: the occipital lobe, the basal ganglia/thalamus, the pre- and postcentral gyrus, the cerebellum, and the brainstem. None of the segmentation methods yielded completely satisfying results for the basal ganglia/thalamus and the brainstem. The best correlation with age could be found for the multimodal T1+T2+FLAIR segmentation. Highest T-scores for identification of LHS were found for T1+T2 segmentation, while highest T-scores for MCD were dependent on lesion and anatomical location. Multimodal segmentation is superior to T1-only segmentation and reduces the misclassification of dura mater and vessels as GM and CSF. Depending on the anatomical region and the pathology of interest (atrophy, lesion detection, etc.), different combinations of T1, T2 and FLAIR yield optimal results. Copyright © 2017 Elsevier

  19. Automated detection of Lupus white matter lesions in MRI

    Directory of Open Access Journals (Sweden)

    Eloy Roura Perez

    2016-08-01

    Full Text Available Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML. In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM, gray matter (GM and cerebrospinal fluid (CSF, while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative and quantitative results in terms of precision and sensitivity of lesion detection (True Positive Rate (62% and Positive Prediction Value (80% respectively as well as segmentation accuracy (Dice Similarity Coefficient (72%. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.

  20. Inter-algorithm lesion volumetry comparison of real and 3D simulated lung lesions in CT

    Science.gov (United States)

    Robins, Marthony; Solomon, Justin; Hoye, Jocelyn; Smith, Taylor; Ebner, Lukas; Samei, Ehsan

    2017-03-01

    The purpose of this study was to establish volumetric exchangeability between real and computational lung lesions in CT. We compared the overall relative volume estimation performance of segmentation tools when used to measure real lesions in actual patient CT images and computational lesions virtually inserted into the same patient images (i.e., hybrid datasets). Pathologically confirmed malignancies from 30 thoracic patient cases from Reference Image Database to Evaluate Therapy Response (RIDER) were modeled and used as the basis for the comparison. Lesions included isolated nodules as well as those attached to the pleura or other lung structures. Patient images were acquired using a 16 detector row or 64 detector row CT scanner (Lightspeed 16 or VCT; GE Healthcare). Scans were acquired using standard chest protocols during a single breath-hold. Virtual 3D lesion models based on real lesions were developed in Duke Lesion Tool (Duke University), and inserted using a validated image-domain insertion program. Nodule volumes were estimated using multiple commercial segmentation tools (iNtuition, TeraRecon, Inc., Syngo.via, Siemens Healthcare, and IntelliSpace, Philips Healthcare). Consensus based volume comparison showed consistent trends in volume measurement between real and virtual lesions across all software. The average percent bias (+/- standard error) shows -9.2+/-3.2% for real lesions versus -6.7+/-1.2% for virtual lesions with tool A, 3.9+/-2.5% and 5.0+/-0.9% for tool B, and 5.3+/-2.3% and 1.8+/-0.8% for tool C, respectively. Virtual lesion volumes were statistically similar to those of real lesions (.05 in most cases. Results suggest that hybrid datasets had similar inter-algorithm variability compared to real datasets.

  1. Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance.

    Science.gov (United States)

    Liu, Bo; Cheng, H D; Huang, Jianhua; Tian, Jiawei; Liu, Jiafeng; Tang, Xianglong

    2009-08-01

    Because of its complicated structure, low signal/noise ratio, low contrast and blurry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, a novel segmentation method for BUS images without human intervention is proposed. Unlike most published approaches, the proposed method handles the segmentation problem by using a two-step strategy: ROI generation and ROI segmentation. First, a well-trained texture classifier categorizes the tissues into different classes, and the background knowledge rules are used for selecting the regions of interest (ROIs) from them. Second, a novel probability distance-based active contour model is applied for segmenting the ROIs and finding the accurate positions of the breast tumors. The active contour model combines both global statistical information and local edge information, using a level set approach. The proposed segmentation method was performed on 103 BUS images (48 benign and 55 malignant). To validate the performance, the results were compared with the corresponding tumor regions marked by an experienced radiologist. Three error metrics, true-positive ratio (TP), false-negative ratio (FN) and false-positive ratio (FP) were used for measuring the performance of the proposed method. The final results (TP = 91.31%, FN = 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically.

  2. PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods

    Science.gov (United States)

    Berthon, Beatrice; Häggström, Ida; Apte, Aditya; Beattie, Bradley J.; Kirov, Assen S.; Humm, John L.; Marshall, Christopher; Spezi, Emiliano; Larsson, Anne; Schmidtlein, C. Ross

    2016-01-01

    Purpose This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques. Methods PETSTEP was implemented within Matlab as open source software. It allows generating three-dimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images. Results PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods. Conclusions PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods. PMID:26321409

  3. Diffusion-weighted imaging-guided MR spectroscopy in breast lesions using readout-segmented echo-planar imaging

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Kun; Chai, Weimin; Zhan, Ying; Luo, Xianfu; Yan, Fuhua [Shanghai Jiao Tong University School of Medicine, Department of Radiology, Ruijin Hospital, Shanghai (China); Fu, Caixia [Siemens MRI Center, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen (China); Shen, Kunwei [Shanghai Jiao Tong University School of Medicine, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai (China)

    2016-06-15

    To investigate the feasibility and effectiveness of diffusion-weighted imaging (DWI)-guided magnetic resonance spectroscopy (MRS) using readout-segmented echo-planar imaging (RS-EPI) to characterise breast lesions. A total of 258 patients with 258 suspicious breast lesions larger than 1 cm in diameter were examined using DWI-guided, single-voxel MRS with RS-EPI. The mean total choline-containing compound (tCho) signal-to-noise ratio (SNR) and concentration were used for the interpretation of MRS data. T-tests, χ{sup 2}-tests, receiver operating characteristic (ROC) curve analyses and Pearson correlations were conducted for statistical analysis. Histologically, 183 lesions were malignant, and 75 lesions were benign. Both the mean tCho SNR and concentration of malignant lesions were higher than those of benign lesions (6.23 ± 3.30 AU/mL vs. 1.26 ± 1.75 AU/mL and 3.17 ± 2.03 mmol/kg vs. 0.86 ± 0.83 mmol/kg, respectively; P < 0.0001). For a tCho SNR of 2.0 AU/mL and a concentration of 1.76 mmol/kg, the corresponding areas under the ROC curves were 0.93 and 0.90, respectively. The mean tCho SNR and concentration negatively correlated with apparent diffusion coefficients calculated from RS-EPI, with correlation coefficients of -0.54 and -0.48, respectively. DWI-guided MRS using RS-EPI is feasible and accurate for characterising breast lesions. (orig.)

  4. Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

    Science.gov (United States)

    Rajab, Maher I

    2011-11-01

    Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

  5. Segmentation of liver tumors on CT images

    International Nuclear Information System (INIS)

    Pescia, D.

    2011-01-01

    This thesis is dedicated to 3D segmentation of liver tumors in CT images. This is a task of great clinical interest since it allows physicians benefiting from reproducible and reliable methods for segmenting such lesions. Accurate segmentation would indeed help them during the evaluation of the lesions, the choice of treatment and treatment planning. Such a complex segmentation task should cope with three main scientific challenges: (i) the highly variable shape of the structures being sought, (ii) their similarity of appearance compared with their surrounding medium and finally (iii) the low signal to noise ratio being observed in these images. This problem is addressed in a clinical context through a two step approach, consisting of the segmentation of the entire liver envelope, before segmenting the tumors which are present within the envelope. We begin by proposing an atlas-based approach for computing pathological liver envelopes. Initially images are pre-processed to compute the envelopes that wrap around binary masks in an attempt to obtain liver envelopes from estimated segmentation of healthy liver parenchyma. A new statistical atlas is then introduced and used to segmentation through its diffeomorphic registration to the new image. This segmentation is achieved through the combination of image matching costs as well as spatial and appearance prior using a multi-scale approach with MRF. The second step of our approach is dedicated to lesions segmentation contained within the envelopes using a combination of machine learning techniques and graph based methods. First, an appropriate feature space is considered that involves texture descriptors being determined through filtering using various scales and orientations. Then, state of the art machine learning techniques are used to determine the most relevant features, as well as the hyper plane that separates the feature space of tumoral voxels to the ones corresponding to healthy tissues. Segmentation is then

  6. New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images

    International Nuclear Information System (INIS)

    Del C Valdes Hernandez, Maria; Ferguson, Karen J.; Chappell, Francesca M.; Wardlaw, Joanna M.

    2010-01-01

    Brain tissue segmentation by conventional threshold-based techniques may have limited accuracy and repeatability in older subjects. We present a new multispectral magnetic resonance (MR) image analysis approach for segmenting normal and abnormal brain tissue, including white matter lesions (WMLs). We modulated two 1.5T MR sequences in the red/green colour space and calculated the tissue volumes using minimum variance quantisation. We tested it on 14 subjects, mean age 73.3 ± 10 years, representing the full range of WMLs and atrophy. We compared the results of WML segmentation with those using FLAIR-derived thresholds, examined the effect of sampling location, WML amount and field inhomogeneities, and tested observer reliability and accuracy. FLAIR-derived thresholds were significantly affected by the location used to derive the threshold (P = 0.0004) and by WML volume (P = 0.0003), and had higher intra-rater variability than the multispectral technique (mean difference ± SD: 759 ± 733 versus 69 ± 326 voxels respectively). The multispectral technique misclassified 16 times fewer WMLs. Initial testing suggests that the multispectral technique is highly reproducible and accurate with the potential to be applied to routinely collected clinical MRI data. (orig.)

  7. Quantitative performance evaluation of 124I PET/MRI lesion dosimetry in differentiated thyroid cancer

    Science.gov (United States)

    Wierts, R.; Jentzen, W.; Quick, H. H.; Wisselink, H. J.; Pooters, I. N. A.; Wildberger, J. E.; Herrmann, K.; Kemerink, G. J.; Backes, W. H.; Mottaghy, F. M.

    2018-01-01

    The aim was to investigate the quantitative performance of 124I PET/MRI for pre-therapy lesion dosimetry in differentiated thyroid cancer (DTC). Phantom measurements were performed on a PET/MRI system (Biograph mMR, Siemens Healthcare) using 124I and 18F. The PET calibration factor and the influence of radiofrequency coil attenuation were determined using a cylindrical phantom homogeneously filled with radioactivity. The calibration factor was 1.00  ±  0.02 for 18F and 0.88  ±  0.02 for 124I. Near the radiofrequency surface coil an underestimation of less than 5% in radioactivity concentration was observed. Soft-tissue sphere recovery coefficients were determined using the NEMA IEC body phantom. Recovery coefficients were systematically higher for 18F than for 124I. In addition, the six spheres of the phantom were segmented using a PET-based iterative segmentation algorithm. For all 124I measurements, the deviations in segmented lesion volume and mean radioactivity concentration relative to the actual values were smaller than 15% and 25%, respectively. The effect of MR-based attenuation correction (three- and four-segment µ-maps) on bone lesion quantification was assessed using radioactive spheres filled with a K2HPO4 solution mimicking bone lesions. The four-segment µ-map resulted in an underestimation of the imaged radioactivity concentration of up to 15%, whereas the three-segment µ-map resulted in an overestimation of up to 10%. For twenty lesions identified in six patients, a comparison of 124I PET/MRI to PET/CT was performed with respect to segmented lesion volume and radioactivity concentration. The interclass correlation coefficients showed excellent agreement in segmented lesion volume and radioactivity concentration (0.999 and 0.95, respectively). In conclusion, it is feasible that accurate quantitative 124I PET/MRI could be used to perform radioiodine pre-therapy lesion dosimetry in DTC.

  8. Contour tracing for segmentation of mammographic masses

    International Nuclear Information System (INIS)

    Elter, Matthias; Held, Christian; Wittenberg, Thomas

    2010-01-01

    CADx systems have the potential to support radiologists in the difficult task of discriminating benign and malignant mammographic lesions. The segmentation of mammographic masses from the background tissue is an important module of CADx systems designed for the characterization of mass lesions. In this work, a novel approach to this task is presented. The segmentation is performed by automatically tracing the mass' contour in-between manually provided landmark points defined on the mass' margin. The performance of the proposed approach is compared to the performance of implementations of three state-of-the-art approaches based on region growing and dynamic programming. For an unbiased comparison of the different segmentation approaches, optimal parameters are selected for each approach by means of tenfold cross-validation and a genetic algorithm. Furthermore, segmentation performance is evaluated on a dataset of ROI and ground-truth pairs. The proposed method outperforms the three state-of-the-art methods. The benchmark dataset will be made available with publication of this paper and will be the first publicly available benchmark dataset for mass segmentation.

  9. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  10. A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

    Science.gov (United States)

    Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

    2018-01-01

    The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

  11. Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach.

    Science.gov (United States)

    Beichel, Reinhard R; Van Tol, Markus; Ulrich, Ethan J; Bauer, Christian; Chang, Tangel; Plichta, Kristin A; Smith, Brian J; Sunderland, John J; Graham, Michael M; Sonka, Milan; Buatti, John M

    2016-06-01

    The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the "just-enough-interaction" principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical oncology decision-making. The properties

  12. Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach

    Energy Technology Data Exchange (ETDEWEB)

    Beichel, Reinhard R., E-mail: reinhard-beichel@uiowa.edu [Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242 (United States); Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States); Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242 (United States); Van Tol, Markus; Ulrich, Ethan J.; Bauer, Christian [Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242 (United States); Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242 (United States); Chang, Tangel; Plichta, Kristin A. [Department of Radiation Oncology, University of Iowa, Iowa City, Iowa 52242 (United States); Smith, Brian J. [Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242 (United States); Sunderland, John J.; Graham, Michael M. [Department of Radiology, University of Iowa, Iowa City, Iowa 52242 (United States); Sonka, Milan [Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242 (United States); Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242 (United States); Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States); Buatti, John M. [Department of Radiation Oncology, University of Iowa, Iowa City, Iowa 52242 (United States); Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States)

    2016-06-15

    Purpose: The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. Methods: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the “just-enough-interaction” principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Results: Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Conclusions: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in

  13. Generation of connectivity-preserving surface models of multiple sclerosis lesions.

    Science.gov (United States)

    Meruvia-Pastor, Oscar; Xiao, Mei; Soh, Jung; Sensen, Christoph W

    2011-01-01

    Progression of multiple sclerosis (MS) results in brain lesions caused by white matter inflammation. MS lesions have various shapes, sizes and locations, affecting cognitive abilities of patients to different extents. To facilitate the visualization of the brain lesion distribution, we have developed a software tool to build 3D surface models of MS lesions. This tool allows users to create 3D models of lesions quickly and to visualize the lesions and brain tissues using various visual attributes and configurations. The software package is based on breadth-first search based 3D connected component analysis and a 3D flood-fill based region growing algorithm to generate 3D models from binary or non-binary segmented medical image stacks.

  14. Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

    Science.gov (United States)

    Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

    1996-04-01

    The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

  15. COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES

    Directory of Open Access Journals (Sweden)

    A.A. Haseena Thasneem

    2015-05-01

    Full Text Available This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive, Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging, Contour models (Active Contour Model and Chan - Vese Model and Spectral Clustering. Accuracy, sensitivity, specificity, Border error, Hammoude distance, Hausdorff distance, MSE, PSNR and elapsed time metrices were used to evaluate various segmentation techniques.

  16. Semiautomatic segmentation of liver metastases on volumetric CT images

    International Nuclear Information System (INIS)

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng

    2015-01-01

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

  17. Volumetric multimodality neural network for brain tumor segmentation

    Science.gov (United States)

    Silvana Castillo, Laura; Alexandra Daza, Laura; Carlos Rivera, Luis; Arbeláez, Pablo

    2017-11-01

    Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.

  18. Performance Analysis of Segmentation of Hyperspectral Images Based on Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    Praveen Agarwal

    2017-06-01

    Full Text Available Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified with some mathematical equations. The spatial constraint allows taking into account the inherent spatial relationships of any image and its color. This approach provides effective PSNR for the segmented image. These results have the better performance as the segmented images are compared with Watershed & Region Growing Algorithm and provide effective segmentation for the Spectral Images & Medical Images.

  19. Rhythm-based segmentation of Popular Chinese Music

    DEFF Research Database (Denmark)

    Jensen, Karl Kristoffer

    2005-01-01

    We present a new method to segment popular music based on rhythm. By computing a shortest path based on the self-similarity matrix calculated from a model of rhythm, segmenting boundaries are found along the di- agonal of the matrix. The cost of a new segment is opti- mized by matching manual...... and automatic segment boundaries. We compile a small song database of 21 randomly selected popular Chinese songs which come from Chinese Mainland, Taiwan and Hong Kong. The segmenting results on the small corpus show that 78% manual segmentation points are detected and 74% auto- matic segmentation points...

  20. Hydrophilic segmented block copolymers based on poly(ethylene oxide) and monodisperse amide segments

    NARCIS (Netherlands)

    Husken, D.; Feijen, Jan; Gaymans, R.J.

    2007-01-01

    Segmented block copolymers based on poly(ethylene oxide) (PEO) flexible segments and monodisperse crystallizable bisester tetra-amide segments were made via a polycondensation reaction. The molecular weight of the PEO segments varied from 600 to 4600 g/mol and a bisester tetra-amide segment (T6T6T)

  1. Market Segmentation in Business Technology Base: The Case of Segmentation of Sparkling

    Directory of Open Access Journals (Sweden)

    Valéria Riscarolli

    2014-08-01

    Full Text Available A common market segmentation premise for products and services rules consumer behavior as the segmentation center piece. Would this be the logic for segmentation used by small technology based companies? In this article we target at determining the principles of market segmentation used by a vitiwinery company, as research object. This company is recognized by its products excellence, either in domestic as well as in the foreign market, among 13 distinct countries. The research method used is a case study, through information from the company’s CEOs and crossed by primary information from observation and formal registries and documents of the company. In this research we look at sparkling wines market segmentation. Main results indicate that the winery studied considers only technological elements as the basis to build a market segment. One may conclude that a market segmentation for this company is based upon technological dominion of sparkling wines production, aligned with a premium-price policy. In the company, directorship believes that as sparkling wines market is still incipient in the country, sparkling wine market segments will form and consolidate after the evolution of consumers tasting preferences, depending on technologies that boost sparkling wines quality. 

  2. Poly(ether amide) segmented block copolymers with adipicacid based tetra amide segments

    NARCIS (Netherlands)

    Biemond, G.J.E.; Feijen, Jan; Gaymans, R.J.

    2007-01-01

    Poly(tetramethylene oxide)-based poly(ether ester amide)s with monodisperse tetraamide segments were synthesized. The tetraamide segment was based on adipic acid, terephthalic acid, and hexamethylenediamine. The synthesis method of the copolymers and the influence of the tetraamide concentration,

  3. Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach.

    Science.gov (United States)

    Shen, Shan; Szameitat, André J; Sterr, Annette

    2008-07-01

    Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral MRI modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3-D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that, when lesions were present, the voxel-intensity-based segmentation and the spatial-location-based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated, which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity-based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were prespecified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (similarity index > 0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.

  4. A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe.

    Science.gov (United States)

    Mah, Yee-Haur; Jager, Rolf; Kennard, Christopher; Husain, Masud; Nachev, Parashkev

    2014-07-01

    Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique-lesion-function mapping-is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard-manual segmentation-sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region. Copyright © 2012 Elsevier Ltd. All rights reserved.

  5. Lesiones traumáticas de los anexos y del segmento anterior del ojo en los boxeadores Traumatic lesions of the adnexa and of the anterior segment of the eye in fighters

    Directory of Open Access Journals (Sweden)

    Agustín Fernández Sánchez

    2003-06-01

    Full Text Available Se realizó un estudio retrospectivo con una muestra de 71 boxeadores a los cuales se les examinaron las estructuras de referencia mediante oftalmoscopia directa y biomicroscopia ocular, antes de las 6 horas posteriores al término de sus peleas; donde se obtuvo como resultado que los anexos afectados fueron los párpados con 5 hematomas, 4 edemas y 1 excoriación; y la conjuntiva con 4 hiperemias y 8 hemorragias. En el segmento anterior del ojo sólo se afectó la córnea con 13 lesiones en total, divididas en 10 excoriaciones superficiales y 3 profundas. Las lesiones traumáticas predominantes en los anexos del ojo son las de los párpados y las de la conjuntiva y las del segmento anterior del ojo, las de la córnea. El ojo más afectado fue el izquierdo y las lesiones más frecuentes fueron en las categorías de 13 y 14, 17 y 18 y 19 y 34 años las corneales, las conjuntivales y la de los párpados, respectivamente.A retrospective study was carried out with a sample of 71 fighters, whose reference structures were examined by direct ophthalmoscopy and ocular biomicroscopy within the 6 hours following their fights. As a result, it was observed that the affected adnexa were the eyelids with 5 hematomas, 4 edemas and 1 excoriation; and the conjunctiva with 4 hyperemias and 8 hemorrhages. In the anteriro segment, only the cornea was affected with 13 lesions in all, divided into 10 superficial and 3 deep excoriations.The traumatic lesions prevailing in the adnexa of the eye are those of the eyelids and the conjunctiva, whereas the predominating traumatic lesions of the anterior segment of the eye are those of the cornea. The left eye was the most affected and the most frequent lesions were in the categories 13 and 14, 17 and 18 and 19 and 34 years old in the cornea, the conjunctiva and the eyelids, respectively.

  6. SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    A. Sharma

    2015-03-01

    Full Text Available Saliency gives the way as humans see any image and saliency based segmentation can be eventually helpful in Psychovisual image interpretation. Keeping this in view few saliency models are used along with segmentation algorithm and only the salient segments from image have been extracted. The work is carried out for terrestrial images as well as for satellite images. The methodology used in this work extracts those segments from segmented image which are having higher or equal saliency value than a threshold value. Salient and non salient regions of image become foreground and background respectively and thus image gets separated. For carrying out this work a dataset of terrestrial images and Worldview 2 satellite images (sample data are used. Results show that those saliency models which works better for terrestrial images are not good enough for satellite image in terms of foreground and background separation. Foreground and background separation in terrestrial images is based on salient objects visible on the images whereas in satellite images this separation is based on salient area rather than salient objects.

  7. Limitations and pitfalls of Couinaud's segmentation of the liver in transaxial Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Strunk, H; Textor, J; Willinek, W [Department of Radiology, University of Bonn, Sigmund Freud-Strasse 25, 53105, Bonn (Germany); Stuckmann, G [Department of Radiology, Kantonsspital Winterthur (Switzerland)

    2003-11-01

    The segmental anatomy of the human liver has become a matter of increasing interest to the radiologist, especially in view of the need for an accurate preoperative localization of focal hepatic lesions. In this review article first an overview of the different classical concepts for delineating segmental and subsegmental anatomy on US, transaxial CT, and MR images is given. Essentially, these procedures are based on Couinaud's concept of three vertical planes that divide the liver into four segments and of a transverse scissura that further subdivides the segments into two subsegments each. In a second part, the limitations of these methods are delineated and discussed with the conclusion that if exact preoperative localization of hepatic lesions is needed, tumor must be located relative to the avascular planes between the different portal territories. (orig.)

  8. Limitations and pitfalls of Couinaud's segmentation of the liver in transaxial Imaging

    International Nuclear Information System (INIS)

    Strunk, H.; Textor, J.; Willinek, W.; Stuckmann, G.

    2003-01-01

    The segmental anatomy of the human liver has become a matter of increasing interest to the radiologist, especially in view of the need for an accurate preoperative localization of focal hepatic lesions. In this review article first an overview of the different classical concepts for delineating segmental and subsegmental anatomy on US, transaxial CT, and MR images is given. Essentially, these procedures are based on Couinaud's concept of three vertical planes that divide the liver into four segments and of a transverse scissura that further subdivides the segments into two subsegments each. In a second part, the limitations of these methods are delineated and discussed with the conclusion that if exact preoperative localization of hepatic lesions is needed, tumor must be located relative to the avascular planes between the different portal territories. (orig.)

  9. Value of a new multiparametric score for prediction of microvascular obstruction lesions in ST-segment elevation myocardial infarction revascularized by percutaneous coronary intervention.

    Science.gov (United States)

    Amabile, Nicolas; Jacquier, Alexis; Gaudart, Jean; Sarran, Anthony; Shuaib, Anes; Panuel, Michel; Moulin, Guy; Bartoli, Jean-Michel; Paganelli, Franck

    2010-10-01

    Despite improvement in revascularization strategies, microvascular obstruction (MO) lesions remain associated with poor outcome after ST-segment elevation myocardial infarction (STEMI). To establish a bedside-available score for predicting MO lesions in STEMI, with cardiac magnetic resonance imaging (CMR) as the reference standard, and to test its prognostic value for clinical outcome. Patients with STEMI of4 accurately identified microcirculatory injuries (sensitivity 84%; specificity 82%) and independently predicted the presence of MO lesions on CMR. MO score>4 predicted adverse cardiovascular events during the first year after STEMI (relative risk 2.60 [1.10-6.60], p=0.03). MO lesions are frequent in PCI-treated STEMI and are associated with larger MIs. MO score accurately predicted MO lesions and identified patients with poor outcome post-STEMI. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

  10. Complete revascularisation versus treatment of the culprit lesion only in patients with ST-segment elevation myocardial infarction and multivessel disease (DANAMI-3—PRIMULTI)

    DEFF Research Database (Denmark)

    Engstrøm, Thomas; Kelbæk, Henning; Helqvist, Steffen

    2015-01-01

    BACKGROUND: Patients with acute ST-segment elevation myocardial infarction (STEMI) and multivessel coronary disease have a worse prognosis compared with individuals with single-vessel disease. We aimed to study the clinical outcome of patients with STEMI treated with fractional flow reserve (FFR...... electronically via a web-based system in permuted blocks of varying size by the clinician who did the primary PCI. All patients received best medical treatment. The primary endpoint was a composite of all-cause mortality, non-fatal reinfarction, and ischaemia-driven revascularization of lesions in non...

  11. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.

    Directory of Open Access Journals (Sweden)

    Thomas Samaille

    Full Text Available White matter hyperintensities (WMH on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm, a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC of 0.96 and a mean similarity index (SI of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN and support vector machines (SVM as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM, and did not need any training set.

  12. GLOBAL CLASSIFICATION OF DERMATITIS DISEASE WITH K-MEANS CLUSTERING IMAGE SEGMENTATION METHODS

    OpenAIRE

    Prafulla N. Aerkewar1 & Dr. G. H. Agrawal2

    2018-01-01

    The objective of this paper to presents a global technique for classification of different dermatitis disease lesions using the process of k-Means clustering image segmentation method. The word global is used such that the all dermatitis disease having skin lesion on body are classified in to four category using k-means image segmentation and nntool of Matlab. Through the image segmentation technique and nntool can be analyze and study the segmentation properties of skin lesions occurs in...

  13. In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading.

    Science.gov (United States)

    Celi, Simona; Berti, Sergio

    2014-10-01

    Optical coherence tomography (OCT) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. This technique is particularly useful for studying coronary atherosclerosis. In this paper, we present a new framework that allows a segmentation and quantification of OCT images of coronary arteries to define the plaque type and stenosis grading. These analyses are usually carried out on-line on the OCT-workstation where measuring is mainly operator-dependent and mouse-based. The aim of this program is to simplify and improve the processing of OCT images for morphometric investigations and to present a fast procedure to obtain 3D geometrical models that can also be used for external purposes such as for finite element simulations. The main phases of our toolbox are the lumen segmentation and the identification of the main tissues in the artery wall. We validated the proposed method with identification and segmentation manually performed by expert OCT readers. The method was evaluated on ten datasets from clinical routine and the validation was performed on 210 images randomly extracted from the pullbacks. Our results show that automated segmentation of the vessel and of the tissue components are possible off-line with a precision that is comparable to manual segmentation for the tissue component and to the proprietary-OCT-console for the lumen segmentation. Several OCT sections have been processed to provide clinical outcome. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. NSCT BASED LOCAL ENHANCEMENT FOR ACTIVE CONTOUR BASED IMAGE SEGMENTATION APPLICATION

    Directory of Open Access Journals (Sweden)

    Hiren Mewada

    2010-08-01

    Full Text Available Because of cross-disciplinary nature, Active Contour modeling techniques have been utilized extensively for the image segmentation. In traditional active contour based segmentation techniques based on level set methods, the energy functions are defined based on the intensity gradient. This makes them highly sensitive to the situation where the underlying image content is characterized by image nonhomogeneities due to illumination and contrast condition. This is the most difficult problem to make them as fully automatic image segmentation techniques. This paper introduces one of the approaches based on image enhancement to this problem. The enhanced image is obtained using NonSubsampled Contourlet Transform, which improves the edges strengths in the direction where the illumination is not proper and then active contour model based on level set technique is utilized to segment the object. Experiment results demonstrate that proposed method can be utilized along with existing active contour model based segmentation method under situation characterized by intensity non-homogeneity to make them fully automatic.

  15. Unsupervised motion-based object segmentation refined by color

    Science.gov (United States)

    Piek, Matthijs C.; Braspenning, Ralph; Varekamp, Chris

    2003-06-01

    For various applications, such as data compression, structure from motion, medical imaging and video enhancement, there is a need for an algorithm that divides video sequences into independently moving objects. Because our focus is on video enhancement and structure from motion for consumer electronics, we strive for a low complexity solution. For still images, several approaches exist based on colour, but these lack in both speed and segmentation quality. For instance, colour-based watershed algorithms produce a so-called oversegmentation with many segments covering each single physical object. Other colour segmentation approaches exist which somehow limit the number of segments to reduce this oversegmentation problem. However, this often results in inaccurate edges or even missed objects. Most likely, colour is an inherently insufficient cue for real world object segmentation, because real world objects can display complex combinations of colours. For video sequences, however, an additional cue is available, namely the motion of objects. When different objects in a scene have different motion, the motion cue alone is often enough to reliably distinguish objects from one another and the background. However, because of the lack of sufficient resolution of efficient motion estimators, like the 3DRS block matcher, the resulting segmentation is not at pixel resolution, but at block resolution. Existing pixel resolution motion estimators are more sensitive to noise, suffer more from aperture problems or have less correspondence to the true motion of objects when compared to block-based approaches or are too computationally expensive. From its tendency to oversegmentation it is apparent that colour segmentation is particularly effective near edges of homogeneously coloured areas. On the other hand, block-based true motion estimation is particularly effective in heterogeneous areas, because heterogeneous areas improve the chance a block is unique and thus decrease the

  16. An automatic quantification system for MS lesions with integrated DICOM structured reporting (DICOM-SR) for implementation within a clinical environment

    Science.gov (United States)

    Jacobs, Colin; Ma, Kevin; Moin, Paymann; Liu, Brent

    2010-03-01

    Multiple Sclerosis (MS) is a common neurological disease affecting the central nervous system characterized by pathologic changes including demyelination and axonal injury. MR imaging has become the most important tool to evaluate the disease progression of MS which is characterized by the occurrence of white matter lesions. Currently, radiologists evaluate and assess the multiple sclerosis lesions manually by estimating the lesion volume and amount of lesions. This process is extremely time-consuming and sensitive to intra- and inter-observer variability. Therefore, there is a need for automatic segmentation of the MS lesions followed by lesion quantification. We have developed a fully automatic segmentation algorithm to identify the MS lesions. The segmentation algorithm is accelerated by parallel computing using Graphics Processing Units (GPU) for practical implementation into a clinical environment. Subsequently, characterized quantification of the lesions is performed. The quantification results, which include lesion volume and amount of lesions, are stored in a structured report together with the lesion location in the brain to establish a standardized representation of the disease progression of the patient. The development of this structured report in collaboration with radiologists aims to facilitate outcome analysis and treatment assessment of the disease and will be standardized based on DICOM-SR. The results can be distributed to other DICOM-compliant clinical systems that support DICOM-SR such as PACS. In addition, the implementation of a fully automatic segmentation and quantification system together with a method for storing, distributing, and visualizing key imaging and informatics data in DICOM-SR for MS lesions improves the clinical workflow of radiologists and visualizations of the lesion segmentations and will provide 3-D insight into the distribution of lesions in the brain.

  17. Multidimensional Brain MRI segmentation using graph cuts

    International Nuclear Information System (INIS)

    Lecoeur, Jeremy

    2010-01-01

    This thesis deals with the segmentation of multimodal brain MRIs by graph cuts method. First, we propose a method that utilizes three MRI modalities by merging them. The border information given by the spectral gradient is then challenged by a region information, given by the seeds selected by the user, using a graph cut algorithm. Then, we propose three enhancements of this method. The first consists in finding an optimal spectral space because the spectral gradient is based on natural images and then inadequate for multimodal medical images. This results in a learning based segmentation method. We then explore the automation of the graph cut method. Here, the various pieces of information usually given by the user are inferred from a robust expectation-maximization algorithm. We show the performance of these two enhanced versions on multiple sclerosis lesions. Finally, we integrate atlases for the automatic segmentation of deep brain structures. These three new techniques show the adaptability of our method to various problems. Our different segmentation methods are better than most of nowadays techniques, speaking of computation time or segmentation accuracy. (authors)

  18. Pathologic features of lower extremity arterial lesions in diabetes mellitus:an analysis of 162 patients

    International Nuclear Information System (INIS)

    Guo Xiangjiang; Zhang Jiwei

    2010-01-01

    Objective: To investigate the angiographic manifestations of lower extremity atherosclerotic occlusion in patients with diabetes mellitus. Methods: The angiographic findings of lower extremity in 162 patients with diabetes mellitus were retrospectively analyzed. (1) The arteries of lower extremity were divided into the following four segments: iliac, femoral, popliteal and crural artery. The involvements of these arteries were documented. (2) Based on the lesion's number, location, nature (stricture or occlusion) and length ( 5 cm), the diabetic arterial diseases were categorized. Results: (1) Of 162 diabetic lower limbs, multiple segmental lesions were seen in 131, superficial femoral arterial lesions in 130, and crural arterial lesions in 139, of which 130 arterial lesions had at least two below-the-knee arteries being involved. (2) Based on segmental angiographic classification, a total of 660 vascular lesions were detected, including stricture lesions (33.8%) and occlusive lesions (66.2%). Of the 437 occlusions, 70.5% were located in below-the-knee arteries, and most of which were longer than 10 cm and located in anterior and posterior tibial arteries, while only a few peroneal arteries were involved (P < 0.0001). One hundred and fifty-two lesions were detected in superficial femoral arteries, of which 49 (31.2%) were located at the origin of the superficial femoral artery and 56 (35.7) were in the adductor canal hiatus. Conclusion: The main feature of peripheral arterial disease of lower extremity caused by diabetes mellitus is multi-level atherosclerotic occlusion, the superficial femoral and the crural arteries are most likely to be involved. The lesions of superficial femoral artery are often located at the arterial origin and in the adductor canal hiatus, while the deep femoral artery and the femoral artery are less involved. Long occlusive lesions are more prevalent in crural arteries, especially in anterior and posterior tibial arteries. (J Intervent

  19. Remote sensing image segmentation based on Hadoop cloud platform

    Science.gov (United States)

    Li, Jie; Zhu, Lingling; Cao, Fubin

    2018-01-01

    To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

  20. Limitations and pitfalls of Couinaud's segmentation of the liver in transaxial Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Strunk, H.; Textor, J.; Willinek, W. [Department of Radiology, University of Bonn, Sigmund Freud-Strasse 25, 53105, Bonn (Germany); Stuckmann, G. [Department of Radiology, Kantonsspital Winterthur (Switzerland)

    2003-11-01

    The segmental anatomy of the human liver has become a matter of increasing interest to the radiologist, especially in view of the need for an accurate preoperative localization of focal hepatic lesions. In this review article first an overview of the different classical concepts for delineating segmental and subsegmental anatomy on US, transaxial CT, and MR images is given. Essentially, these procedures are based on Couinaud's concept of three vertical planes that divide the liver into four segments and of a transverse scissura that further subdivides the segments into two subsegments each. In a second part, the limitations of these methods are delineated and discussed with the conclusion that if exact preoperative localization of hepatic lesions is needed, tumor must be located relative to the avascular planes between the different portal territories. (orig.)

  1. Intravascular ultrasound assessment of remodelling and reference segment plaque burden in type-2 diabetic patients

    DEFF Research Database (Denmark)

    Jensen, Lisette Okkels; Thayssen, Per; Mintz, Gary S

    2007-01-01

    AIMS: Intravascular ultrasound (IVUS) assesses arterial remodelling by comparing the lesion external elastic membrane (EEM) with the reference segments; however, reference segments are rarely disease-free. The aim was to assess lesion and reference segment remodelling and plaque burden in patients...... with type-2 diabetes mellitus. METHODS AND RESULTS: We used pre-intervention IVUS to study 62 de novo lesions in 43 patients with type-2 diabetes mellitus. The lesion site was the image slice with the smallest lumen cross-sectional area (CSA). The proximal and distal reference segments were the most normal...... size [slope = -0.12 (95% CI -0.17 to -0.07); P type-2 diabetes mellitus. CONCLUSION: Lesions in type-2 diabetic patients are different from previous reports in non-diabetics. Lesions in type-2 diabetics are characterized by a large reference segment plaque burden...

  2. Video-based noncooperative iris image segmentation.

    Science.gov (United States)

    Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

    2011-02-01

    In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

  3. [Evidence-based therapy for cartilage lesions in the knee - regenerative treatment options].

    Science.gov (United States)

    Proffen, B; von Keudell, A; Vavken, P

    2012-06-01

    The treatment of cartilage defects has seen a shift from replacement to regeneration in the last few years. The rationale behind this development is the improvement in the quality-of-care for the growing segment of young patients who are prone to arthroplasty complications because of their specific characteristics - young age, high level of activity, high demand for functionality. These days, two of the most popular regenerative treatments are microfracture and autologous chondrocyte implantation (ACI). Although these new options show promising results, no final algorithm for the treatment of cartilage lesions has been established as yet. The objective of this review is to describe and compare these two treatment options and to present an evidence-based treatment algorithm for focal cartilage defects. Microfracture is a cost-effective, arthroscopic one-stage procedure, in which by drilling of the subchondral plate, mesenchymal stem cells from the bone marrow migrate into the defect and rebuild the cartilage. ACI is a two-stage procedure in which first chondrocytes are harvested, expanded in cell culture and in a second open procedure reimplanted into the cartilage defect. Microfracture is usually used for focal cartilage defects osteophyte, and for the ACI patient, periosteal hypertrophy and the need for two procedures in ACI. Only a few studies provide detailed and evidence-based information on a comparative assessment. These studies, however, are showing widely similar clinical outcomes but better histological results for ACI, which are likely to translate into better long-term outcomes. Although evidence-based studies comparing microfracture and ACI have not found significant differences in the clinical outcome, the literature does show that choosing the treatment based on the size and characteristics of the osteochondral lesion might be beneficial. The American Association of Orthopedic Surgeons suggest that contained lesions < 4 cm2 should be treated by

  4. Disseminated paracoccidioidomycosis diagnosis based on oral lesions

    Directory of Open Access Journals (Sweden)

    Liana Preto Webber

    2014-01-01

    Full Text Available Paracoccidioidomycosis (PCM is a deep mycosis with primary lung manifestations that may present cutaneous and oral lesions. Oral lesions mimic other infectious diseases or even squamous cell carcinoma, clinically and microscopically. Sometimes, the dentist is the first to detect the disease, because lung lesions are asymptomatic, or even misdiagnosed. An unusual case of PCM with 5 months of evolution presenting pulmonary, oral, and cutaneous lesions that was diagnosed by the dentist based on oral lesions is presented and discussed.

  5. Short segment myelitis as a first manifestation of neuromyelitis optica spectrum disorders.

    Science.gov (United States)

    Huh, So-Young; Kim, Su-Hyun; Hyun, Jae-Won; Jeong, In Hye; Park, Min Su; Lee, Sang-Hyun; Kim, Ho Jin

    2017-03-01

    Some patients with neuromyelitis optica spectrum disorders (NMOSD) present with spinal cord lesions extending fewer than three vertebral segments (short transverse myelitis, STM), hindering an early diagnosis. We investigated the frequency and imaging characteristics of STM lesions in patients presenting with myelitis as an initial manifestation of NMOSD. Patients seen at three referral hospitals in Korea between June 2005 and March 2015 who met the following inclusion criteria were recruited for review: seropositivity for aquaporin-4 antibody, initial presentation with myelitis and spinal cord magnetic resonance imaging (MRI) performed within 1 month of initial myelitis onset. Of the 76 enrolled patients, 65 (85.5%) collectively had 69 longitudinally extensive transverse myelitis lesions, while the remaining 11 (14.5%) had a total of 15 STM lesions. Of the 15 STM lesions, 5 spanned 2.5 vertebral segments, 6 were continuous over two segments, 3 showed a length of 1.5 segments and 1 was confined to a single segment. On axial imaging, all of the STM lesions involved the central grey matter. These MRI findings suggested that STM does not preclude the possibility of an NMOSD diagnosis.

  6. A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions.

    Science.gov (United States)

    Giri, Maria Grazia; Cavedon, Carlo; Mazzarotto, Renzo; Ferdeghini, Marco

    2016-05-01

    The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10-37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to

  7. A Dirichlet process mixture model for automatic 18F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

    International Nuclear Information System (INIS)

    Giri, Maria Grazia; Cavedon, Carlo; Mazzarotto, Renzo; Ferdeghini, Marco

    2016-01-01

    Purpose: The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. Methods: The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10–37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Results: Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve

  8. Quantification of osteolytic bone lesions in a preclinical rat trial

    Science.gov (United States)

    Fränzle, Andrea; Bretschi, Maren; Bäuerle, Tobias; Giske, Kristina; Hillengass, Jens; Bendl, Rolf

    2013-10-01

    In breast cancer, most of the patients who died, have developed bone metastasis as disease progression. Bone metastases in case of breast cancer are mainly bone destructive (osteolytic). To understand pathogenesis and to analyse response to different treatments, animal models, in our case rats, are examined. For assessment of treatment response to bone remodelling therapies exact segmentations of osteolytic lesions are needed. Manual segmentations are not only time-consuming but lack in reproducibility. Computerized segmentation tools are essential. In this paper we present an approach for the computerized quantification of osteolytic lesion volumes using a comparison to a healthy reference model. The presented qualitative and quantitative evaluation of the reconstructed bone volumes show, that the automatically segmented lesion volumes complete missing bone in a reasonable way.

  9. Spinal focal lesion detection in multiple myeloma using multimodal image features

    Science.gov (United States)

    Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

    2015-03-01

    Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

  10. A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

    Science.gov (United States)

    Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

    2016-09-01

    Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05). Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Utilising Tree-Based Ensemble Learning for Speaker Segmentation

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

    2014-01-01

    In audio and speech processing, accurate detection of the changing points between multiple speakers in speech segments is an important stage for several applications such as speaker identification and tracking. Bayesian Information Criteria (BIC)-based approaches are the most traditionally used...... for a certain condition, the model becomes biased to the data used for training limiting the model’s generalisation ability. In this paper, we propose a BIC-based tuning-free approach for speaker segmentation through the use of ensemble-based learning. A forest of segmentation trees is constructed in which each...... tree is trained using a sampled version of the speech segment. During the tree construction process, a set of randomly selected points in the input sequence is examined as potential segmentation points. The point that yields the highest ΔBIC is chosen and the same process is repeated for the resultant...

  12. Consistent interactive segmentation of pulmonary ground glass nodules identified in CT studies

    Science.gov (United States)

    Zhang, Li; Fang, Ming; Naidich, David P.; Novak, Carol L.

    2004-05-01

    Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they characteristically have extremely slow rates of growth. This problem is further magnified by the small size of many of these lesions now being routinely detected following the introduction of multislice CT scanners capable of acquiring contiguous high resolution 1 to 1.25 mm sections throughout the thorax in a single breathhold period. Although segmentation of solid nodules can be used clinically to determine volume doubling times quantitatively, reliable methods for segmentation of pure ground glass nodules have yet to be introduced. Our purpose is to evaluate a newly developed computer-based segmentation method for rapid and reproducible measurements of pure ground glass nodules. 23 pure or mixed ground glass nodules were identified in a total of 8 patients by a radiologist and subsequently segmented by our computer-based method using Markov random field and shape analysis. The computer-based segmentation was initialized by a click point. Methodological consistency was assessed using the overlap ratio between 3 segmentations initialized by 3 different click points for each nodule. The 95% confidence interval on the mean of the overlap ratios proved to be [0.984, 0.998]. The computer-based method failed on two nodules that were difficult to segment even manually either due to especially low contrast or markedly irregular margins. While achieving consistent manual segmentation of ground glass nodules has proven problematic most often due to indistinct boundaries and interobserver variability, our proposed method introduces a powerful new tool for obtaining reproducible quantitative measurements of these lesions. It is our intention to further document the value of this approach with a still larger set of ground glass nodules.

  13. Performance evaluation for volumetric segmentation of multiple sclerosis lesions using MATLAB and computing engine in the graphical processing unit (GPU)

    Science.gov (United States)

    Le, Anh H.; Park, Young W.; Ma, Kevin; Jacobs, Colin; Liu, Brent J.

    2010-03-01

    Multiple Sclerosis (MS) is a progressive neurological disease affecting myelin pathways in the brain. Multiple lesions in the white matter can cause paralysis and severe motor disabilities of the affected patient. To solve the issue of inconsistency and user-dependency in manual lesion measurement of MRI, we have proposed a 3-D automated lesion quantification algorithm to enable objective and efficient lesion volume tracking. The computer-aided detection (CAD) of MS, written in MATLAB, utilizes K-Nearest Neighbors (KNN) method to compute the probability of lesions on a per-voxel basis. Despite the highly optimized algorithm of imaging processing that is used in CAD development, MS CAD integration and evaluation in clinical workflow is technically challenging due to the requirement of high computation rates and memory bandwidth in the recursive nature of the algorithm. In this paper, we present the development and evaluation of using a computing engine in the graphical processing unit (GPU) with MATLAB for segmentation of MS lesions. The paper investigates the utilization of a high-end GPU for parallel computing of KNN in the MATLAB environment to improve algorithm performance. The integration is accomplished using NVIDIA's CUDA developmental toolkit for MATLAB. The results of this study will validate the practicality and effectiveness of the prototype MS CAD in a clinical setting. The GPU method may allow MS CAD to rapidly integrate in an electronic patient record or any disease-centric health care system.

  14. Automated lung tumor segmentation for whole body PET volume based on novel downhill region growing

    Science.gov (United States)

    Ballangan, Cherry; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Feng, Dagan

    2010-03-01

    We propose an automated lung tumor segmentation method for whole body PET images based on a novel downhill region growing (DRG) technique, which regards homogeneous tumor hotspots as 3D monotonically decreasing functions. The method has three major steps: thoracic slice extraction with K-means clustering of the slice features; hotspot segmentation with DRG; and decision tree analysis based hotspot classification. To overcome the common problem of leakage into adjacent hotspots in automated lung tumor segmentation, DRG employs the tumors' SUV monotonicity features. DRG also uses gradient magnitude of tumors' SUV to improve tumor boundary definition. We used 14 PET volumes from patients with primary NSCLC for validation. The thoracic region extraction step achieved good and consistent results for all patients despite marked differences in size and shape of the lungs and the presence of large tumors. The DRG technique was able to avoid the problem of leakage into adjacent hotspots and produced a volumetric overlap fraction of 0.61 +/- 0.13 which outperformed four other methods where the overlap fraction varied from 0.40 +/- 0.24 to 0.59 +/- 0.14. Of the 18 tumors in 14 NSCLC studies, 15 lesions were classified correctly, 2 were false negative and 15 were false positive.

  15. A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI.

    Science.gov (United States)

    Yang, Y; Van Reeth, E; Poh, C L; Tan, C H; Tham, I W K

    2014-05-01

    Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery.

  16. Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

    Directory of Open Access Journals (Sweden)

    Shoujun Zhou

    2010-08-01

    Full Text Available Abstract Background Segmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions. Methods This paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO and a vessel structure pattern detector (SPD] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern. Results By appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel

  17. Hypereosinophilic syndrome: CT findings in patients with hepatic lobar or segmental involvement

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Jae Hoon; Lee, Won Jae [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of); Lee, Dong Ho [Kyunghee University Hospital, Seoul (Korea, Republic of); Nam, Kyung Jin [Donga University College of Medicine, Pusan (Korea, Republic of)

    2000-06-01

    The purpose of this study was to describe the CT findings of hepatic hypereosinophilic syndrome in which hepatic lobes or segments were involved. Seven patients with hypereosinophilic syndrome with hepatic lobar or segmental involvement were included in our study. In all seven, diagnosis was based on liver biopsy and the results of corticosteroid treatment. CT findings were retrospectively reviewed by three radiologists, who reached a consensus. Biopsy specimens were examined, with special reference to portal and periportal inflammation. CT demonstrated well-defined, homogeneous or heterogeneous low attenuation with a straight margin limited to a hepatic lobe (n = 2), segments (n = 3), or subsegments (n = 2), particularly during the portal phase. Where there was subsegmental involvement, lesions were multiple, ovoid or wedge-shaped, and showed low attenuation. In two patients with lobar or segmental involvement, segmental portal vein narrowing was observed. Histopathologic examination disclosed eosinophilic infiltration in the periportal area, sinusoids and central veins, as well as portal phlebitis. Hypereosinophilic syndrome may involve the presence of hepatic lobar, segmental, or subsegmental low-attenuated lesions, as seen on CT images. Their presence may be related to damage of the liver parenchyma and to portal phlebitis.

  18. Hypereosinophilic syndrome: CT findings in patients with hepatic lobar or segmental involvement

    International Nuclear Information System (INIS)

    Lim, Jae Hoon; Lee, Won Jae; Lee, Dong Ho; Nam, Kyung Jin

    2000-01-01

    The purpose of this study was to describe the CT findings of hepatic hypereosinophilic syndrome in which hepatic lobes or segments were involved. Seven patients with hypereosinophilic syndrome with hepatic lobar or segmental involvement were included in our study. In all seven, diagnosis was based on liver biopsy and the results of corticosteroid treatment. CT findings were retrospectively reviewed by three radiologists, who reached a consensus. Biopsy specimens were examined, with special reference to portal and periportal inflammation. CT demonstrated well-defined, homogeneous or heterogeneous low attenuation with a straight margin limited to a hepatic lobe (n = 2), segments (n = 3), or subsegments (n = 2), particularly during the portal phase. Where there was subsegmental involvement, lesions were multiple, ovoid or wedge-shaped, and showed low attenuation. In two patients with lobar or segmental involvement, segmental portal vein narrowing was observed. Histopathologic examination disclosed eosinophilic infiltration in the periportal area, sinusoids and central veins, as well as portal phlebitis. Hypereosinophilic syndrome may involve the presence of hepatic lobar, segmental, or subsegmental low-attenuated lesions, as seen on CT images. Their presence may be related to damage of the liver parenchyma and to portal phlebitis

  19. A Dirichlet process mixture model for automatic {sup 18}F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

    Energy Technology Data Exchange (ETDEWEB)

    Giri, Maria Grazia, E-mail: mariagrazia.giri@ospedaleuniverona.it; Cavedon, Carlo [Medical Physics Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126 (Italy); Mazzarotto, Renzo [Radiation Oncology Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126 (Italy); Ferdeghini, Marco [Nuclear Medicine Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126 (Italy)

    2016-05-15

    Purpose: The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on {sup 18}F-fluorodeoxyglucose positron emission tomography ({sup 18}F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. Methods: The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10–37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Results: Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a

  20. A modified Seeded Region Growing algorithm for vessel segmentation in breast MRI images for investigating the nature of potential lesions

    Science.gov (United States)

    Glotsos, D.; Vassiou, K.; Kostopoulos, S.; Lavdas, El; Kalatzis, I.; Asvestas, P.; Arvanitis, D. L.; Fezoulidis, I. V.; Cavouras, D.

    2014-03-01

    The role of Magnetic Resonance Imaging (MRI) as an alternative protocol for screening of breast cancer has been intensively investigated during the past decade. Preliminary research results have indicated that gadolinium-agent administrative MRI scans may reveal the nature of breast lesions by analyzing the contrast-agent's uptake time. In this study, we attempt to deduce the same conclusion, however, from a different perspective by investigating, using image processing, the vascular network of the breast at two different time intervals following the administration of gadolinium. Twenty cases obtained from a 3.0-T MRI system (SIGNA HDx; GE Healthcare) were included in the study. A new modification of the Seeded Region Growing (SRG) algorithm was used to segment vessels from surrounding background. Delineated vessels were investigated by means of their topology, morphology and texture. Results have shown that it is possible to estimate the nature of the lesions with approximately 94.4% accuracy, thus, it may be claimed that the breast vascular network does encodes useful, patterned, information, which can be used for characterizing breast lesions.

  1. Image segmentation algorithm based on T-junctions cues

    Science.gov (United States)

    Qian, Yanyu; Cao, Fengyun; Wang, Lu; Yang, Xuejie

    2016-03-01

    To improve the over-segmentation and over-merge phenomenon of single image segmentation algorithm,a novel approach of combing Graph-Based algorithm and T-junctions cues is proposed in this paper. First, a method by L0 gradient minimization is applied to the smoothing of the target image eliminate artifacts caused by noise and texture detail; Then, the initial over-segmentation result of the smoothing image using the graph-based algorithm; Finally, the final results via a region fusion strategy by t-junction cues. Experimental results on a variety of images verify the new approach's efficiency in eliminating artifacts caused by noise,segmentation accuracy and time complexity has been significantly improved.

  2. Bifurcation Culprit Lesions in ST-segment Elevation Myocardial Infarction: Procedural Success and 5-year Outcome Compared With Nonbifurcation Lesions.

    Science.gov (United States)

    Salinas, Pablo; Mejía-Rentería, Hernán; Herrera-Nogueira, Raúl; Jiménez-Quevedo, Pilar; Nombela-Franco, Luis; Núñez-Gil, Iván Javier; Gonzalo, Nieves; Del Trigo, María; Pérez-Vizcayno, María José; Quirós, Alicia; Escaned, Javier; Macaya, Carlos; Fernández-Ortiz, Antonio

    2017-08-09

    We assessed short- and long-term outcomes of primary angioplasty in ST-segment elevation myocardial infarction by comparing bifurcation culprit lesions (BCL) with non-BCL. Observational study with a propensity score matched control group. Among 2746 consecutive ST-segment elevation myocardial infarction patients, we found 274 (10%) patients with BCL. The primary outcome was a composite endpoint including all-cause death, myocardial infarction, coronary artery bypass grafting or target vessel revascularization, assessed at 30-days and 5-years. Baseline characteristics showed no differences after propensity matching (1:1). In the BCL group, the most frequent strategy was provisional stenting of the main branch (84%). Compared with the non-BCL group, the procedures were technically more complex in the BCL group in terms of need for balloon dilatation (71% BCL vs 59% non-BCL; P = .003), longer procedural time (70 ± 29minutes BCL vs 62.8 ± 28.9minutes non-BCL; P = .004) and contrast use (256.2 ± 87.9mL BCL vs 221.1 ± 82.3mL non-BCL; P < .001). Main branch angiographic success was similar (93.4% BCL vs 93.8% non-BCL; P = .86). Thirty-day all-cause mortality was similar between groups: 4.7% BCL vs 5.1% non-BCL; P = .84. At the 5-year follow-up, there were no differences in all-cause death (12% BCL vs 13% non-BCL; P = .95) or the combined event (22% BCL vs 21% non-BCL; P = .43). Primary angioplasty of a BCL was technically more complex; however, main branch angiographic success was similar, and there were no differences in long-term prognosis compared with non-BCL patients. Copyright © 2017 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

  3. Dictionary Based Image Segmentation

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2015-01-01

    We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets...

  4. An Efficient Evolutionary Based Method For Image Segmentation

    OpenAIRE

    Aslanzadeh, Roohollah; Qazanfari, Kazem; Rahmati, Mohammad

    2017-01-01

    The goal of this paper is to present a new efficient image segmentation method based on evolutionary computation which is a model inspired from human behavior. Based on this model, a four layer process for image segmentation is proposed using the split/merge approach. In the first layer, an image is split into numerous regions using the watershed algorithm. In the second layer, a co-evolutionary process is applied to form centers of finals segments by merging similar primary regions. In the t...

  5. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  6. Voxel-based lesion-symptom mapping of stroke lesions underlying somatosensory deficits

    Directory of Open Access Journals (Sweden)

    Sarah Meyer

    2016-01-01

    Full Text Available The aim of this study was to investigate the relationship between stroke lesion location and the resulting somatosensory deficit. We studied exteroceptive and proprioceptive somatosensory symptoms and stroke lesions in 38 patients with first-ever acute stroke. The Erasmus modified Nottingham Sensory Assessment was used to clinically evaluate somatosensory functioning in the arm and hand within the first week after stroke onset. Additionally, more objective measures such as the perceptual threshold of touch and somatosensory evoked potentials were recorded. Non-parametric voxel-based lesion-symptom mapping was performed to investigate lesion contribution to different somatosensory deficits in the upper limb. Additionally, structural connectivity of brain areas that demonstrated the strongest association with somatosensory symptoms was determined, using probabilistic fiber tracking based on diffusion tensor imaging data from a healthy age-matched sample. Voxels with a significant association to somatosensory deficits were clustered in two core brain regions: the central parietal white matter, also referred to as the sensory component of the superior thalamic radiation, and the parietal operculum close to the insular cortex, representing the secondary somatosensory cortex. Our objective recordings confirmed findings from clinical assessments. Probabilistic tracking connected the first region to thalamus, internal capsule, brain stem, postcentral gyrus, cerebellum, and frontal pathways, while the second region demonstrated structural connections to thalamus, insular and primary somatosensory cortex. This study reveals that stroke lesions in the sensory fibers of the superior thalamocortical radiation and the parietal operculum are significantly associated with multiple exteroceptive and proprioceptive deficits in the arm and hand.

  7. Voxel-based lesion-symptom mapping of stroke lesions underlying somatosensory deficits.

    Science.gov (United States)

    Meyer, Sarah; Kessner, Simon S; Cheng, Bastian; Bönstrup, Marlene; Schulz, Robert; Hummel, Friedhelm C; De Bruyn, Nele; Peeters, Andre; Van Pesch, Vincent; Duprez, Thierry; Sunaert, Stefan; Schrooten, Maarten; Feys, Hilde; Gerloff, Christian; Thomalla, Götz; Thijs, Vincent; Verheyden, Geert

    2016-01-01

    The aim of this study was to investigate the relationship between stroke lesion location and the resulting somatosensory deficit. We studied exteroceptive and proprioceptive somatosensory symptoms and stroke lesions in 38 patients with first-ever acute stroke. The Erasmus modified Nottingham Sensory Assessment was used to clinically evaluate somatosensory functioning in the arm and hand within the first week after stroke onset. Additionally, more objective measures such as the perceptual threshold of touch and somatosensory evoked potentials were recorded. Non-parametric voxel-based lesion-symptom mapping was performed to investigate lesion contribution to different somatosensory deficits in the upper limb. Additionally, structural connectivity of brain areas that demonstrated the strongest association with somatosensory symptoms was determined, using probabilistic fiber tracking based on diffusion tensor imaging data from a healthy age-matched sample. Voxels with a significant association to somatosensory deficits were clustered in two core brain regions: the central parietal white matter, also referred to as the sensory component of the superior thalamic radiation, and the parietal operculum close to the insular cortex, representing the secondary somatosensory cortex. Our objective recordings confirmed findings from clinical assessments. Probabilistic tracking connected the first region to thalamus, internal capsule, brain stem, postcentral gyrus, cerebellum, and frontal pathways, while the second region demonstrated structural connections to thalamus, insular and primary somatosensory cortex. This study reveals that stroke lesions in the sensory fibers of the superior thalamocortical radiation and the parietal operculum are significantly associated with multiple exteroceptive and proprioceptive deficits in the arm and hand.

  8. Stereovision-Based Object Segmentation for Automotive Applications

    Directory of Open Access Journals (Sweden)

    Fu Shan

    2005-01-01

    Full Text Available Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentation method has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map ( - plane and in layered images ( - planes. The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.

  9. Major destructive asymptomatic lumbar Charcot lesion treated with three column resection and short segment reconstruction. Case report, treatment strategy and review of literature

    Directory of Open Access Journals (Sweden)

    Valancius Kestutis

    2017-01-01

    Full Text Available Charcot's spine is a long-term complication of spinal cord injury. The lesion is often localized at the caudal end of long fusion constructs and distal to the level of paraplegia. However, cases are rare and the literature relevant to the management of Charcot's arthropathy is limited. This paper reviews the clinical features, diagnosis, and surgical management of post-traumatic spinal neuroarthropathy in the current literature. We present a rare case of adjacent level Charcot's lesion of the lumbar spine in a paraplegic patient, primarily treated for traumatic spinal cord lesion 39 years before current surgery. We have performed end-to-end apposition of bone after 3 column resection of the lesion, 3D correction of the deformity, and posterior instrumentation using a four-rod construct. Although the natural course of the disease remains unclear, surgery is always favorable and remains the primary treatment modality. Posterior long-segment spinal fusion with a four-rod construct is the mainstay of treatment to prevent further morbidity. Our technique eliminated the need for more extensive anterior surgery while preserving distal motion

  10. Multifractal-based nuclei segmentation in fish images.

    Science.gov (United States)

    Reljin, Nikola; Slavkovic-Ilic, Marijeta; Tapia, Coya; Cihoric, Nikola; Stankovic, Srdjan

    2017-09-01

    The method for nuclei segmentation in fluorescence in-situ hybridization (FISH) images, based on the inverse multifractal analysis (IMFA) is proposed. From the blue channel of the FISH image in RGB format, the matrix of Holder exponents, with one-by-one correspondence with the image pixels, is determined first. The following semi-automatic procedure is proposed: initial nuclei segmentation is performed automatically from the matrix of Holder exponents by applying predefined hard thresholding; then the user evaluates the result and is able to refine the segmentation by changing the threshold, if necessary. After successful nuclei segmentation, the HER2 (human epidermal growth factor receptor 2) scoring can be determined in usual way: by counting red and green dots within segmented nuclei, and finding their ratio. The IMFA segmentation method is tested over 100 clinical cases, evaluated by skilled pathologist. Testing results show that the new method has advantages compared to already reported methods.

  11. Facial nerve hemangioma: a rare case involving the vertical segment.

    Science.gov (United States)

    Ahmadi, Neda; Newkirk, Kenneth; Kim, H Jeffrey

    2013-02-01

    This case report and literature review reports on a rare case of facial nerve hemangioma (FNH) involving the vertical facial nerve (FN) segment, and discusses the clinical presentation, imaging, pathogenesis, and management of these rare lesions. A 53-year-old male presented with a 10-year history of right hemifacial twitching and progressive facial paresis (House-Brackmann grading score V/VI). The computed tomography and magnetic resonance imaging studies confirmed an expansile lesion along the vertical FN segment. Excision and histopathologic examination demonstrated FNH. FNHs involving the vertical FN segment are extremely rare. Despite being rare lesions, we believe that familiarity with the presentation and management of FNHs are imperative. Laryngoscope, 2012. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

  12. Use of articulated registration for response assessment of individual metastatic bone lesions

    International Nuclear Information System (INIS)

    Yip, Stephen; Jeraj, Robert

    2014-01-01

    Accurate skeleton registration is necessary to match corresponding metastatic bone lesions for response assessment over multiple scans. In articulated registration (ART), whole-body skeletons are registered by auto-segmenting individual bones, then rigidly aligning them. Performance and robustness of the ART in lesion matching were evaluated and compared to other commonly used registration techniques. Sixteen prostate cancer patients were treated either with molecular targeted therapy or chemotherapy. Ten out of the 16 patients underwent the double baseline whole-body [F-18]NaF PET/CT scans for test-retest (TRT) evaluation. Twelve of the 16 patients underwent pre- and mid-treatment [F-18]NaF PET/CT scans. Skeletons at different time points were registered using ART, rigid, and deformable (DR) registration algorithms. The corresponding lesions were contoured and identified on successive PET images based on including the voxels with the standardized uptake value over 15. Each algorithm was evaluated for its ability to accurately align corresponding lesions via skeleton registration. A lesion matching score (MS) was measured for each lesion, which quantified the per cent overlap between the lesion's two corresponding contours. Three separate sensitivity studies were conducted to investigate the robustness of ART in matching: sensitivity of lesion matching to various contouring threshold levels, effects of imperfections in the bone auto-segmentation and sensitivity of mis-registration. The performance of ART (MS = 82% for both datasets, p ≪ 0.001) in lesion matching was significantly better than rigid (MS TRT  = 53%, MS Response  = 46%) and DR (MS TRT  = 46%, MS Response  = 45%) algorithms. Neither varying threshold levels for lesion contouring nor imperfect bone segmentation had significant (p∼0.10) impact on the ART matching performance as the MS remained unchanged. Despite the mis-registration reduced MS for ART, as low as 67% (p ≪ 0.001), the

  13. Sequential segmental classification of feline congenital heart disease.

    Science.gov (United States)

    Scansen, Brian A; Schneider, Matthias; Bonagura, John D

    2015-12-01

    Feline congenital heart disease is less commonly encountered in veterinary medicine than acquired feline heart diseases such as cardiomyopathy. Understanding the wide spectrum of congenital cardiovascular disease demands a familiarity with a variety of lesions, occurring both in isolation and in combination, along with an appreciation of complex nomenclature and variable classification schemes. This review begins with an overview of congenital heart disease in the cat, including proposed etiologies and prevalence, examination approaches, and principles of therapy. Specific congenital defects are presented and organized by a sequential segmental classification with respect to their morphologic lesions. Highlights of diagnosis, treatment options, and prognosis are offered. It is hoped that this review will provide a framework for approaching congenital heart disease in the cat, and more broadly in other animal species based on the sequential segmental approach, which represents an adaptation of the common methodology used in children and adults with congenital heart disease. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. A semi-automated measuring system of brain diffusion and perfusion magnetic resonance imaging abnormalities in patients with multiple sclerosis based on the integration of coregistration and tissue segmentation procedures

    International Nuclear Information System (INIS)

    Revenaz, Alfredo; Ruggeri, Massimiliano; Laganà, Marcella; Bergsland, Niels; Groppo, Elisabetta; Rovaris, Marco; Fainardi, Enrico

    2016-01-01

    Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) abnormalities in patients with multiple sclerosis (MS) are currently measured by a complex combination of separate procedures. Therefore, the purpose of this study was to provide a reliable method for reducing analysis complexity and obtaining reproducible results. We implemented a semi-automated measuring system in which different well-known software components for magnetic resonance imaging (MRI) analysis are integrated to obtain reliable measurements of DWI and PWI disturbances in MS. We generated the Diffusion/Perfusion Project (DPP) Suite, in which a series of external software programs are managed and harmonically and hierarchically incorporated by in-house developed Matlab software to perform the following processes: 1) image pre-processing, including imaging data anonymization and conversion from DICOM to Nifti format; 2) co-registration of 2D and 3D non-enhanced and Gd-enhanced T1-weighted images in fluid-attenuated inversion recovery (FLAIR) space; 3) lesion segmentation and classification, in which FLAIR lesions are at first segmented and then categorized according to their presumed evolution; 4) co-registration of segmented FLAIR lesion in T1 space to obtain the FLAIR lesion mask in the T1 space; 5) normal appearing tissue segmentation, in which T1 lesion mask is used to segment basal ganglia/thalami, normal appearing grey matter (NAGM) and normal appearing white matter (NAWM); 6) DWI and PWI map generation; 7) co-registration of basal ganglia/thalami, NAGM, NAWM, DWI and PWI maps in previously segmented FLAIR space; 8) data analysis. All these steps are automatic, except for lesion segmentation and classification. We developed a promising method to limit misclassifications and user errors, providing clinical researchers with a practical and reproducible tool to measure DWI and PWI changes in MS

  15. Leisure market segmentation : an integrated preferences/constraints-based approach

    NARCIS (Netherlands)

    Stemerding, M.P.; Oppewal, H.; Beckers, T.A.M.; Timmermans, H.J.P.

    1996-01-01

    Traditional segmentation schemes are often based on a grouping of consumers with similar preference functions. The research steps, ultimately leading to such segmentation schemes, are typically independent. In the present article, a new integrated approach to segmentation is introduced, which

  16. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Akhbardeh, Alireza; Jacobs, Michael A. [Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States); Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States) and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States)

    2012-04-15

    Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment

  17. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

    International Nuclear Information System (INIS)

    Akhbardeh, Alireza; Jacobs, Michael A.

    2012-01-01

    Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B 1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both

  18. Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases

    NARCIS (Netherlands)

    Rodríguez López, M.E.; Volk, Anja

    2014-01-01

    Gestalt-based segmentation models constitute the current state of the art in automatic segmentation of melodies. These models commonly assume that segment boundary perception is mainly triggered by local discontinuities, i.e. by abrupt changes in pitch and/or duration between neighbouring notes.

  19. High-speed MRF-based segmentation algorithm using pixonal images

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Hassanpour, H.; Naimi, H. M.

    2013-01-01

    Segmentation is one of the most complicated procedures in the image processing that has important role in the image analysis. In this paper, an improved pixon-based method for image segmentation is proposed. In proposed algorithm, complex partial differential equations (PDEs) is used as a kernel...... function to make pixonal image. Using this kernel function causes noise on images to reduce and an image not to be over-segment when the pixon-based method is used. Utilising the PDE-based method leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance...... and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually, we segment the image with the use of a Markov random field. The experimental results indicate that the proposed pixon-based approach has a reduced computational load...

  20. Dictionary Based Segmentation in Volumes

    DEFF Research Database (Denmark)

    Emerson, Monica Jane; Jespersen, Kristine Munk; Jørgensen, Peter Stanley

    2015-01-01

    We present a method for supervised volumetric segmentation based on a dictionary of small cubes composed of pairs of intensity and label cubes. Intensity cubes are small image volumes where each voxel contains an image intensity. Label cubes are volumes with voxelwise probabilities for a given...... label. The segmentation process is done by matching a cube from the volume, of the same size as the dictionary intensity cubes, to the most similar intensity dictionary cube, and from the associated label cube we get voxel-wise label probabilities. Probabilities from overlapping cubes are averaged...... and hereby we obtain a robust label probability encoding. The dictionary is computed from labeled volumetric image data based on weighted clustering. We experimentally demonstrate our method using two data sets from material science – a phantom data set of a solid oxide fuel cell simulation for detecting...

  1. Can the Diagnostics of Triangular Fibrocartilage Complex Lesions Be Improved by MRI-Based Soft-Tissue Reconstruction? An Imaging-Based Workup and Case Presentation.

    Science.gov (United States)

    Hammer, Niels; Hirschfeld, Ulrich; Strunz, Hendrik; Werner, Michael; Wolfskämpf, Thomas; Löffler, Sabine

    2017-01-01

    Introduction . The triangular fibrocartilage complex (TFCC) provides both mobility and stability of the radiocarpal joint. TFCC lesions are difficult to diagnose due to the complex anatomy. The standard treatment for TFCC lesions is arthroscopy, posing surgery-related risks onto the patients. This feasibility study aimed at developing a workup for soft-tissue reconstruction using clinical imaging, to verify these results in retrospective patient data. Methods . Microcomputed tomography ( μ -CT), 3 T magnetic resonance imaging (MRI), and plastination were used to visualize the TFCC in cadaveric specimens applying segmentation-based 3D reconstruction. This approach further trialed the MRI dataset of a patient with minor radiological TFCC alterations but persistent pain. Results . TFCC reconstruction was impossible using μ -CT only but feasible using MRI, resulting in an appreciation of its substructures, as seen in the plastinates. Applying this approach allowed for visualizing a Palmer 2C lesion in a patient, confirming ex postum the arthroscopy findings, being markedly different from MRI (Palmer 1B). Discussion . This preliminary study showed that image-based TFCC reconstruction may help to identify pathologies invisible in standard MRI. The combined approach of μ -CT, MRI, and plastination allowed for a three-dimensional appreciation of the TFCC. Image quality and time expenditure limit the approach's usefulness as a diagnostic tool.

  2. A novel line segment detection algorithm based on graph search

    Science.gov (United States)

    Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

    2018-02-01

    To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

  3. Market Segmentation Based on the Consumers' Impulsive Buying Behaviour

    Directory of Open Access Journals (Sweden)

    Mirela Mihić

    2010-12-01

    Full Text Available The major purpose of this research is to determine the sufficiently different segments of consumers based on their impulsivity in the buying behaviour. The research was conducted in Splitsko-Dalmatinska county on the sample of 180 respondents. Based on the subject matter and research goals, the basic as well as four additional hypotheses were set. The used methodology comprised of the cluster analysis, which helped to divide three segments that were named as: ‘’rational’’, ‘’somewhat rational and somewhat impulsive’’ and ‘’impulsive’’ consumers. The variance analysis was used in order to describe the segments properly and to determine whether they are different enough with respect to demographic, socio-economic characteristics and individual differences variables. The findings confirmed the hypothesis based on the possibility of dividing different consumer segments according to the analysed variables. Correlating the demographics and individual differences factors with the impulse buy, the expected results were gained. When analyzing demographics the results indicate the segment differentiation solely in the case of age and working status. However, from the aspect of majority of individual differences variables the distinction among the segments is significant.

  4. On exploiting wavelet bases in statistical region-based segmentation

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren

    2002-01-01

    Statistical region-based segmentation methods such as the Active Appearance Models establish dense correspondences by modelling variation of shape and pixel intensities in low-resolution 2D images. Unfortunately, for high-resolution 2D and 3D images, this approach is rendered infeasible due to ex...... 9-7 wavelet on cardiac MRIs and human faces show that the segmentation accuracy is minimally degraded at compression ratios of 1:10 and 1:20, respectively....

  5. Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors

    Science.gov (United States)

    Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin

    2014-03-01

    One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

  6. Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation

    Directory of Open Access Journals (Sweden)

    Jens K. Boldsen

    2018-04-01

    Full Text Available Stroke is the second most common cause of death worldwide, responsible for 6.24 million deaths in 2015 (about 11% of all deaths. Three out of four stroke survivors suffer long term disability, as many cannot return to their prior employment or live independently. Eighty-seven percent of strokes are ischemic. As an increasing volume of ischemic brain tissue proceeds to permanent infarction in the hours following the onset, immediate treatment is pivotal to increase the likelihood of good clinical outcome for the patient. Triaging stroke patients for active therapy requires assessment of the volume of salvageable and irreversible damaged tissue, respectively. With Magnetic Resonance Imaging (MRI, diffusion-weighted imaging is commonly used to assess the extent of permanently damaged tissue, the core lesion. To speed up and standardize decision-making in acute stroke management we present a fully automated algorithm, ATLAS, for delineating the core lesion. We compare performance to widely used threshold based methodology, as well as a recently proposed state-of-the-art algorithm: COMBAT Stroke. ATLAS is a machine learning algorithm trained to match the lesion delineation by human experts. The algorithm utilizes decision trees along with spatial pre- and post-regularization to outline the lesion. As input data the algorithm takes images from 108 patients with acute anterior circulation stroke from the I-Know multicenter study. We divided the data into training and test data using leave-one-out cross validation to assess performance in independent patients. Performance was quantified by the Dice index. The median Dice coefficient of ATLAS algorithm was 0.6122, which was significantly higher than COMBAT Stroke, with a median Dice coefficient of 0.5636 (p < 0.0001 and the best possible performing methods based on thresholding of the diffusion weighted images (median Dice coefficient: 0.3951 or the apparent diffusion coefficient (median Dice coefficeint

  7. 'Do not touch' lesions of the skull base

    International Nuclear Information System (INIS)

    Dobre, Mircea C.; Fischbein, Nancy

    2014-01-01

    Imaging of the skull base presents many challenges due to its anatomical complexity, numerous normal variants and lack of familiarity to many radiologists. As the skull base is a region which is not amenable to physical examination and as lesions of the skull base are generally difficult to biopsy and even more difficult to operate on, the radiologist plays a major role in directing patient management via accurate image interpretation. Knowledge of the skull base should not be limited to neuroradiologists and head and neck radiologists, however, as the central skull base is routinely included in the field of view when imaging the brain, cervical spine, or head and neck with computed tomography or magnetic resonance imaging, and hence, its nuances should be familiar to general radiologists as well. We herein review the imaging findings of a subcategory of lesions of the central skull base, the 'do not touch' lesions.

  8. Responsiveness of culture-based segmentation of organizational buyers

    Directory of Open Access Journals (Sweden)

    Veronika Jadczaková

    2013-01-01

    Full Text Available Much published work over the four decades has acknowledged market segmentation in business-to-business settings yet primarily focusing on observable segmentation bases such as firmographics or geographics. However, such bases were proved to have a weak predictive validity with respect to industrial buying behavior. Therefore, this paper attempts to add a debate to this topic by introducing new (unobservable segmentation base incorporating several facets of business culture, denoted as psychographics. The justification for this approach is that the business culture captures the collective mindset of an organization and thus enables marketers to target the organization as a whole. Given the hypothesis that culture has a merit for micro-segmentation a sample of 278 manufacturing firms was first subjected to principal component analysis and Varimax to reveal underlying cultural traits. In next step, cluster analysis was performed on retained factors to construct business profiles. Finally, non-parametric one-way analysis of variance confirmed discriminative power between profiles based on psychographics in terms of industrial buying behavior. Owing to this, business culture may assist marketers when targeting more effectively than some traditional approaches.

  9. Segmental tuberculosis verrucosa cutis

    Directory of Open Access Journals (Sweden)

    Hanumanthappa H

    1994-01-01

    Full Text Available A case of segmental Tuberculosis Verrucosa Cutis is reported in 10 year old boy. The condition was resembling the ascending lymphangitic type of sporotrichosis. The lesions cleared on treatment with INH 150 mg daily for 6 months.

  10. COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES

    OpenAIRE

    Hetangi D. Mehta*, Daxa Vekariya, Pratixa Badelia

    2017-01-01

    Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different...

  11. International market segmentation based on consumer-product relations

    NARCIS (Netherlands)

    ter Hofstede, F; Steenkamp, JBEM; Wedel, M

    With increasing competition in the global marketplace, international segmentation has become an ever more important issue in developing, positioning, and selling products across national borders. The authors propose a methodology to identify cross-national market segments, based on means-end chain

  12. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

    Science.gov (United States)

    Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

    2017-08-01

    Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

  13. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  14. Major destructive asymptomatic lumbar Charcot lesion treated with three column resection and short segment reconstruction. Case report, treatment strategy and review of literature.

    Science.gov (United States)

    Valancius, Kestutis; Garg, Gaurav; Duicu, Madalina; Hansen, Ebbe Stender; Bunger, Cody

    2017-01-01

    Charcot's spine is a long-term complication of spinal cord injury. The lesion is often localized at the caudal end of long fusion constructs and distal to the level of paraplegia. However, cases are rare and the literature relevant to the management of Charcot's arthropathy is limited. This paper reviews the clinical features, diagnosis, and surgical management of post-traumatic spinal neuroarthropathy in the current literature. We present a rare case of adjacent level Charcot's lesion of the lumbar spine in a paraplegic patient, primarily treated for traumatic spinal cord lesion 39 years before current surgery. We have performed end-to-end apposition of bone after 3 column resection of the lesion, 3D correction of the deformity, and posterior instrumentation using a four-rod construct. Although the natural course of the disease remains unclear, surgery is always favorable and remains the primary treatment modality. Posterior long-segment spinal fusion with a four-rod construct is the mainstay of treatment to prevent further morbidity. Our technique eliminated the need for more extensive anterior surgery while preserving distal motion. © The Authors, published by EDP Sciences, 2017.

  15. Do Culture-based Segments Predict Selection of Market Strategy?

    Directory of Open Access Journals (Sweden)

    Veronika Jadczaková

    2015-01-01

    Full Text Available Academists and practitioners have already acknowledged the importance of unobservable segmentation bases (such as psychographics yet still focusing on how well these bases are capable of describing relevant segments (the identifiability criterion rather than on how precisely these segments can predict (the predictability criterion. Therefore, this paper intends to add a debate to this topic by exploring whether culture-based segments do account for a selection of market strategy. To do so, a set of market strategy variables over a sample of 251 manufacturing firms was first regressed on a set of 19 cultural variables using canonical correlation analysis. Having found significant relationship in the first canonical function, it was further examined by means of correspondence analysis which cultural segments – if any – are linked to which market strategies. However, as correspondence analysis failed to find a significant relationship, it may be concluded that business culture might relate to the adoption of market strategy but not to the cultural groupings presented in the paper.

  16. Asymmetry and irregularity border as discrimination factor between melanocytic lesions

    Science.gov (United States)

    Sbrissa, David; Pratavieira, Sebastião.; Salvio, Ana Gabriela; Kurachi, Cristina; Bagnato, Vanderlei Salvadori; Costa, Luciano Da Fontoura; Travieso, Gonzalo

    2015-06-01

    Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.

  17. Segmental sensory disturbance in brain stem infarctions of the lateral lower pons and lateral medulla

    International Nuclear Information System (INIS)

    Matsumoto, Sadayuki; Yamasaki, Masahiro; Maya, Kiyomi; Imai, Terukuni; Okuda, Bungo.

    1987-01-01

    We reported on seven cases of brainstem infarctions of the lateral lower pons and lateral medulla, the sensory deficit manifested over the trunk or the leg namely segmental sensory disturbances. All patients showed dissociated sensory disturbance of pain and temperature with retained deep sensations except two cases in which touch was also slightly impaired. The sensory distribution was classified into two types. The first ''crossed type'', ipsilatral face and contralateral trunk and leg below the level was involved in 4 cases, and the second ''unilateral type'' contralateral face and trunk above the level in 3 cases. Clinico-anatomical evaluation was executed by MRI. Lesions were detected in the lateral lower pons in two cases and in the lateral medulla in one case. The location of lesions by MRI revealed more lateral lesions showed ''crossed type'' of segmental sensory disturbance and more medial lesions ''unilateral type''. It was shown that the segmental sensory disturbance could be explained by the partial involvement of the lateral spinothalamic tract, which is arranged with the fibers from the sacral segments most lateral. We considered it very important to differentiate the segmental sensory disturbance by brainstem lesion in practical clinical diagnosis. We also emphasize the type of segmental sensory disturbance could be a localizing sign in the lateral brainstem as such, ''crossed type'' indicating the lesion of the lateral portion and ''unilateral type'' the medial portion of the lateral lower brainstem. (author)

  18. Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering.

    Science.gov (United States)

    Saffarzadeh, Vahid Mohammadi; Osareh, Alireza; Shadgar, Bita

    2014-04-01

    Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.

  19. Segment-based dose optimization using a genetic algorithm

    International Nuclear Information System (INIS)

    Cotrutz, Cristian; Xing Lei

    2003-01-01

    Intensity modulated radiation therapy (IMRT) inverse planning is conventionally done in two steps. Firstly, the intensity maps of the treatment beams are optimized using a dose optimization algorithm. Each of them is then decomposed into a number of segments using a leaf-sequencing algorithm for delivery. An alternative approach is to pre-assign a fixed number of field apertures and optimize directly the shapes and weights of the apertures. While the latter approach has the advantage of eliminating the leaf-sequencing step, the optimization of aperture shapes is less straightforward than that of beamlet-based optimization because of the complex dependence of the dose on the field shapes, and their weights. In this work we report a genetic algorithm for segment-based optimization. Different from a gradient iterative approach or simulated annealing, the algorithm finds the optimum solution from a population of candidate plans. In this technique, each solution is encoded using three chromosomes: one for the position of the left-bank leaves of each segment, the second for the position of the right-bank and the third for the weights of the segments defined by the first two chromosomes. The convergence towards the optimum is realized by crossover and mutation operators that ensure proper exchange of information between the three chromosomes of all the solutions in the population. The algorithm is applied to a phantom and a prostate case and the results are compared with those obtained using beamlet-based optimization. The main conclusion drawn from this study is that the genetic optimization of segment shapes and weights can produce highly conformal dose distribution. In addition, our study also confirms previous findings that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization. Thus the technique may have useful applications in facilitating IMRT treatment planning

  20. Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis

    Science.gov (United States)

    Zhang, Jun; Ghate, Sujata V.; Grimm, Lars J.; Saha, Ashirbani; Cain, Elizabeth Hope; Zhu, Zhe; Mazurowski, Maciej A.

    2018-02-01

    Digital breast tomosynthesis (DBT) is a relatively new modality for breast imaging that can provide detailed assessment of dense tissue within the breast. In the domains of cancer diagnosis, radiogenomics, and resident education, it is important to accurately segment breast masses. However, breast mass segmentation is a very challenging task, since mass regions have low contrast difference between their neighboring tissues. Notably, the task might become more difficult in cases that were assigned BI-RADS 0 category since this category includes many lesions that are of low conspicuity and locations that were deemed to be overlapping normal tissue upon further imaging and were not sent to biopsy. Segmentation of such lesions is of particular importance in the domain of reader performance analysis and education. In this paper, we propose a novel deep learning-based method for segmentation of BI-RADS 0 lesions in DBT. The key components of our framework are an encoding path for local-to-global feature extraction, and a decoding patch to expand the images. To address the issue of limited training data, in the training stage, we propose to sample patches not only in mass regions but also in non-mass regions. We utilize a Dice-like loss function in the proposed network to alleviate the class-imbalance problem. The preliminary results on 40 subjects show promise of our method. In addition to quantitative evaluation of the method, we present a visualization of the results that demonstrate both the performance of the algorithm as well as the difficulty of the task at hand.

  1. SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

    Energy Technology Data Exchange (ETDEWEB)

    Verburg, E; Waard, SN de; Veldhuis, WB; Gils, CH van; Gilhuijs, KGA [University Medical Center Utrecht, Utrecht (Netherlands)

    2016-06-15

    Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.

  2. SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

    International Nuclear Information System (INIS)

    Verburg, E; Waard, SN de; Veldhuis, WB; Gils, CH van; Gilhuijs, KGA

    2016-01-01

    Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.

  3. Segmented Thermoelectric Oxide-based Module

    DEFF Research Database (Denmark)

    Le, Thanh Hung; Linderoth, Søren

    for a more stable high temperature material. In this study, thermoelectric properties from 300 to 1200 K of Ca0.9Y0.1Mn1-xFexO3 for 0 ≤ x ≤ 0.25 were systematically investigated in term of Y and Fe co-doping at the Ca- and Mn-sites, respectively. It was found that with increasing the content of Fe doping......-performance segmented oxide-based module comprising of 4-unicouples using segmentation of the half-Heusler Ti0.3Zr0.35Hf0.35CoSb0.8Sn0.2 and the misfit-layered cobaltite Ca3Co4O9+δ as the p-leg and 2% Al-doped ZnO as the n-leg was successfully fabricated and characterized. The results (presented in Chapter 5) show...... result, although a slight degradation tendency could be observed after 48 hours of operating in air. Nevertheless, the total conversion efficiency of this segmented module is still low less than 2%, and needs to be further improved. A degradation mechanism was observed, which attributed to the increase...

  4. Muscles of mastication model-based MR image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Ng, H.P. [NUS Graduate School for Integrative Sciences and Engineering, Singapore (Singapore); Agency for Science Technology and Research, Singapore (Singapore). Biomedical Imaging Lab.; Ong, S.H. [National Univ. of Singapore (Singapore). Dept. of Electrical and Computer Engineering; National Univ. of Singapore (Singapore). Div. of Bioengineering; Hu, Q.; Nowinski, W.L. [Agency for Science Technology and Research, Singapore (Singapore). Biomedical Imaging Lab.; Foong, K.W.C. [NUS Graduate School for Integrative Sciences and Engineering, Singapore (Singapore); National Univ. of Singapore (Singapore). Dept. of Preventive Dentistry; Goh, P.S. [National Univ. of Singapore (Singapore). Dept. of Diagnostic Radiology

    2006-11-15

    Objective: The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods: Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results: The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (K) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion: A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations. (orig.)

  5. Muscles of mastication model-based MR image segmentation

    International Nuclear Information System (INIS)

    Ng, H.P.; Agency for Science Technology and Research, Singapore; Ong, S.H.; National Univ. of Singapore; Hu, Q.; Nowinski, W.L.; Foong, K.W.C.; National Univ. of Singapore; Goh, P.S.

    2006-01-01

    Objective: The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods: Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results: The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (K) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion: A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations. (orig.)

  6. Segment-based Eyring-Wilson viscosity model for polymer solutions

    International Nuclear Information System (INIS)

    Sadeghi, Rahmat

    2005-01-01

    A theory-based model is presented for correlating viscosity of polymer solutions and is based on the segment-based Eyring mixture viscosity model as well as the segment-based Wilson model for describing deviations from ideality. The model has been applied to several polymer solutions and the results show that it is reliable both for correlation and prediction of the viscosity of polymer solutions at different molar masses and temperature of the polymer

  7. Skeleton-based Hierarchical Shape Segmentation

    NARCIS (Netherlands)

    Reniers, Dennie; Telea, Alexandru

    2007-01-01

    We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input. We use these points

  8. A Unified 3D Mesh Segmentation Framework Based on Markov Random Field

    OpenAIRE

    Z.F. Shi; L.Y. Lu; D. Le; X.M. Niu

    2012-01-01

    3D Mesh segmentation has become an important research field in computer graphics during the past decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. In this paper, we present a definition of mesh segmentation according to labeling problem. Inspired by the Markov Random Field (MRF) based image segmentation, we propose a new framework of 3D mesh segmentation based on MRF and use graph cuts to solve it. Any features of 3D mesh can be integra...

  9. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans

    Energy Technology Data Exchange (ETDEWEB)

    Okada, Kazunori, E-mail: kazokada@sfsu.edu [Department of Computer Science, San Francisco State University, San Francisco, California 94132 (United States); Rysavy, Steven [Biomedical and Health Informatics Program, University of Washington, Seattle, Washington 98195 (United States); Flores, Arturo [Computer Science and Engineering, University of California, San Diego, California 92093 (United States); Linguraru, Marius George [Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, Washington, DC 20010 and Departments of Radiology and Pediatrics, George Washington University, Washington, DC 20037 (United States)

    2015-04-15

    Purpose: This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions. Methods: The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost. Results: A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon’s state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts. Conclusions: Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon’s conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.

  10. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans

    International Nuclear Information System (INIS)

    Okada, Kazunori; Rysavy, Steven; Flores, Arturo; Linguraru, Marius George

    2015-01-01

    Purpose: This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions. Methods: The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost. Results: A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon’s state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts. Conclusions: Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon’s conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions

  11. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans.

    Science.gov (United States)

    Okada, Kazunori; Rysavy, Steven; Flores, Arturo; Linguraru, Marius George

    2015-04-01

    This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions. The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost. A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon's state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts. Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon's conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.

  12. SEGMENTATION AND CLASSIFICATION OF CERVICAL CYTOLOGY IMAGES USING MORPHOLOGICAL AND STATISTICAL OPERATIONS

    Directory of Open Access Journals (Sweden)

    S Anantha Sivaprakasam

    2017-02-01

    Full Text Available Cervical cancer that is a disease, in which malignant (cancer cells form in the tissues of the cervix, is one of the fourth leading causes of cancer death in female community worldwide. The cervical cancer can be prevented and/or cured if it is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is called Papanicolaou test or Pap test which is used to detect the abnormality of the cell. Due to intricacy of the cell nature, automating of this procedure is still a herculean task for the pathologist. This paper addresses solution for the challenges in terms of a simple and novel method to segment and classify the cervical cell automatically. The primary step of this procedure is pre-processing in which de-nosing, de-correlation operation and segregation of colour components are carried out, Then, two new techniques called Morphological and Statistical Edge based segmentation and Morphological and Statistical Region Based segmentation Techniques- put forward in this paper, and that are applied on the each component of image to segment the nuclei from cervical image. Finally, all segmented colour components are combined together to make a final segmentation result. After extracting the nuclei, the morphological features are extracted from the nuclei. The performance of two techniques mentioned above outperformed than standard segmentation techniques. Besides, Morphological and Statistical Edge based segmentation is outperformed than Morphological and Statistical Region based Segmentation. Finally, the nuclei are classified based on the morphological value The segmentation accuracy is echoed in classification accuracy. The overall segmentation accuracy is 97%.

  13. Stroke subtype classification by geometrical descriptors of lesion shape.

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    Bastian Cheng

    Full Text Available Inference of etiology from lesion pattern in acute magnetic resonance imaging is valuable for management and prognosis of acute stroke patients. This study aims to assess the value of three-dimensional geometrical lesion-shape descriptors for stroke-subtype classification, specifically regarding stroke of cardioembolic origin.Stroke Etiology was classified according to ASCOD in retrospectively selected patients with acute stroke. Lesions were segmented on diffusion-weighed datasets, and descriptors of lesion shape quantified: surface area, sphericity, bounding box volume, and ratio between bounding box and lesion volume. Morphological measures were compared between stroke subtypes classified by ASCOD and between patients with embolic stroke of cardiac and non-cardiac source.150 patients (mean age 77 years; 95% CI, 65-80 years; median NIHSS 6, range 0-22 were included. Group comparison of lesion shape measures demonstrated that lesions caused by small-vessel disease were smaller and more spherical compared to other stroke subtypes. No significant differences of morphological measures were detected between patients with cardioembolic and non-cardioembolic stroke.Stroke lesions caused by small vessel disease can be distinguished from other stroke lesions based on distinctive morphological properties. However, within the group of embolic strokes, etiology could not be inferred from the morphology measures studied in our analysis.

  14. Can the Diagnostics of Triangular Fibrocartilage Complex Lesions Be Improved by MRI-Based Soft-Tissue Reconstruction? An Imaging-Based Workup and Case Presentation

    Directory of Open Access Journals (Sweden)

    Niels Hammer

    2017-01-01

    Full Text Available Introduction. The triangular fibrocartilage complex (TFCC provides both mobility and stability of the radiocarpal joint. TFCC lesions are difficult to diagnose due to the complex anatomy. The standard treatment for TFCC lesions is arthroscopy, posing surgery-related risks onto the patients. This feasibility study aimed at developing a workup for soft-tissue reconstruction using clinical imaging, to verify these results in retrospective patient data. Methods. Microcomputed tomography (μ-CT, 3 T magnetic resonance imaging (MRI, and plastination were used to visualize the TFCC in cadaveric specimens applying segmentation-based 3D reconstruction. This approach further trialed the MRI dataset of a patient with minor radiological TFCC alterations but persistent pain. Results. TFCC reconstruction was impossible using μ-CT only but feasible using MRI, resulting in an appreciation of its substructures, as seen in the plastinates. Applying this approach allowed for visualizing a Palmer 2C lesion in a patient, confirming ex postum the arthroscopy findings, being markedly different from MRI (Palmer 1B. Discussion. This preliminary study showed that image-based TFCC reconstruction may help to identify pathologies invisible in standard MRI. The combined approach of μ-CT, MRI, and plastination allowed for a three-dimensional appreciation of the TFCC. Image quality and time expenditure limit the approach’s usefulness as a diagnostic tool.

  15. Automated measurement of local white matter lesion volume

    DEFF Research Database (Denmark)

    van der Lijn, Fedde; Verhaaren, Benjamin F. J.; Ikram, M. Arfan

    2012-01-01

    in a periventricular region close to the ventricles and a subcortical zone further away. In this work we present a novel automated method for local white matter lesion volume quantification in magnetic resonance images. The method segments and measures the white matter lesion volume in 43 regions defined...

  16. An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

    Directory of Open Access Journals (Sweden)

    Ningning Zhou

    2014-01-01

    Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

  17. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model

    International Nuclear Information System (INIS)

    Montgomery, David W. G.; Amira, Abbes; Zaidi, Habib

    2007-01-01

    The widespread application of positron emission tomography (PET) in clinical oncology has driven this imaging technology into a number of new research and clinical arenas. Increasing numbers of patient scans have led to an urgent need for efficient data handling and the development of new image analysis techniques to aid clinicians in the diagnosis of disease and planning of treatment. Automatic quantitative assessment of metabolic PET data is attractive and will certainly revolutionize the practice of functional imaging since it can lower variability across institutions and may enhance the consistency of image interpretation independent of reader experience. In this paper, a novel automated system for the segmentation of oncological PET data aiming at providing an accurate quantitative analysis tool is proposed. The initial step involves expectation maximization (EM)-based mixture modeling using a k-means clustering procedure, which varies voxel order for initialization. A multiscale Markov model is then used to refine this segmentation by modeling spatial correlations between neighboring image voxels. An experimental study using an anthropomorphic thorax phantom was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The comparison of actual tumor volumes to the volumes calculated using different segmentation methodologies including standard k-means, spatial domain Markov Random Field Model (MRFM), and the new multiscale MRFM proposed in this paper showed that the latter dramatically reduces the relative error to less than 8% for small lesions (7 mm radii) and less than 3.5% for larger lesions (9 mm radii). The analysis of the resulting segmentations of clinical oncologic PET data seems to confirm that this methodology shows promise and can successfully segment patient lesions. For problematic images, this technique enables the identification of tumors situated very close to nearby high normal physiologic uptake. The

  18. An improved segmentation-based HMM learning method for Condition-based Maintenance

    International Nuclear Information System (INIS)

    Liu, T; Lemeire, J; Cartella, F; Meganck, S

    2012-01-01

    In the domain of condition-based maintenance (CBM), persistence of machine states is a valid assumption. Based on this assumption, we present an improved Hidden Markov Model (HMM) learning algorithm for the assessment of equipment states. By a good estimation of initial parameters, more accurate learning can be achieved than by regular HMM learning methods which start with randomly chosen initial parameters. It is also better in avoiding getting trapped in local maxima. The data is segmented with a change-point analysis method which uses a combination of cumulative sum charts (CUSUM) and bootstrapping techniques. The method determines a confidence level that a state change happens. After the data is segmented, in order to label and combine the segments corresponding to the same states, a clustering technique is used based on a low-pass filter or root mean square (RMS) values of the features. The segments with their labelled hidden state are taken as 'evidence' to estimate the parameters of an HMM. Then, the estimated parameters are served as initial parameters for the traditional Baum-Welch (BW) learning algorithms, which are used to improve the parameters and train the model. Experiments on simulated and real data demonstrate that both performance and convergence speed is improved.

  19. Adjustable Two-Tier Cache for IPTV Based on Segmented Streaming

    Directory of Open Access Journals (Sweden)

    Kai-Chun Liang

    2012-01-01

    Full Text Available Internet protocol TV (IPTV is a promising Internet killer application, which integrates video, voice, and data onto a single IP network, and offers viewers an innovative set of choices and control over their TV content. To provide high-quality IPTV services, an effective strategy is based on caching. This work proposes a segment-based two-tier caching approach, which divides each video into multiple segments to be cached. This approach also partitions the cache space into two layers, where the first layer mainly caches to-be-played segments and the second layer saves possibly played segments. As the segment access becomes frequent, the proposed approach enlarges the first layer and reduces the second layer, and vice versa. Because requested segments may not be accessed frequently, this work further designs an admission control mechanism to determine whether an incoming segment should be cached or not. The cache architecture takes forward/stop playback into account and may replace the unused segments under the interrupted playback. Finally, we conduct comprehensive simulation experiments to evaluate the performance of the proposed approach. The results show that our approach can yield higher hit ratio than previous work under various environmental parameters.

  20. Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

    Science.gov (United States)

    Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

    2018-01-01

    Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

  1. Deep learning and texture-based semantic label fusion for brain tumor segmentation

    Science.gov (United States)

    Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

    2018-02-01

    Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

  2. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET

    Energy Technology Data Exchange (ETDEWEB)

    Hatt, M [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Lamare, F [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609, (France); Boussion, N [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Turzo, A [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Collet, C [Ecole Nationale Superieure de Physique de Strasbourg (ENSPS), ULP, Strasbourg, F-67000 (France); Salzenstein, F [Institut d' Electronique du Solide et des Systemes (InESS), ULP, Strasbourg, F-67000 (France); Roux, C [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Jarritt, P [Medical Physics Agency, Royal Victoria Hospital, Belfast (United Kingdom); Carson, K [Medical Physics Agency, Royal Victoria Hospital, Belfast (United Kingdom); Rest, C Cheze-Le [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Visvikis, D [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France)

    2007-07-21

    Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm{sup 3} and 64 mm{sup 3}). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The

  3. FUSION SEGMENTATION METHOD BASED ON FUZZY THEORY FOR COLOR IMAGES

    Directory of Open Access Journals (Sweden)

    J. Zhao

    2017-09-01

    Full Text Available The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefore, a fusion segmentation method based on fuzzy theory is proposed in this paper. We use membership function to model the uncertainties on each color channel of the color image. Then, we segment the color image according to the fuzzy reasoning. The experiment results show that our proposed method can get better segmentation results both on the natural scene images and optical remote sensing images compared with the traditional thresholding method. The fusion method in this paper can provide new ideas for the information extraction of optical remote sensing images and polarization SAR images.

  4. Research on segmentation based on multi-atlas in brain MR image

    Science.gov (United States)

    Qian, Yuejing

    2018-03-01

    Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

  5. Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

    Directory of Open Access Journals (Sweden)

    Matthew Parkan

    2018-02-01

    Full Text Available Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys, using a probabilistic approach. First, a coarse segmentation (marker controlled watershed is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes. By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered segmentation.

  6. Graph-based surface reconstruction from stereo pairs using image segmentation

    Science.gov (United States)

    Bleyer, Michael; Gelautz, Margrit

    2005-01-01

    This paper describes a novel stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. The use of segmentation makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function via a robust optimization technique that employs graph cuts. The cost function is defined on the pixel level, as well as on the segment level. While the pixel level measures the data similarity based on the current disparity map and detects occlusions symmetrically in both views, the segment level propagates the segmentation information and incorporates a smoothness term. New planar models are then generated based on the disparity layers' spatial extents. Results obtained for benchmark and self-recorded image pairs indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.

  7. An LG-graph-based early evaluation of segmented images

    International Nuclear Information System (INIS)

    Tsitsoulis, Athanasios; Bourbakis, Nikolaos

    2012-01-01

    Image segmentation is one of the first important parts of image analysis and understanding. Evaluation of image segmentation, however, is a very difficult task, mainly because it requires human intervention and interpretation. In this work, we propose a blind reference evaluation scheme based on regional local–global (RLG) graphs, which aims at measuring the amount and distribution of detail in images produced by segmentation algorithms. The main idea derives from the field of image understanding, where image segmentation is often used as a tool for scene interpretation and object recognition. Evaluation here derives from summarization of the structural information content and not from the assessment of performance after comparisons with a golden standard. Results show measurements for segmented images acquired from three segmentation algorithms, applied on different types of images (human faces/bodies, natural environments and structures (buildings)). (paper)

  8. Texture alteration detection in bitemporal images of lesions with psoriasis

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    The objective of this work is to explore the feasibility of quantifying textural change between pairs of segmented patterns without registering them. The Multi-variate Alteration Detection (M.A.D.) Transform is applied to a texture model constructed with the data of segmented psoriasis lesions im...... images. The texture model is Haralick's co-occurrence matrix, which is computed and normalized for each single band with the equalized data of a given lesion. The contribution of each single color band to the textural change is analyzed....

  9. Random forest learning of ultrasonic statistical physics and object spaces for lesion detection in 2D sonomammography

    Science.gov (United States)

    Sheet, Debdoot; Karamalis, Athanasios; Kraft, Silvan; Noël, Peter B.; Vag, Tibor; Sadhu, Anup; Katouzian, Amin; Navab, Nassir; Chatterjee, Jyotirmoy; Ray, Ajoy K.

    2013-03-01

    Breast cancer is the most common form of cancer in women. Early diagnosis can significantly improve lifeexpectancy and allow different treatment options. Clinicians favor 2D ultrasonography for breast tissue abnormality screening due to high sensitivity and specificity compared to competing technologies. However, inter- and intra-observer variability in visual assessment and reporting of lesions often handicaps its performance. Existing Computer Assisted Diagnosis (CAD) systems though being able to detect solid lesions are often restricted in performance. These restrictions are inability to (1) detect lesion of multiple sizes and shapes, and (2) differentiate between hypo-echoic lesions from their posterior acoustic shadowing. In this work we present a completely automatic system for detection and segmentation of breast lesions in 2D ultrasound images. We employ random forests for learning of tissue specific primal to discriminate breast lesions from surrounding normal tissues. This enables it to detect lesions of multiple shapes and sizes, as well as discriminate between hypo-echoic lesion from associated posterior acoustic shadowing. The primal comprises of (i) multiscale estimated ultrasonic statistical physics and (ii) scale-space characteristics. The random forest learns lesion vs. background primal from a database of 2D ultrasound images with labeled lesions. For segmentation, the posterior probabilities of lesion pixels estimated by the learnt random forest are hard thresholded to provide a random walks segmentation stage with starting seeds. Our method achieves detection with 99.19% accuracy and segmentation with mean contour-to-contour error < 3 pixels on a set of 40 images with 49 lesions.

  10. CT-based manual segmentation and evaluation of paranasal sinuses.

    Science.gov (United States)

    Pirner, S; Tingelhoff, K; Wagner, I; Westphal, R; Rilk, M; Wahl, F M; Bootz, F; Eichhorn, Klaus W G

    2009-04-01

    Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.

  11. An automatic system to discriminate malignant from benign massive lesions on mammograms

    International Nuclear Information System (INIS)

    Retico, A.; Delogu, P.; Fantacci, M.E.; Kasae, P.

    2006-01-01

    Mammography is widely recognized as the most reliable technique for early detection of breast cancers. Automated or semi-automated computerized classification schemes can be very useful in assisting radiologists with a second opinion about the visual diagnosis of breast lesions, thus leading to a reduction in the number of unnecessary biopsies. We present a computer-aided diagnosis (CADi) system for the characterization of massive lesions in mammograms, whose aim is to distinguish malignant from benign masses. The CADi system we realized is based on a three-stage algorithm: (a) a segmentation technique extracts the contours of the massive lesion from the image; (b) 16 features based on size and shape of the lesion are computed; (c) a neural classifier merges the features into an estimated likelihood of malignancy. A data set of 226 massive lesions (109 malignant and 117 benign) has been used in this study. The system performances have been evaluated in terms of the receiver-operating characteristic (ROC) analysis, obtaining A z =0.80+/-0.04 as the estimated area under the ROC curve

  12. Model-based segmentation and classification of trajectories (Extended abstract)

    NARCIS (Netherlands)

    Alewijnse, S.P.A.; Buchin, K.; Buchin, M.; Sijben, S.; Westenberg, M.A.

    2014-01-01

    We present efficient algorithms for segmenting and classifying a trajectory based on a parameterized movement model like the Brownian bridge movement model. Segmentation is the problem of subdividing a trajectory into parts such that each art is homogeneous in its movement characteristics. We

  13. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

    Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

  14. Accurate determination of high-risk coronary lesion type by multidetector cardiac computed tomography.

    Science.gov (United States)

    Alasnag, Mirvat; Umakanthan, Branavan; Foster, Gary P

    2008-07-01

    Coronary arteriography (CA) is the standard method to image coronary lesions. Multidetector cardiac computerized tomography (MDCT) provides high-resolution images of coronary arteries, allowing a noninvasive alternative to determine lesion type. To date, no studies have assessed the ability of MDCT to categorize coronary lesion types. The objective of this study was to determine the accuracy of lesion type categorization by MDCT using CA as a reference standard. Patients who underwent both MDCT and CA within 2 months of each other were enrolled. MDCT and CA images were reviewed in a blinded fashion. Lesions were categorized according to the SCAI classification system (Types I-IV). The origin, proximal and middle segments of the major arteries were analyzed. Each segment comprised a data point for comparison. Analysis was performed using the Spearman Correlation Test. Four hundred eleven segments were studied, of which 110 had lesions. The lesion distribution was as follows: 35 left anterior descending (LAD), 29 circumflex (Cx), 31 right coronary artery (RCA), 2 ramus intermedius, 8 diagonal, 4 obtuse marginal and 2 left internal mammary arteries. Correlations between MDCT and CA were significant in all major vessels (LAD, Cx, RCA) (p < 0.001). The overall correlation coefficient was 0.67. Concordance was strong for lesion Types II-IV (97%) and poor for Type I (30%). High-risk coronary lesion types can be accurately categorized by MDCT. This ability may allow MDCT to play an important noninvasive role in the planning of coronary interventions.

  15. Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening.

    Science.gov (United States)

    Kwak, Kichang; Yoon, Uicheul; Lee, Dong-Kyun; Kim, Geon Ha; Seo, Sang Won; Na, Duk L; Shim, Hack-Joon; Lee, Jong-Min

    2013-09-01

    The hippocampus has been known to be an important structure as a biomarker for Alzheimer's disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index=0.81±0.03) than the conventional atlas-based segmentation method (0.72±0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision=0.76±0.04, recall=0.86±0.05) produced lower ratios than the conventional methods (0.73±0.05, 0.72±0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Science.gov (United States)

    2018-01-01

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved. PMID:29439500

  17. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Directory of Open Access Journals (Sweden)

    Yuexiang Li

    2018-02-01

    Full Text Available Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1, lesion dermoscopic feature extraction (task 2 and lesion classification (task 3. A deep learning framework consisting of two fully convolutional residual networks (FCRN is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  18. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

    Science.gov (United States)

    Li, Yuexiang; Shen, Linlin

    2018-02-11

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  19. Correction of quantification errors in pelvic and spinal lesions caused by ignoring higher photon attenuation of bone in [{sup 18}F]NaF PET/MR

    Energy Technology Data Exchange (ETDEWEB)

    Schramm, Georg, E-mail: georg.schramm@kuleuven.be; Maus, Jens; Hofheinz, Frank; Petr, Jan; Lougovski, Alexandr [Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden 01328 (Germany); Beuthien-Baumann, Bettina; Oehme, Liane [Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Dresden 01307 (Germany); Platzek, Ivan [Department of Radiology, University Hospital Carl Gustav Carus, Dresden 01307 (Germany); Hoff, Jörg van den [Helmholtz-Zentrum Dresden-Rossendorf, Institute for Radiopharmaceutical Cancer Research, Dresden 01328 (Germany); Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Dresden 01307 (Germany)

    2015-11-15

    Purpose: MR-based attenuation correction (MRAC) in routine clinical whole-body positron emission tomography and magnetic resonance imaging (PET/MRI) is based on tissue type segmentation. Due to lack of MR signal in cortical bone and the varying signal of spongeous bone, standard whole-body segmentation-based MRAC ignores the higher attenuation of bone compared to the one of soft tissue (MRAC{sub nobone}). The authors aim to quantify and reduce the bias introduced by MRAC{sub nobone} in the standard uptake value (SUV) of spinal and pelvic lesions in 20 PET/MRI examinations with [{sup 18}F]NaF. Methods: The authors reconstructed 20 PET/MR [{sup 18}F]NaF patient data sets acquired with a Philips Ingenuity TF PET/MRI. The PET raw data were reconstructed with two different attenuation images. First, the authors used the vendor-provided MRAC algorithm that ignores the higher attenuation of bone to reconstruct PET{sub nobone}. Second, the authors used a threshold-based algorithm developed in their group to automatically segment bone structures in the [{sup 18}F]NaF PET images. Subsequently, an attenuation coefficient of 0.11 cm{sup −1} was assigned to the segmented bone regions in the MRI-based attenuation image (MRAC{sub bone}) which was used to reconstruct PET{sub bone}. The automatic bone segmentation algorithm was validated in six PET/CT [{sup 18}F]NaF examinations. Relative SUV{sub mean} and SUV{sub max} differences between PET{sub bone} and PET{sub nobone} of 8 pelvic and 41 spinal lesions, and of other regions such as lung, liver, and bladder, were calculated. By varying the assigned bone attenuation coefficient from 0.11 to 0.13 cm{sup −1}, the authors investigated its influence on the reconstructed SUVs of the lesions. Results: The comparison of [{sup 18}F]NaF-based and CT-based bone segmentation in the six PET/CT patients showed a Dice similarity of 0.7 with a true positive rate of 0.72 and a false discovery rate of 0.33. The [{sup 18}F]NaF-based bone

  20. Workflow-centred evaluation of an automatic lesion tracking software for chemotherapy monitoring by CT

    Energy Technology Data Exchange (ETDEWEB)

    Moltz, Jan Hendrik; Peitgen, Heinz-Otto [Fraunhofer MEVIS - Institute for Medical Image Computing, Bremen (Germany); D' Anastasi, Melvin [University Hospital Munich-Grosshadern, Department of Clinical Radiology, Muenchen (Germany); Kiessling, Andreas [University Hospital Giessen and Marburg, Department of Diagnostic Radiology, Marburg (Germany); Pinto dos Santos, Daniel [University Hospital Mainz, Department of Diagnostic and Interventional Radiology, Mainz (Germany); Schuelke, Christoph [University Hospital Muenster, Institute of Clinical Radiology, Muenster (Germany)

    2012-12-15

    In chemotherapy monitoring, an estimation of the change in tumour size is an important criterion for the assessment of treatment success. This requires a comparison between corresponding lesions in the baseline and follow-up computed tomography (CT) examinations. We evaluate the clinical benefits of an automatic lesion tracking tool that identifies the target lesions in the follow-up CT study and pre-computes the lesion volumes. Four radiologists performed volumetric follow-up examinations for 52 patients with and without lesion tracking. In total, 139 lung nodules, liver metastases and lymph nodes were given as target lesions. We measured reading time, inter-reader variability in lesion identification and volume measurements, and the amount of manual adjustments of the segmentation results. With lesion tracking, target lesion assessment time decreased by 38 % or 22 s per lesion. Relative volume difference between readers was reduced from 0.171 to 0.1. Segmentation quality was comparable with and without lesion tracking. Our automatic lesion tracking tool can make interpretation of follow-up CT examinations quicker and provide results that are less reader-dependent. (orig.)

  1. Segmentation of Handwritten Chinese Character Strings Based on improved Algorithm Liu

    Directory of Open Access Journals (Sweden)

    Zhihua Cai

    2014-09-01

    Full Text Available Algorithm Liu attracts high attention because of its high accuracy in segmentation of Japanese postal address. But the disadvantages, such as complexity and difficult implementation of algorithm, etc. have an adverse effect on its popularization and application. In this paper, the author applies the principles of algorithm Liu to handwritten Chinese character segmentation according to the characteristics of the handwritten Chinese characters, based on deeply study on algorithm Liu.In the same time, the author put forward the judgment criterion of Segmentation block classification and adhering mode of the handwritten Chinese characters.In the process of segmentation, text images are seen as the sequence made up of Connected Components (CCs, while the connected components are made up of several horizontal itinerary set of black pixels in image. The author determines whether these parts will be merged into segmentation through analyzing connected components. And then the author does image segmentation through adhering mode based on the analysis of outline edges. Finally cut the text images into character segmentation. Experimental results show that the improved Algorithm Liu obtains high segmentation accuracy and produces a satisfactory segmentation result.

  2. Multi-atlas Based Segmentation Editing with Interaction-Guided Constraints

    OpenAIRE

    Park, Sang Hyun; Gao, Yaozong; Shen, Dinggang

    2015-01-01

    We propose a novel multi-atlas based segmentation method to address the editing scenario, when given an incomplete segmentation along with a set of training label images. Unlike previous multi-atlas based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate training labels and derive their voting weights. Specifically, we divide user interactions, provided on erroneous parts, into multiple local interaction combinations, and th...

  3. Contextual segment-based classification of airborne laser scanner data

    NARCIS (Netherlands)

    Vosselman, George; Coenen, Maximilian; Rottensteiner, Franz

    2017-01-01

    Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset

  4. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    Science.gov (United States)

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  5. Strategy-Based Segmentation of Industrial Markets

    NARCIS (Netherlands)

    Verhallen, Theo M.M.; Frambach, Ruud T.; Prabhu, Jaideep

    Segmentation of industrial markets is typically based on observable characteristics of firms such as their location and size. However, such variables have been found to be poor predictors of industrial buying behavior. To improve the effectiveness and power of existing approaches to industrial

  6. Image segmentation-based robust feature extraction for color image watermarking

    Science.gov (United States)

    Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

    2018-04-01

    This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

  7. Fuzzy clustering-based segmented attenuation correction in whole-body PET

    CERN Document Server

    Zaidi, H; Boudraa, A; Slosman, DO

    2001-01-01

    Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

  8. Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT

    Science.gov (United States)

    Wang, Xixi; Nagarajan, Mahesh B.; Conover, David; Ning, Ruola; O'Connell, Avice; Wismueller, Axel

    2014-04-01

    Cone beam computed tomography (CBCT) has found use in mammography for imaging the entire breast with sufficient spatial resolution at a radiation dose within the range of that of conventional mammography. Recently, enhancement of lesion tissue through the use of contrast agents has been proposed for cone beam CT. This study investigates whether the use of such contrast agents improves the ability of texture features to differentiate lesion texture from healthy tissue on CBCT in an automated manner. For this purpose, 9 lesions were annotated by an experienced radiologist on both regular and contrast-enhanced CBCT images using two-dimensional (2D) square ROIs. These lesions were then segmented, and each pixel within the lesion ROI was assigned a label - lesion or non-lesion, based on the segmentation mask. On both sets of CBCT images, four three-dimensional (3D) Minkowski Functionals were used to characterize the local topology at each pixel. The resulting feature vectors were then used in a machine learning task involving support vector regression with a linear kernel (SVRlin) to classify each pixel as belonging to the lesion or non-lesion region of the ROI. Classification performance was assessed using the area under the receiver-operating characteristic (ROC) curve (AUC). Minkowski Functionals derived from contrastenhanced CBCT images were found to exhibit significantly better performance at distinguishing between lesion and non-lesion areas within the ROI when compared to those extracted from CBCT images without contrast enhancement (p < 0.05). Thus, contrast enhancement in CBCT can improve the ability of texture features to distinguish lesions from surrounding healthy tissue.

  9. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

    Science.gov (United States)

    Premaladha, J; Ravichandran, K S

    2016-04-01

    Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

  10. Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation.

    Science.gov (United States)

    Kang, Sung-Won; Lee, Woo-Jin; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Kim, Tae-Il; Yi, Won-Jin

    2015-03-01

    We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (pimplants using micro-CT analysis using a region-based segmentation method.

  11. IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

    Institute of Scientific and Technical Information of China (English)

    纳瑟; 刘重庆

    2002-01-01

    This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K-means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.

  12. Effects of neck and circumoesophageal connective lesions on posture and locomotion in the cockroach.

    Science.gov (United States)

    Ridgel, Angela L; Ritzmann, Roy E

    2005-06-01

    Few studies in arthropods have documented to what extent local control centers in the thorax can support locomotion in absence of inputs from head ganglia. Posture, walking, and leg motor activity was examined in cockroaches with lesions of neck or circumoesophageal connectives. Early in recovery, cockroaches with neck lesions had hyper-extended postures and did not walk. After recovery, posture was less hyper-extended and animals initiated slow leg movements for multiple cycles. Neck lesioned individuals showed an increase in walking after injection of either octopamine or pilocarpine. The phase of leg movement between segments was reduced in neck lesioned cockroaches from that seen in intact animals, while phases in the same segment remained constant. Neither octopamine nor pilocarpine initiated changes in coordination between segments in neck lesioned individuals. Animals with lesions of the circumoesophageal connectives had postures similar to intact individuals but walked in a tripod gait for extended periods of time. Changes in activity of slow tibial extensor and coxal depressor motor neurons and concomitant changes in leg joint angles were present after the lesions. This suggests that thoracic circuits are sufficient to produce leg movements but coordinated walking with normal motor patterns requires descending input from head ganglia.

  13. A Modelling Framework for estimating Road Segment Based On-Board Vehicle Emissions

    International Nuclear Information System (INIS)

    Lin-Jun, Yu; Ya-Lan, Liu; Yu-Huan, Ren; Zhong-Ren, Peng; Meng, Liu Meng

    2014-01-01

    Traditional traffic emission inventory models aim to provide overall emissions at regional level which cannot meet planners' demand for detailed and accurate traffic emissions information at the road segment level. Therefore, a road segment-based emission model for estimating light duty vehicle emissions is proposed, where floating car technology is used to collect information of traffic condition of roads. The employed analysis framework consists of three major modules: the Average Speed and the Average Acceleration Module (ASAAM), the Traffic Flow Estimation Module (TFEM) and the Traffic Emission Module (TEM). The ASAAM is used to obtain the average speed and the average acceleration of the fleet on each road segment using FCD. The TFEM is designed to estimate the traffic flow of each road segment in a given period, based on the speed-flow relationship and traffic flow spatial distribution. Finally, the TEM estimates emissions from each road segment, based on the results of previous two modules. Hourly on-road light-duty vehicle emissions for each road segment in Shenzhen's traffic network are obtained using this analysis framework. The temporal-spatial distribution patterns of the pollutant emissions of road segments are also summarized. The results show high emission road segments cluster in several important regions in Shenzhen. Also, road segments emit more emissions during rush hours than other periods. The presented case study demonstrates that the proposed approach is feasible and easy-to-use to help planners make informed decisions by providing detailed road segment-based emission information

  14. A quantification strategy for missing bone mass in case of osteolytic bone lesions

    International Nuclear Information System (INIS)

    Fränzle, Andrea; Giske, Kristina; Bretschi, Maren; Bäuerle, Tobias; Hillengass, Jens; Bendl, Rolf

    2013-01-01

    Purpose: Most of the patients who died of breast cancer have developed bone metastases. To understand the pathogenesis of bone metastases and to analyze treatment response of different bone remodeling therapies, preclinical animal models are examined. In breast cancer, bone metastases are often bone destructive. To assess treatment response of bone remodeling therapies, the volumes of these lesions have to be determined during the therapy process. The manual delineation of missing structures, especially if large parts are missing, is very time-consuming and not reproducible. Reproducibility is highly important to have comparable results during the therapy process. Therefore, a computerized approach is needed. Also for the preclinical research, a reproducible measurement of the lesions is essential. Here, the authors present an automated segmentation method for the measurement of missing bone mass in a preclinical rat model with bone metastases in the hind leg bones based on 3D CT scans. Methods: The affected bone structure is compared to a healthy model. Since in this preclinical rat trial the metastasis only occurs on the right hind legs, which is assured by using vessel clips, the authors use the left body side as a healthy model. The left femur is segmented with a statistical shape model which is initialised using the automatically segmented medullary cavity. The left tibia and fibula are segmented using volume growing starting at the tibia medullary cavity and stopping at the femur boundary. Masked images of both segmentations are mirrored along the median plane and transferred manually to the position of the affected bone by rigid registration. Affected bone and healthy model are compared based on their gray values. If the gray value of a voxel indicates bone mass in the healthy model and no bone in the affected bone, this voxel is considered to be osteolytic. Results: The lesion segmentations complete the missing bone structures in a reasonable way. The mean

  15. Epidermal hydrogen peroxide is not increased in lesional and non-lesional skin of vitiligo.

    Science.gov (United States)

    Zailaie, Mohammad Z

    2017-01-01

    It is widely believed that the loss of the epidermal melanocytes in vitiligo is basically due to excessive oxidative stress. Previous research work described abnormal elevation of the absolute concentration of the epidermal hydrogen peroxide (H 2 O 2 ) in lesional and non-lesional skin of vitiligo. Based on this finding, our primary research objective was to use this feature as a screening marker in individuals at a great risk of developing vitiligo. Ninety-six patients of non-segmental vitiligo (NSV) of varying durations, skin phototypes, and treatment modalities (psoralen UVA-, narrow band UVB-treated) were recruited for this study. Raman spectroscopic measurements, using an external probehead, of the lesional and non-lesional skin were obtained, and the resulting spectra were analyzed using the Opus software package of the MultiRam spectrometer and the intensity of the peak at 875 cm -1 that represents the absolute concentration of H 2 O 2 was calculated. Contrary to previous reports, in patients of skin phototype IV, the absolute concentrations of H 2 O 2 in non-lesional and lesional NSV of all groups were non-significantly decreased compared to normal control. In patients of NSV of skin phototype V, the decrease in the absolute concentrations of H 2 O 2 was not significant in the untreated group, and a slight non-significant increase in the NBUVB-treated group was noted. However, in the PUVA-treated group, the non-lesional skin demonstrated significant increase in the absolute concentration of H 2 O 2 , whereas the lesional skin showed only a slight non-significant increase compared to normal control. In NSV patients of skin phototype VI who were previously treated with PUVA, the non-lesional skin showed a slight non-significant increase in the absolute concentration of H 2 O 2 ; however, the lesional skin showed a marked significant decrease compared to normal control and the non-lesional skin. Thereof, one can conclude that the epidermal H 2 O 2 is not

  16. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    Science.gov (United States)

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-07-01

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Superiority Of Graph-Based Visual Saliency GVS Over Other Image Segmentation Methods

    Directory of Open Access Journals (Sweden)

    Umu Lamboi

    2017-02-01

    Full Text Available Although inherently tedious the segmentation of images and the evaluation of segmented images are critical in computer vision processes. One of the main challenges in image segmentation evaluation arises from the basic conflict between generality and objectivity. For general segmentation purposes the lack of well-defined ground-truth and segmentation accuracy limits the evaluation of specific applications. Subjectivity is the most common method of evaluation of segmentation quality where segmented images are visually compared. This is daunting task however limits the scope of segmentation evaluation to a few predetermined sets of images. As an alternative supervised evaluation compares segmented images against manually-segmented or pre-processed benchmark images. Not only good evaluation methods allow for different comparisons but also for integration with target recognition systems for adaptive selection of appropriate segmentation granularity with improved recognition accuracy. Most of the current segmentation methods still lack satisfactory measures of effectiveness. Thus this study proposed a supervised framework which uses visual saliency detection to quantitatively evaluate image segmentation quality. The new benchmark evaluator uses Graph-based Visual Saliency GVS to compare boundary outputs for manually segmented images. Using the Berkeley Segmentation Database the proposed algorithm was tested against 4 other quantitative evaluation methods Probabilistic Rand Index PRI Variation of Information VOI Global Consistency Error GSE and Boundary Detection Error BDE. Based on the results the GVS approach outperformed any of the other 4 independent standard methods in terms of visual saliency detection of images.

  18. GPU-based relative fuzzy connectedness image segmentation

    International Nuclear Information System (INIS)

    Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

    2013-01-01

    Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ ∞ -based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  19. GPU-based relative fuzzy connectedness image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W. [Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States); Department of Mathematics, West Virginia University, Morgantown, West Virginia 26506 (United States) and Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States)

    2013-01-15

    Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  20. GPU-based relative fuzzy connectedness image segmentation.

    Science.gov (United States)

    Zhuge, Ying; Ciesielski, Krzysztof C; Udupa, Jayaram K; Miller, Robert W

    2013-01-01

    Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. The most common FC segmentations, optimizing an [script-l](∞)-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  1. GPU-based relative fuzzy connectedness image segmentation

    Science.gov (United States)

    Zhuge, Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

    2013-01-01

    Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ∞-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology. PMID:23298094

  2. Gradient-based reliability maps for ACM-based segmentation of hippocampus.

    Science.gov (United States)

    Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

    2014-04-01

    Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

  3. SeLeCT: a lexical cohesion based news story segmentation system

    OpenAIRE

    Stokes, Nicola; Carthy, Joe; Smeaton, Alan F.

    2004-01-01

    In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between ...

  4. Lesion insertion in the projection domain: Methods and initial results

    International Nuclear Information System (INIS)

    Chen, Baiyu; Leng, Shuai; Yu, Lifeng; Yu, Zhicong; Ma, Chi; McCollough, Cynthia

    2015-01-01

    Purpose: To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way of achieving this objective is to create hybrid images that combine patient images with inserted lesions. Because conventional hybrid images generated in the image domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Methods: Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion. Results: For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically

  5. Lesion insertion in the projection domain: Methods and initial results

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Baiyu; Leng, Shuai; Yu, Lifeng; Yu, Zhicong; Ma, Chi; McCollough, Cynthia, E-mail: mccollough.cynthia@mayo.edu [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)

    2015-12-15

    Purpose: To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way of achieving this objective is to create hybrid images that combine patient images with inserted lesions. Because conventional hybrid images generated in the image domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Methods: Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion. Results: For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically

  6. Lesion insertion in the projection domain: Methods and initial results.

    Science.gov (United States)

    Chen, Baiyu; Leng, Shuai; Yu, Lifeng; Yu, Zhicong; Ma, Chi; McCollough, Cynthia

    2015-12-01

    To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way of achieving this objective is to create hybrid images that combine patient images with inserted lesions. Because conventional hybrid images generated in the image domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion. For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically acquired for the ACR

  7. Automatic blood vessel based-liver segmentation using the portal phase abdominal CT

    Science.gov (United States)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

    2018-02-01

    Liver segmentation is the basis for computer-based planning of hepatic surgical interventions. In diagnosis and analysis of hepatic diseases and surgery planning, automatic segmentation of liver has high importance. Blood vessel (BV) has showed high performance at liver segmentation. In our previous work, we developed a semi-automatic method that segments the liver through the portal phase abdominal CT images in two stages. First stage was interactive segmentation of abdominal blood vessels (ABVs) and subsequent classification into hepatic (HBVs) and non-hepatic (non-HBVs). This stage had 5 interactions that include selective threshold for bone segmentation, selecting two seed points for kidneys segmentation, selection of inferior vena cava (IVC) entrance for starting ABVs segmentation, identification of the portal vein (PV) entrance to the liver and the IVC-exit for classifying HBVs from other ABVs (non-HBVs). Second stage is automatic segmentation of the liver based on segmented ABVs as described in [4]. For full automation of our method we developed a method [5] that segments ABVs automatically tackling the first three interactions. In this paper, we propose full automation of classifying ABVs into HBVs and non- HBVs and consequently full automation of liver segmentation that we proposed in [4]. Results illustrate that the method is effective at segmentation of the liver through the portal abdominal CT images.

  8. Superpixel-based segmentation of glottal area from videolaryngoscopy images

    Science.gov (United States)

    Turkmen, H. Irem; Albayrak, Abdulkadir; Karsligil, M. Elif; Kocak, Ismail

    2017-11-01

    Segmentation of the glottal area with high accuracy is one of the major challenges for the development of systems for computer-aided diagnosis of vocal-fold disorders. We propose a hybrid model combining conventional methods with a superpixel-based segmentation approach. We first employed a superpixel algorithm to reveal the glottal area by eliminating the local variances of pixels caused by bleedings, blood vessels, and light reflections from mucosa. Then, the glottal area was detected by exploiting a seeded region-growing algorithm in a fully automatic manner. The experiments were conducted on videolaryngoscopy images obtained from both patients having pathologic vocal folds as well as healthy subjects. Finally, the proposed hybrid approach was compared with conventional region-growing and active-contour model-based glottal area segmentation algorithms. The performance of the proposed method was evaluated in terms of segmentation accuracy and elapsed time. The F-measure, true negative rate, and dice coefficients of the hybrid method were calculated as 82%, 93%, and 82%, respectively, which are superior to the state-of-art glottal-area segmentation methods. The proposed hybrid model achieved high success rates and robustness, making it suitable for developing a computer-aided diagnosis system that can be used in clinical routines.

  9. Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images.

    Science.gov (United States)

    Jian, Junming; Xiong, Fei; Xia, Wei; Zhang, Rui; Gu, Jinhui; Wu, Xiaodong; Meng, Xiaochun; Gao, Xin

    2018-06-01

    Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator-dependent. In the framework of FCNs, a segmentation method for colorectal tumor was presented. Normalization was applied to reduce the differences among images. Borrowing from transfer learning, VGG-16 was employed to extract features from normalized images. We conducted five side-output blocks from the last convolutional layer of each block of VGG-16 along the network, these side-output blocks can deep dive multiscale features, and produced corresponding predictions. Finally, all of the predictions from side-output blocks were fused to determine the final boundaries of the tumors. A quantitative comparison of 2772 colorectal tumor manual segmentation results from T2-weighted magnetic resonance images shows that the average Dice similarity coefficient, positive predictive value, specificity, sensitivity, Hammoude distance, and Hausdorff distance were 83.56, 82.67, 96.75, 87.85%, 0.2694, and 8.20, respectively. The proposed method is superior to U-net in colorectal tumor segmentation (P colorectal tumor segmentation (P > 0.05). The results indicate that the introduction of FCNs contributed to accurate segmentation of colorectal tumors. This method has the potential to replace the present time-consuming and nonreproducible manual segmentation method.

  10. Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast

    International Nuclear Information System (INIS)

    Bell, L R; Pogson, E M; Metcalfe, P; Holloway, L; Dowling, J A

    2017-01-01

    Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes. (paper)

  11. Intra-temporal facial nerve centerline segmentation for navigated temporal bone surgery

    Science.gov (United States)

    Voormolen, Eduard H. J.; van Stralen, Marijn; Woerdeman, Peter A.; Pluim, Josien P. W.; Noordmans, Herke J.; Regli, Luca; Berkelbach van der Sprenkel, Jan W.; Viergever, Max A.

    2011-03-01

    Approaches through the temporal bone require surgeons to drill away bone to expose a target skull base lesion while evading vital structures contained within it, such as the sigmoid sinus, jugular bulb, and facial nerve. We hypothesize that an augmented neuronavigation system that continuously calculates the distance to these structures and warns if the surgeon drills too close, will aid in making safe surgical approaches. Contemporary image guidance systems are lacking an automated method to segment the inhomogeneous and complexly curved facial nerve. Therefore, we developed a segmentation method to delineate the intra-temporal facial nerve centerline from clinically available temporal bone CT images semi-automatically. Our method requires the user to provide the start- and end-point of the facial nerve in a patient's CT scan, after which it iteratively matches an active appearance model based on the shape and texture of forty facial nerves. Its performance was evaluated on 20 patients by comparison to our gold standard: manually segmented facial nerve centerlines. Our segmentation method delineates facial nerve centerlines with a maximum error along its whole trajectory of 0.40+/-0.20 mm (mean+/-standard deviation). These results demonstrate that our model-based segmentation method can robustly segment facial nerve centerlines. Next, we can investigate whether integration of this automated facial nerve delineation with a distance calculating neuronavigation interface results in a system that can adequately warn surgeons during temporal bone drilling, and effectively diminishes risks of iatrogenic facial nerve palsy.

  12. Functional Paraganglioma: A Rare Conus‑cauda Lesion

    African Journals Online (AJOL)

    Magnetic resonance imaging of the lumbosacral spinal segment, before and after gadolinium administration, demonstrated a lobulated lesion extending ... The role of MRI in the diagnosis and treatment of this condition is paramount, owing to ...

  13. Impact of analyzing fewer image frames per segment during offline volumetric radiofrequency based intravascular ultrasound measurements of target lesions prior to percutaneous coronary interventions

    NARCIS (Netherlands)

    Huisman, J.; Hartmann, M.; Hartmann, M.; Mintz, G.S.; van Houwelingen, G.K.; Stoel, M.G.; de Man, F.H.; Louwerenburg, H.; von Birgelen, Clemens

    2012-01-01

    In the present study, we evaluated the impact of a 50% reduction in number of image frames (every second frame) on the analysis time and variability of offline volumetric radiofrequency-based intravascular ultrasound (RF-IVUS) measurements in target lesions prior to percutaneous coronary

  14. Automatic detection and segmentation of vascular structures in dermoscopy images using a novel vesselness measure based on pixel redness and tubularness

    Science.gov (United States)

    Kharazmi, Pegah; Lui, Harvey; Stoecker, William V.; Lee, Tim

    2015-03-01

    Vascular structures are one of the most important features in the diagnosis and assessment of skin disorders. The presence and clinical appearance of vascular structures in skin lesions is a discriminating factor among different skin diseases. In this paper, we address the problem of segmentation of vascular patterns in dermoscopy images. Our proposed method is composed of three parts. First, based on biological properties of human skin, we decompose the skin to melanin and hemoglobin component using independent component analysis of skin color images. The relative quantities and pure color densities of each component were then estimated. Subsequently, we obtain three reference vectors of the mean RGB values for normal skin, pigmented skin and blood vessels from the hemoglobin component by averaging over 100000 pixels of each group outlined by an expert. Based on the Euclidean distance thresholding, we generate a mask image that extracts the red regions of the skin. Finally, Frangi measure was applied to the extracted red areas to segment the tubular structures. Finally, Otsu's thresholding was applied to segment the vascular structures and get a binary vessel mask image. The algorithm was implemented on a set of 50 dermoscopy images. In order to evaluate the performance of our method, we have artificially extended some of the existing vessels in our dermoscopy data set and evaluated the performance of the algorithm to segment the newly added vessel pixels. A sensitivity of 95% and specificity of 87% were achieved.

  15. TOURISM SEGMENTATION BASED ON TOURISTS PREFERENCES: A MULTIVARIATE APPROACH

    Directory of Open Access Journals (Sweden)

    Sérgio Dominique Ferreira

    2010-11-01

    Full Text Available Over the last decades, tourism became one of the most important sectors of the international economy. Specifically in Portugal and Brazil, its contribution to Gross Domestic Product (GDP and job creation is quite relevant. In this sense, to follow a strong marketing approach on the management of tourism resources of a country comes to be paramount. Such an approach should be based on innovations which help unveil the preferences of tourists with accuracy, turning it into a competitive advantage. In this context, the main objective of the present study is to illustrate the importance and benefits associated with the use of multivariate methodologies for market segmentation. Another objective of this work is to illustrate on the importance of a post hoc segmentation. In this work, the authors applied a Cluster Analysis, with a hierarchical method followed by an  optimization method. The main results of this study allow the identification of five clusters that are distinguished by assigning special importance to certain tourism attributes at the moment of choosing a specific destination. Thus, the authors present the advantages of post hoc segmentation based on tourists’ preferences, in opposition to an a priori segmentation based on socio-demographic characteristics.

  16. Computerized detection method for asymptomatic white matter lesions in brain screening MR images using a clustering technique

    International Nuclear Information System (INIS)

    Kunieda, Takuya; Uchiyama, Yoshikazu; Hara, Takeshi

    2008-01-01

    Asymptomatic white matter lesions are frequently identified by the screening system known as Brain Dock, which is intended for the detection of asymptomatic brain diseases. The detection of asymptomatic white matter lesions is important because their presence is associated with an increased risk of stroke. Therefore, we have developed a computerized method for the detection of asymptomatic white matter lesions in order to assist radiologists in image interpretation as a ''second opinion''. Our database consisted of T 1 - and T 2 -weighted images obtained from 73 patients. The locations of the white matter lesions were determined by an experienced neuroradiologist. In order to restrict the area to be searched for white matter lesions, we first segmented the cerebral region in T 1 -weighted images by applying thresholding and region-growing techniques. To identify the initial candidate lesions, k-means clustering with pixel values in T 1 - and T 2 -weighted images was applied to the segmented cerebral region. To eliminate false positives (FPs), we determined the features, such as location, size, and circularity, of each of the initial candidate lesions. Finally, a rule-based scheme and a quadratic discriminant analysis with these features were employed to distinguish between white matter lesions and FPs. The results showed that the sensitivity for the detection of white matter lesions was 93.2%, with 4.3 FPs per image, suggesting that our computerized method may be useful for the detection of asymptomatic white matter lesions in T 1 - and T 2 -weighted images. (author)

  17. An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

    Directory of Open Access Journals (Sweden)

    Rasha Al Shehhi

    2016-01-01

    Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

  18. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

    Directory of Open Access Journals (Sweden)

    Bo Chen

    2015-05-01

    Full Text Available Segmentation, which is usually the first step in object-based image analysis (OBIA, greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD and the edge penalty (EP. First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition’s multi-scale segmentation.

  19. Anatomo-clinical overlapping maps (AnaCOM): a new method to create anatomo-functional maps from neuropsychological tests and structural MRI scan of subjects with brain lesions

    Science.gov (United States)

    Kinkingnehun, Serge R. J.; du Boisgueheneuc, Foucaud; Golmard, Jean-Louis; Zhang, Sandy X.; Levy, Richard; Dubois, Bruno

    2004-04-01

    We have developed a new technique to analyze correlations between brain anatomy and its neurological functions. The technique is based on the anatomic MRI of patients with brain lesions who are administered neuropsychological tests. Brain lesions of the MRI scans are first manually segmented. The MRI volumes are then normalized to a reference map, using the segmented area as a mask. After normalization, the brain lesions of the MRI are segmented again in order to redefine the border of the lesions in the context of the normalized brain. Once the MRI is segmented, the patient's score on the neuropsychological test is assigned to each voxel in the lesioned area, while the rest of the voxels of the image are set to 0. Subsequently, the individual patient's MRI images are superimposed, and each voxel is reassigned the average score of the patients who have a lesion at that voxel. A threshold is applied to remove regions having less than three overlaps. This process leads to an anatomo-functional map that links brain areas to functional loss. Other maps can be created to aid in analyzing the functional maps, such as one that indicates the 95% confidence interval of the averaged scores for each area. This anatomo-clinical overlapping map (AnaCOM) method was used to obtain functional maps from patients with lesions in the superior frontal gyrus. By finding particular subregions more responsible for a particular deficit, this method can generate new hypotheses to be tested by conventional group methods.

  20. Improved dynamic-programming-based algorithms for segmentation of masses in mammograms

    International Nuclear Information System (INIS)

    Dominguez, Alfonso Rojas; Nandi, Asoke K.

    2007-01-01

    In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID 2 PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID 2 PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions

  1. Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Sung Won; Lee, Woo Jin; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il; Yi, Won Ji [Dental Research Institute, School of Dentistry, Seoul National University, Seoul (Korea, Republic of)

    2015-03-15

    We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

  2. Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation

    International Nuclear Information System (INIS)

    Kang, Sung Won; Lee, Woo Jin; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il; Yi, Won Ji

    2015-01-01

    We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

  3. Tissues segmentation based on multi spectral medical images

    Science.gov (United States)

    Li, Ya; Wang, Ying

    2017-11-01

    Each band image contains the most obvious tissue feature according to the optical characteristics of different tissues in different specific bands for multispectral medical images. In this paper, the tissues were segmented by their spectral information at each multispectral medical images. Four Local Binary Patter descriptors were constructed to extract blood vessels based on the gray difference between the blood vessels and their neighbors. The segmented tissue in each band image was merged to a clear image.

  4. Segmentation of DTI based on tensorial morphological gradient

    Science.gov (United States)

    Rittner, Leticia; de Alencar Lotufo, Roberto

    2009-02-01

    This paper presents a segmentation technique for diffusion tensor imaging (DTI). This technique is based on a tensorial morphological gradient (TMG), defined as the maximum dissimilarity over the neighborhood. Once this gradient is computed, the tensorial segmentation problem becomes an scalar one, which can be solved by conventional techniques, such as watershed transform and thresholding. Similarity functions, namely the dot product, the tensorial dot product, the J-divergence and the Frobenius norm, were compared, in order to understand their differences regarding the measurement of tensor dissimilarities. The study showed that the dot product and the tensorial dot product turned out to be inappropriate for computation of the TMG, while the Frobenius norm and the J-divergence were both capable of measuring tensor dissimilarities, despite the distortion of Frobenius norm, since it is not an affine invariant measure. In order to validate the TMG as a solution for DTI segmentation, its computation was performed using distinct similarity measures and structuring elements. TMG results were also compared to fractional anisotropy. Finally, synthetic and real DTI were used in the method validation. Experiments showed that the TMG enables the segmentation of DTI by watershed transform or by a simple choice of a threshold. The strength of the proposed segmentation method is its simplicity and robustness, consequences of TMG computation. It enables the use, not only of well-known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since TMG computation transforms tensorial images in scalar ones.

  5. Duodenum-preserving total pancreatic head resection for benign cystic neoplastic lesions.

    Science.gov (United States)

    Beger, Hans G; Schwarz, Michael; Poch, Bertram

    2012-11-01

    Cystic neoplasms of the pancreas are diagnosed frequently due to early use of abdominal imaging techniques. Intraductal papillary mucinous neoplasm, mucinous cystic neoplasm, and serous pseudopapillary neoplasia are considered pre-cancerous lesions because of frequent transformation to cancer. Complete surgical resection of the benign lesion is a pancreatic cancer preventive treatment. The application for a limited surgical resection for the benign lesions is increasingly used to reduce the surgical trauma with a short- and long-term benefit compared to major surgical procedures. Duodenum-preserving total pancreatic head resection introduced for inflammatory tumors in the pancreatic head transfers to the patient with a benign cystic lesion located in the pancreatic head, the advantages of a minimalized surgical treatment. Based on the experience of 17 patients treated for cystic neoplastic lesions with duodenum-preserving total pancreatic head resection, the surgical technique of total pancreatic head resection for adenoma, borderline tumors, and carcinoma in situ of cystic neoplasm is presented. A segmental resection of the peripapillary duodenum is recommended in case of suspected tissue ischemia of the peripapillary duodenum. In 305 patients, collected from the literature by PubMed search, in about 40% of the patients a segmental resection of the duodenum and 60% a duodenum and common bile duct-preserving total pancreatic head resection has been performed. Hospital mortality of the 17 patients was 0%. In 305 patients collected, the hospital mortality was 0.65%, 13.2% experienced a delay of gastric emptying and a pancreatic fistula in 18.2%. Recurrence of the disease was 1.5%. Thirty-two of 175 patients had carcinoma in situ. Duodenum-preserving total pancreatic head resection for benign cystic neoplastic lesions is a safe surgical procedure with low post-operative morbidity and mortality.

  6. Semi-automated Robust Quantification of Lesions (SRQL Toolbox

    Directory of Open Access Journals (Sweden)

    Kaori L Ito

    2017-05-01

    Full Text Available Quantifying lesions in a reliable manner is fundamental for studying the effects of neuroanatomical changes related to recovery in the post-stroke brain. However, the wide variability in lesion characteristics across individuals makes manual lesion segmentation a challenging and often subjective process. This often makes it difficult to combine stroke lesion data across multiple research sites, due to subjective differences in how lesions may be defined. Thus, we developed the Semi-automated Robust Quantification of Lesions (SRQL; https://github.com/npnl/SRQL; DOI: 10.5281/zenodo.557114 Toolbox that performs several analysis steps: 1 a white matter intensity correction that removes healthy white matter voxels from the lesion mask, thereby making lesions slightly more robust to subjective errors; 2 an automated report of descriptive statistics on lesions for simplified comparison between or across groups, and 3 an option to perform analyses in both native and standard space to facilitate analyses in either space. Here, we describe the methods implemented in the toolbox.

  7. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

    2017-11-01

    Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

  8. Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy

    Science.gov (United States)

    Yu, H.; Barriga, S.; Agurto, C.; Zamora, G.; Bauman, W.; Soliz, P.

    2012-03-01

    Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg, Germany). The proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness make the blood vessel segmentation method described here suitable for broad application in automated analysis of retinal images.

  9. A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors.

    Science.gov (United States)

    Appia, Vikram V; Ganapathy, Balaji; Abufadel, Amer; Yezzi, Anthony; Faber, Tracy

    2010-01-18

    We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.

  10. Reproducibility of myelin content-based human habenula segmentation at 3 Tesla.

    Science.gov (United States)

    Kim, Joo-Won; Naidich, Thomas P; Joseph, Joshmi; Nair, Divya; Glasser, Matthew F; O'halloran, Rafael; Doucet, Gaelle E; Lee, Won Hee; Krinsky, Hannah; Paulino, Alejandro; Glahn, David C; Anticevic, Alan; Frangou, Sophia; Xu, Junqian

    2018-03-26

    In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis. © 2018 Wiley Periodicals, Inc.

  11. Pupil-segmentation-based adaptive optics for microscopy

    Science.gov (United States)

    Ji, Na; Milkie, Daniel E.; Betzig, Eric

    2011-03-01

    Inhomogeneous optical properties of biological samples make it difficult to obtain diffraction-limited resolution in depth. Correcting the sample-induced optical aberrations needs adaptive optics (AO). However, the direct wavefront-sensing approach commonly used in astronomy is not suitable for most biological samples due to their strong scattering of light. We developed an image-based AO approach that is insensitive to sample scattering. By comparing images of the sample taken with different segments of the pupil illuminated, local tilt in the wavefront is measured from image shift. The aberrated wavefront is then obtained either by measuring the local phase directly using interference or with phase reconstruction algorithms similar to those used in astronomical AO. We implemented this pupil-segmentation-based approach in a two-photon fluorescence microscope and demonstrated that diffraction-limited resolution can be recovered from nonbiological and biological samples.

  12. Underwater Object Segmentation Based on Optical Features

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2018-01-01

    Full Text Available Underwater optical environments are seriously affected by various optical inputs, such as artificial light, sky light, and ambient scattered light. The latter two can block underwater object segmentation tasks, since they inhibit the emergence of objects of interest and distort image information, while artificial light can contribute to segmentation. Artificial light often focuses on the object of interest, and, therefore, we can initially identify the region of target objects if the collimation of artificial light is recognized. Based on this concept, we propose an optical feature extraction, calculation, and decision method to identify the collimated region of artificial light as a candidate object region. Then, the second phase employs a level set method to segment the objects of interest within the candidate region. This two-phase structure largely removes background noise and highlights the outline of underwater objects. We test the performance of the method with diverse underwater datasets, demonstrating that it outperforms previous methods.

  13. Alport's syndrome with focal segmental glomerulosclerosis lesion - Pattern to recognize.

    Science.gov (United States)

    Alsahli, Afnan A; Alshahwan, Sara I; Alotaibi, Amal O; Alsaad, Khaled O; Aloudah, Nourah; Farooqui, Mahfooz; Al Sayyari, Abdullah A

    2018-01-01

    The association between Alport's syndrome (AS) and focal segmental glomerulosclerosis (FSGS) in the same patient is complex and rarely reported. We report a case of a 42-year-old male presenting with proteinuria, microscopic hematuria, elevated serum creatinine and hypertension with unremarkable physical examination apart from obesity. The renal biopsy showed well-established FSGS pattern of injury with mild interstitial fibrosis and tubular atrophy, while the electron microscopic examination demonstrated glomerular basement membranes (GBM) changes compatible with AS. AS can be complicated by segmental glomerular scarring, which can mimic primary FSGS, while familial FSGS can result from mutations in collagen IV network of the GBM. This overlap can complicate histopathological interpretation of renal biopsy, which should be accompanied by mutational analysis for accurate diagnosis and proper therapeutic intervention.

  14. CO2 permeation properties of poly(ethylene oxide)-based segmented block copolymers

    NARCIS (Netherlands)

    Husken, D.; Visser, Tymen; Wessling, Matthias; Gaymans, R.J.

    2010-01-01

    This paper discusses the gas permeation properties of poly(ethylene oxide) (PEO)-based segmented block copolymers containing monodisperse amide segments. These monodisperse segments give rise to a well phase-separated morphology, comprising a continuous PEO phase with dispersed crystallised amide

  15. Image Mosaic Method Based on SIFT Features of Line Segment

    Directory of Open Access Journals (Sweden)

    Jun Zhu

    2014-01-01

    Full Text Available This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

  16. Correction tool for Active Shape Model based lumbar muscle segmentation.

    Science.gov (United States)

    Valenzuela, Waldo; Ferguson, Stephen J; Ignasiak, Dominika; Diserens, Gaelle; Vermathen, Peter; Boesch, Chris; Reyes, Mauricio

    2015-08-01

    In the clinical environment, accuracy and speed of the image segmentation process plays a key role in the analysis of pathological regions. Despite advances in anatomic image segmentation, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a low number of interactions, and a user-independent solution. In this work we present a new interactive correction method for correcting the image segmentation. Given an initial segmentation and the original image, our tool provides a 2D/3D environment, that enables 3D shape correction through simple 2D interactions. Our scheme is based on direct manipulation of free form deformation adapted to a 2D environment. This approach enables an intuitive and natural correction of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle segmentation from Magnetic Resonance Images. Experimental results show that full segmentation correction could be performed within an average correction time of 6±4 minutes and an average of 68±37 number of interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.03.

  17. Superpixel-based segmentation of muscle fibers in multi-channel microscopy.

    Science.gov (United States)

    Nguyen, Binh P; Heemskerk, Hans; So, Peter T C; Tucker-Kellogg, Lisa

    2016-12-05

    Confetti fluorescence and other multi-color genetic labelling strategies are useful for observing stem cell regeneration and for other problems of cell lineage tracing. One difficulty of such strategies is segmenting the cell boundaries, which is a very different problem from segmenting color images from the real world. This paper addresses the difficulties and presents a superpixel-based framework for segmentation of regenerated muscle fibers in mice. We propose to integrate an edge detector into a superpixel algorithm and customize the method for multi-channel images. The enhanced superpixel method outperforms the original and another advanced superpixel algorithm in terms of both boundary recall and under-segmentation error. Our framework was applied to cross-section and lateral section images of regenerated muscle fibers from confetti-fluorescent mice. Compared with "ground-truth" segmentations, our framework yielded median Dice similarity coefficients of 0.92 and higher. Our segmentation framework is flexible and provides very good segmentations of multi-color muscle fibers. We anticipate our methods will be useful for segmenting a variety of tissues in confetti fluorecent mice and in mice with similar multi-color labels.

  18. Utility of lead aVR for identifying the culprit lesion in acute myocardial infarction

    DEFF Research Database (Denmark)

    Kühl, Jørgen Tobias; Berg, Ronan M G

    2009-01-01

    BACKGROUND: Lead aVR is a neglected, however, potentially useful tool in electrocardiography. Our aim was to evaluate its value in clinical practice, by reviewing existing literature regarding its utility for identifying the culprit lesion in acute myocardial infarction (AMI). METHODS: Based...... on a systematic search strategy, 16 studies were assessed with the intent to pool data; diagnostic test rates were calculated as key results. RESULTS: Five studies investigated if ST-segment elevation (STE) in aVR is valuable for the diagnosis of left main stem stenosis (LMS) in non-ST-segment AMI (NSTEMI......). The studies were too heterogeneous to pool, but the individual studies all showed that STE in aVR has a high negative predictive value (NPV) for LMS. Six studies evaluated if STE in aVR is valuable for distinguishing proximal from distal lesions in the left anterior descending artery (LAD) in anterior ST...

  19. A novel multiphoton microscopy images segmentation method based on superpixel and watershed.

    Science.gov (United States)

    Wu, Weilin; Lin, Jinyong; Wang, Shu; Li, Yan; Liu, Mingyu; Liu, Gaoqiang; Cai, Jianyong; Chen, Guannan; Chen, Rong

    2017-04-01

    Multiphoton microscopy (MPM) imaging technique based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) shows fantastic performance for biological imaging. The automatic segmentation of cellular architectural properties for biomedical diagnosis based on MPM images is still a challenging issue. A novel multiphoton microscopy images segmentation method based on superpixels and watershed (MSW) is presented here to provide good segmentation results for MPM images. The proposed method uses SLIC superpixels instead of pixels to analyze MPM images for the first time. The superpixels segmentation based on a new distance metric combined with spatial, CIE Lab color space and phase congruency features, divides the images into patches which keep the details of the cell boundaries. Then the superpixels are used to reconstruct new images by defining an average value of superpixels as image pixels intensity level. Finally, the marker-controlled watershed is utilized to segment the cell boundaries from the reconstructed images. Experimental results show that cellular boundaries can be extracted from MPM images by MSW with higher accuracy and robustness. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Comparison of atlas-based techniques for whole-body bone segmentation

    DEFF Research Database (Denmark)

    Arabi, Hossein; Zaidi, Habib

    2017-01-01

    out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice....../MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross...... validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean...

  1. A Region-Based GeneSIS Segmentation Algorithm for the Classification of Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    Stelios K. Mylonas

    2015-03-01

    Full Text Available This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms.

  2. detecting multiple sclerosis lesions with a fully bioinspired visual attention model

    Science.gov (United States)

    Villalon-Reina, Julio; Gutierrez-Carvajal, Ricardo; Thompson, Paul M.; Romero-Castro, Eduardo

    2013-11-01

    The detection, segmentation and quantification of multiple sclerosis (MS) lesions on magnetic resonance images (MRI) has been a very active field for the last two decades because of the urge to correlate these measures with the effectiveness of pharmacological treatment. A myriad of methods has been developed and most of these are non specific for the type of lesions and segment the lesions in their acute and chronic phases together. On the other hand, radiologists are able to distinguish between several stages of the disease on different types of MRI images. The main motivation of the work presented here is to computationally emulate the visual perception of the radiologist by using modeling principles of the neuronal centers along the visual system. By using this approach we are able to detect the lesions in the majority of the images in our population sample. This type of approach also allows us to study and improve the analysis of brain networks by introducing a priori information.

  3. Snake Model Based on Improved Genetic Algorithm in Fingerprint Image Segmentation

    Directory of Open Access Journals (Sweden)

    Mingying Zhang

    2016-12-01

    Full Text Available Automatic fingerprint identification technology is a quite mature research field in biometric identification technology. As the preprocessing step in fingerprint identification, fingerprint segmentation can improve the accuracy of fingerprint feature extraction, and also reduce the time of fingerprint preprocessing, which has a great significance in improving the performance of the whole system. Based on the analysis of the commonly used methods of fingerprint segmentation, the existing segmentation algorithm is improved in this paper. The snake model is used to segment the fingerprint image. Additionally, it is improved by using the global optimization of the improved genetic algorithm. Experimental results show that the algorithm has obvious advantages both in the speed of image segmentation and in the segmentation effect.

  4. Dilatation of the intrahepatic bile ducts associated with benign liver lesions: an unusual finding

    International Nuclear Information System (INIS)

    Lapeyre, Matthieu; Mathieu, Didier; Rahmouni, Alain; Kobeiter, Hicham; Tailboux, Laurent

    2002-01-01

    In three patients presenting different types of liver lesions, including isolated cyst, focal nodular hyperplasia (FNH), and hemangioma, intrahepatic bile duct dilatation was observed on US and CT. Final diagnosis was obtained by surgery in two cases (cyst and FNH) and by 1-year follow-up in one patient presenting an isolated hemangioma. The only common characteristic in our three cases was that lesions were present in segment four according to Couinaud's classification, at the level of the transverse fissure, suggesting that a space-occupying lesion at this site may cause compression of the common hepatic duct and right or left intrahepatic bile ducts. Our report indicates that compression may occur even with lesion of moderate size (35-40 mm in diameter). A benign liver lesion may cause a bile duct dilatation, particularly if located in segment 4, close to the hilum. Awareness of this possibility is important to avoid unnecessary invasive diagnostic procedures, particularly when all imaging criteria are consistent with a benign lesion. (orig.)

  5. Development of an acquisition protocol and a segmentation algortihm for wounds of cutaneous Leishmaniasis in digital images

    Science.gov (United States)

    Diaz, Kristians; Castañeda, Benjamín; Miranda, César; Lavarello, Roberto; Llanos, Alejandro

    2010-03-01

    We developed a protocol for the acquisition of digital images and an algorithm for a color-based automatic segmentation of cutaneous lesions of Leishmaniasis. The protocol for image acquisition provides control over the working environment to manipulate brightness, lighting and undesirable shadows on the injury using indirect lighting. Also, this protocol was used to accurately calculate the area of the lesion expressed in mm2 even in curved surfaces by combining the information from two consecutive images. Different color spaces were analyzed and compared using ROC curves in order to determine the color layer with the highest contrast between the background and the wound. The proposed algorithm is composed of three stages: (1) Location of the wound determined by threshold and mathematical morphology techniques to the H layer of the HSV color space, (2) Determination of the boundaries of the wound by analyzing the color characteristics in the YIQ space based on masks (for the wound and the background) estimated from the first stage, and (3) Refinement of the calculations obtained on the previous stages by using the discrete dynamic contours algorithm. The segmented regions obtained with the algorithm were compared with manual segmentations made by a medical specialist. Broadly speaking, our results support that color provides useful information during segmentation and measurement of wounds of cutaneous Leishmaniasis. Results from ten images showed 99% specificity, 89% sensitivity, and 98% accuracy.

  6. Automated brain structure segmentation based on atlas registration and appearance models

    DEFF Research Database (Denmark)

    van der Lijn, Fedde; de Bruijne, Marleen; Klein, Stefan

    2012-01-01

    Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure’s location and appearance. The spatial...... with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results...

  7. Value of a dixon-based MR/PET attenuation correction sequence for the localization and evaluation of PET-positive lesions

    International Nuclear Information System (INIS)

    Eiber, Matthias; Holzapfel, Konstantin; Rummeny, Ernst J.; Martinez-Moeller, Axel; Souvatzoglou, Michael; Ziegler, Sibylle; Schwaiger, Markus; Nekolla, Stephan G.; Beer, Ambros J.; Pickhard, Anja; Loeffelbein, Dennys; Santi, Ivan

    2011-01-01

    In this study, the potential contribution of Dixon-based MR imaging with a rapid low-resolution breath-hold sequence, which is a technique used for MR-based attenuation correction (AC) for MR/positron emission tomography (PET), was evaluated for anatomical correlation of PET-positive lesions on a 3T clinical scanner compared to low-dose CT. This technique is also used in a recently installed fully integrated whole-body MR/PET system. Thirty-five patients routinely scheduled for oncological staging underwent 18 F-fluorodeoxyglucose (FDG) PET/CT and a 2-point Dixon 3-D volumetric interpolated breath-hold examination (VIBE) T1-weighted MR sequence on the same day. Two PET data sets reconstructed using attenuation maps from low-dose CT (PET AC C T ) or simulated MR-based segmentation (PET AC M R ) were evaluated for focal PET-positive lesions. The certainty for the correlation with anatomical structures was judged in the low-dose CT and Dixon-based MRI on a 4-point scale (0-3). In addition, the standardized uptake values (SUVs) for PET AC C T and PET AC M R were compared. Statistically, no significant difference could be found concerning anatomical localization for all 81 PET-positive lesions in low-dose CT compared to Dixon-based MR (mean 2.51 ± 0.85 and 2.37 ± 0.87, respectively; p = 0.1909). CT tended to be superior for small lymph nodes, bone metastases and pulmonary nodules, while Dixon-based MR proved advantageous for soft tissue pathologies like head/neck tumours and liver metastases. For the PET AC C T - and PET AC M R -based SUVs (mean 6.36 ± 4.47 and 6.31 ± 4.52, respectively) a nearly complete concordance with a highly significant correlation was found (r = 0.9975, p < 0.0001). Dixon-based MR imaging for MR AC allows for anatomical allocation of PET-positive lesions similar to low-dose CT in conventional PET/CT. Thus, this approach appears to be useful for future MR/PET for body regions not fully covered by diagnostic MRI due to potential time

  8. A smartphone application for psoriasis segmentation and classification (Conference Presentation)

    Science.gov (United States)

    Vasefi, Fartash; MacKinnon, Nicholas B.; Horita, Timothy; Shi, Kevin; Khan Munia, Tamanna Tabassum; Tavakolian, Kouhyar; Alhashim, Minhal; Fazel-Rezai, Reza

    2017-02-01

    Psoriasis is a chronic skin disease affecting approximately 125 million people worldwide. Currently, dermatologists monitor changes of psoriasis by clinical evaluation or by measuring psoriasis severity scores over time which lead to Subjective management of this condition. The goal of this paper is to develop a reliable assessment system to quantitatively assess the changes of erythema and intensity of scaling of psoriatic lesions. A smartphone deployable mobile application is presented that uses the smartphone camera and cloud-based image processing to analyze physiological characteristics of psoriasis lesions, identify the type and stage of the scaling and erythema. The application targets to automatically evaluate Psoriasis Area Severity Index (PASI) by measuring the severity and extent of psoriasis. The mobile application performs the following core functions: 1) it captures text information from user input to create a profile in a HIPAA compliant database. 2) It captures an image of the skin with psoriasis as well as image-related information entered by the user. 3) The application color correct the image based on environmental lighting condition using calibration process including calibration procedure by capturing Macbeth ColorChecker image. 4) The color-corrected image will be transmitted to a cloud-based engine for image processing. In cloud, first, the algorithm removes the non-skin background to ensure the psoriasis segmentation is only applied to the skin regions. Then, the psoriasis segmentation algorithm estimates the erythema and scaling boundary regions of lesion. We analyzed 10 images of psoriasis images captured by cellphone, determined PASI score for each subject during our pilot study, and correlated it with changes in severity scores given by dermatologists. The success of this work allows smartphone application for psoriasis severity assessment in a long-term treatment.

  9. Two-stage atlas subset selection in multi-atlas based image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States)

    2015-06-15

    Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors

  10. Two-stage atlas subset selection in multi-atlas based image segmentation.

    Science.gov (United States)

    Zhao, Tingting; Ruan, Dan

    2015-06-01

    Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. The authors have developed a novel two-stage atlas

  11. Two-stage atlas subset selection in multi-atlas based image segmentation

    International Nuclear Information System (INIS)

    Zhao, Tingting; Ruan, Dan

    2015-01-01

    Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors

  12. Computer-aided segmentation system for 3D chest CT

    International Nuclear Information System (INIS)

    Iwasawa, Tae; Komagata, Takanobu; Ogura, Takashi; Iwao, Yuma; Goto, Toshiyuki; Asakura, Akira; Inoue, Tomio

    2012-01-01

    We will introduce the quantitative analysis of the chest CT images using computer-assisted segmentation system (Gaussian Histogram Normalized Correlation; GHNC). This system can divide the lung into several patterns, for example, normal, emphysema and fibrous lesion, and measure each lesion volume quantitatively. We analyzed 3D-CT images of 20 patients with lung cancer. GHNC could measure the volumes of emphysema and fibrosis lesions, respectively. GHNC analysis will be feasible for preoperative CT evaluation, especially in the patients with combined pulmonary fibrosis and emphysema. (author)

  13. Extracolic-infiltrative lesions of the sigmoid colon and recto-sigmoid junction

    Energy Technology Data Exchange (ETDEWEB)

    Krestin, G.P.; Beyer, D.; Lorenz, R.

    1983-06-01

    The problems of extracolic-infiltrative lesions of the sigmoid colon and recto-sigmoid junction are discussed, based on representative case studies. Because of the localisation of these bowel segments in the lower peritoneal cavity, they can be reached by direct infiltration of contiguous tumors - especially carcinoma of the ovary - or by seeding into the pouch of Douglas and the sigmoid mesocolon. In many cases characteristic roentgen-morphologic features indicate a secondary infiltration, but inflammatory and malignant lesions have to be differentiated. The differential-diagnosis must consider also diverticulitis, endometriosis and extracolic growth of colonic carcinoma. Additional ultrasonography and computed tomography may help to clear topographical relations and to detect direct and indirect signs of peritoneal carcinomatosis and abscess.

  14. Extracolic-infiltrative lesions of the sigmoid colon and recto-sigmoid junction

    International Nuclear Information System (INIS)

    Krestin, G.P.; Beyer, D.; Lorenz, R.

    1983-01-01

    The problems of extracolic-infiltrative lesions of the sigmoid colon and recto-sigmoid junction are discussed, based on representative case studies. Because of the localisation of these bowel segments in the lower peritoneal cavity, they can be reached by direct infiltration of contiguous tumors - especially carcinoma of the ovary - or by seading into the pouch of Douglas and the sigmoid mesocolon. In many cases characteristic roentgen-morphologic features indicate a secondary infiltration, but inflammatory and malignant lesions have to be differentiated. The differential-diagnosis must consider also diverticulitis, endometriosis and extracolic growth of colonic carcinoma. Additional ultrasonography and computed tomography may help to clear topographical relations and to detect direct and indirect signs of peritoneal carcinomatosis and abscess. (orig.)

  15. Toward a non-invasive screening tool for differentiation of pancreatic lesions based on intra-voxel incoherent motion derived parameters

    Energy Technology Data Exchange (ETDEWEB)

    Graf, Markus; Simon, Dirk; Mang, Sarah [Deutsches Krebsforschungszentrum (DKFZ), Heidelberg (Germany). Software Development for Integrated Therapy and Diagnostics; Lemke, Andreas [Heidelberg Univ., Mannheim (Germany). Dept. of Computer Assisted Clinical Medicine; Gruenberg, Katharina [Deutsches Krebsforschungszentrum (DKFZ), Heidelberg (Germany). Dept. of Radiology

    2013-03-01

    Early recognition of and differential diagnosis between pancreatic cancer and chronic pancreatitis is an important step in successful therapy. Parameters of the IVIM (intra-voxel incoherent motion) theory can be used to differentiate between those lesions. The objective of this work is to evaluate the effects of rigid image registration on IVIM derived parameters for differentiation of pancreatic lesions such as pancreatic cancer and solid mass forming pancreatitis. The effects of linear image registration methods on reproducibility and accuracy of IVIM derived parameters were quantified on MR images of ten volunteers. For this purpose, they were evaluated statistically by comparison of registered and unregistered parameter data. Further, the perfusion fraction f was used to differentiate pancreatic lesions on eleven previously diagnosed patient data sets. Its diagnostic power with and without rigid registration was evaluated using receiver operating curves (ROC) analysis. The pancreas was segmented manually on MR data sets of healthy volunteers as well as the patients showing solid pancreatic lesions. Diffusion weighted imaging was performed in 10 blocks of breath-hold phases. Linear registration of the weighted image stack leads to a 3.7% decrease in variability of the IVIM derived parameter f due to an improved anatomical overlap of 5%. Consequently, after registration the area under the curve in the ROC-analysis for the differentiation approach increased by 2.7%. In conclusion, rigid registration improves the differentiation process based on f-values. (orig.)

  16. Toward a non-invasive screening tool for differentiation of pancreatic lesions based on intra-voxel incoherent motion derived parameters

    International Nuclear Information System (INIS)

    Graf, Markus; Simon, Dirk; Mang, Sarah; Lemke, Andreas; Gruenberg, Katharina

    2013-01-01

    Early recognition of and differential diagnosis between pancreatic cancer and chronic pancreatitis is an important step in successful therapy. Parameters of the IVIM (intra-voxel incoherent motion) theory can be used to differentiate between those lesions. The objective of this work is to evaluate the effects of rigid image registration on IVIM derived parameters for differentiation of pancreatic lesions such as pancreatic cancer and solid mass forming pancreatitis. The effects of linear image registration methods on reproducibility and accuracy of IVIM derived parameters were quantified on MR images of ten volunteers. For this purpose, they were evaluated statistically by comparison of registered and unregistered parameter data. Further, the perfusion fraction f was used to differentiate pancreatic lesions on eleven previously diagnosed patient data sets. Its diagnostic power with and without rigid registration was evaluated using receiver operating curves (ROC) analysis. The pancreas was segmented manually on MR data sets of healthy volunteers as well as the patients showing solid pancreatic lesions. Diffusion weighted imaging was performed in 10 blocks of breath-hold phases. Linear registration of the weighted image stack leads to a 3.7% decrease in variability of the IVIM derived parameter f due to an improved anatomical overlap of 5%. Consequently, after registration the area under the curve in the ROC-analysis for the differentiation approach increased by 2.7%. In conclusion, rigid registration improves the differentiation process based on f-values. (orig.)

  17. A new user-assisted segmentation and tracking technique for an object-based video editing system

    Science.gov (United States)

    Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

    2004-03-01

    This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

  18. Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy.

    Science.gov (United States)

    Pagnozzi, Alex M; Dowson, Nicholas; Doecke, James; Fiori, Simona; Bradley, Andrew P; Boyd, Roslyn N; Rose, Stephen

    2016-01-01

    White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs) of children with cerebral palsy (CP). Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established. In this study, we perform automatic lesion segmentation on a large cohort of data (107 children with unilateral CP and 18 healthy children) with a new, validated method for segmenting both white matter (WM) and grey matter (GM) lesions. The method has better accuracy (94%) than the best current methods (73%), and only requires standard structural MRI sequences. Anatomical lesion burdens most predictive of clinical scores of motor, cognitive, visual and communicative function were identified using the Least Absolute Shrinkage and Selection operator (LASSO). The improved segmentations enabled identification of significant correlations between regional lesion burden and clinical performance, which conform to known structure-function relationships. Model performance was validated in an independent test set, with significant correlations observed for both WM and GM regional lesion burden with motor function (p < 0.008), and between WM and GM lesions alone with cognitive and visual function respectively (p < 0.008). The significant correlation of GM lesions with functional outcome highlights the serious implications GM lesions, in addition to WM lesions, have for prognosis, and the utility of structural MRI alone for quantifying lesion burden and planning therapy interventions.

  19. Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy

    Directory of Open Access Journals (Sweden)

    Alex M. Pagnozzi

    2016-01-01

    Full Text Available White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs of children with cerebral palsy (CP. Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established. In this study, we perform automatic lesion segmentation on a large cohort of data (107 children with unilateral CP and 18 healthy children with a new, validated method for segmenting both white matter (WM and grey matter (GM lesions. The method has better accuracy (94% than the best current methods (73%, and only requires standard structural MRI sequences. Anatomical lesion burdens most predictive of clinical scores of motor, cognitive, visual and communicative function were identified using the Least Absolute Shrinkage and Selection operator (LASSO. The improved segmentations enabled identification of significant correlations between regional lesion burden and clinical performance, which conform to known structure-function relationships. Model performance was validated in an independent test set, with significant correlations observed for both WM and GM regional lesion burden with motor function (p < 0.008, and between WM and GM lesions alone with cognitive and visual function respectively (p < 0.008. The significant correlation of GM lesions with functional outcome highlights the serious implications GM lesions, in addition to WM lesions, have for prognosis, and the utility of structural MRI alone for quantifying lesion burden and planning therapy interventions.

  20. Boundary fitting based segmentation of fluorescence microscopy images

    Science.gov (United States)

    Lee, Soonam; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

    2015-03-01

    Segmentation is a fundamental step in quantifying characteristics, such as volume, shape, and orientation of cells and/or tissue. However, quantification of these characteristics still poses a challenge due to the unique properties of microscopy volumes. This paper proposes a 2D segmentation method that utilizes a combination of adaptive and global thresholding, potentials, z direction refinement, branch pruning, end point matching, and boundary fitting methods to delineate tubular objects in microscopy volumes. Experimental results demonstrate that the proposed method achieves better performance than an active contours based scheme.

  1. Common histological patterns in glomerular epithelial cells in secondary focal segmental glomerulosclerosis

    NARCIS (Netherlands)

    Kuppe, C.; Grone, H.J.; Ostendorf, T.; Kuppevelt, T.H. van; Boor, P.; Floege, J.; Smeets, B.; Moeller, M.J.

    2015-01-01

    Parietal epithelial cells (PECs) are involved in the development of sclerotic lesions in primary focal and segmental glomerulosclerosis (FSGS). Here, the role of PECs was explored in the more common secondary FSGS lesions in 68 patient biopsies, diagnosed with 11 different frequently or rarely

  2. A comprehensive segmentation analysis of crude oil market based on time irreversibility

    Science.gov (United States)

    Xia, Jianan; Shang, Pengjian; Lu, Dan; Yin, Yi

    2016-05-01

    In this paper, we perform a comprehensive entropic segmentation analysis of crude oil future prices from 1983 to 2014 which used the Jensen-Shannon divergence as the statistical distance between segments, and analyze the results from original series S and series begin at 1986 (marked as S∗) to find common segments which have same boundaries. Then we apply time irreversibility analysis of each segment to divide all segments into two groups according to their asymmetry degree. Based on the temporal distribution of the common segments and high asymmetry segments, we figure out that these two types of segments appear alternately and do not overlap basically in daily group, while the common portions are also high asymmetry segments in weekly group. In addition, the temporal distribution of the common segments is fairly close to the time of crises, wars or other events, because the hit from severe events to oil price makes these common segments quite different from their adjacent segments. The common segments can be confirmed in daily group series, or weekly group series due to the large divergence between common segments and their neighbors. While the identification of high asymmetry segments is helpful to know the segments which are not affected badly by the events and can recover to steady states automatically. Finally, we rearrange the segments by merging the connected common segments or high asymmetry segments into a segment, and conjoin the connected segments which are neither common nor high asymmetric.

  3. Label fusion based brain MR image segmentation via a latent selective model

    Science.gov (United States)

    Liu, Gang; Guo, Xiantang; Zhu, Kai; Liao, Hengxu

    2018-04-01

    Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

  4. IBES: A Tool for Creating Instructions Based on Event Segmentation

    Directory of Open Access Journals (Sweden)

    Katharina eMura

    2013-12-01

    Full Text Available Receiving informative, well-structured, and well-designed instructions supports performance and memory in assembly tasks. We describe IBES, a tool with which users can quickly and easily create multimedia, step-by-step instructions by segmenting a video of a task into segments. In a validation study we demonstrate that the step-by-step structure of the visual instructions created by the tool corresponds to the natural event boundaries, which are assessed by event segmentation and are known to play an important role in memory processes. In one part of the study, twenty participants created instructions based on videos of two different scenarios by using the proposed tool. In the other part of the study, ten and twelve participants respectively segmented videos of the same scenarios yielding event boundaries for coarse and fine events. We found that the visual steps chosen by the participants for creating the instruction manual had corresponding events in the event segmentation. The number of instructional steps was a compromise between the number of fine and coarse events. Our interpretation of results is that the tool picks up on natural human event perception processes of segmenting an ongoing activity into events and enables the convenient transfer into meaningful multimedia instructions for assembly tasks. We discuss the practical application of IBES, for example, creating manuals for differing expertise levels, and give suggestions for research on user-oriented instructional design based on this tool.

  5. IBES: a tool for creating instructions based on event segmentation.

    Science.gov (United States)

    Mura, Katharina; Petersen, Nils; Huff, Markus; Ghose, Tandra

    2013-12-26

    Receiving informative, well-structured, and well-designed instructions supports performance and memory in assembly tasks. We describe IBES, a tool with which users can quickly and easily create multimedia, step-by-step instructions by segmenting a video of a task into segments. In a validation study we demonstrate that the step-by-step structure of the visual instructions created by the tool corresponds to the natural event boundaries, which are assessed by event segmentation and are known to play an important role in memory processes. In one part of the study, 20 participants created instructions based on videos of two different scenarios by using the proposed tool. In the other part of the study, 10 and 12 participants respectively segmented videos of the same scenarios yielding event boundaries for coarse and fine events. We found that the visual steps chosen by the participants for creating the instruction manual had corresponding events in the event segmentation. The number of instructional steps was a compromise between the number of fine and coarse events. Our interpretation of results is that the tool picks up on natural human event perception processes of segmenting an ongoing activity into events and enables the convenient transfer into meaningful multimedia instructions for assembly tasks. We discuss the practical application of IBES, for example, creating manuals for differing expertise levels, and give suggestions for research on user-oriented instructional design based on this tool.

  6. Fluid region segmentation in OCT images based on convolution neural network

    Science.gov (United States)

    Liu, Dong; Liu, Xiaoming; Fu, Tianyu; Yang, Zhou

    2017-07-01

    In the retinal image, characteristics of fluid have great significance for diagnosis in eye disease. In the clinical, the segmentation of fluid is usually conducted manually, but is time-consuming and the accuracy is highly depend on the expert's experience. In this paper, we proposed a segmentation method based on convolution neural network (CNN) for segmenting the fluid from fundus image. The B-scans of OCT are segmented into layers, and patches from specific region with annotation are used for training. After the data set being divided into training set and test set, network training is performed and a good segmentation result is obtained, which has a significant advantage over traditional methods such as threshold method.

  7. Application of In-Segment Multiple Sampling in Object-Based Classification

    Directory of Open Access Journals (Sweden)

    Nataša Đurić

    2014-12-01

    Full Text Available When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN and Support Vector Machine (SVM. Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

  8. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  9. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-01-01

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  10. Semi-automated Robust Quantification of Lesions (SRQL Toolbox

    Directory of Open Access Journals (Sweden)

    Kaori Ito

    2017-02-01

    Full Text Available Quantifying lesions in a robust manner is fundamental for studying the effects of neuroanatomical changes in the post-stroke brain on recovery. However, the wide variability in lesion characteristics across individuals makes manual lesion segmentation a challenging and often subjective process. This makes it difficult to combine stroke lesion data across multiple research sites, due to subjective differences in how lesions may be defined. We developed the Semi-automated Robust Quantification of Lesions (SRQL; https://github.com/npnl/SRQL; DOI: 10.5281/zenodo.267213 Toolbox that performs several analysis steps: 1 a white matter intensity correction that removes healthy white matter voxels from the lesion mask, thereby making lesions slightly more robust to subjective errors; 2 an automated report of descriptive statistics on lesions for simplified comparison between or across groups, and 3 an option to perform analyses in both native and standard space to facilitate analyses in either space, or comparisons between spaces. Here, we describe the methods implemented in the toolbox and demonstrate the outputs of the SRQL toolbox.

  11. Assignment of simian rotavirus SA11 temperature-sensitive mutant groups B and E to genome segments

    International Nuclear Information System (INIS)

    Gombold, J.L.; Estes, M.K.; Ramig, R.F.

    1985-01-01

    Recombinant (reassortant) viruses were selected from crosses between temperature-sensitive (ts) mutants of simian rotavirus SA11 and wild-type human rotavirus Wa. The double-stranded genome RNAs of the reassortants were examined by electrophoresis in Tris-glycine-buffered polyacrylamide gels and by dot hybridization with a cloned DNA probe for genome segment 2. Analysis of replacements of genome segments in the reassortants allowed construction of a map correlating genome segments providing functions interchangeable between SA11 and Wa. The reassortants revealed a functional correspondence in order of increasing electrophoretic mobility of genome segments. Analysis of the parental origin of genome segments in ts+ SA11/Wa reassortants derived from the crosses SA11 tsB(339) X Wa and SA11 tsE(1400) X Wa revealed that the group B lesion of tsB(339) was located on genome segment 3 and the group E lesion of tsE(1400) was on segment 8

  12. Assignment of simian rotavirus SA11 temperature-sensitive mutant groups B and E to genome segments

    Energy Technology Data Exchange (ETDEWEB)

    Gombold, J.L.; Estes, M.K.; Ramig, R.F.

    1985-05-01

    Recombinant (reassortant) viruses were selected from crosses between temperature-sensitive (ts) mutants of simian rotavirus SA11 and wild-type human rotavirus Wa. The double-stranded genome RNAs of the reassortants were examined by electrophoresis in Tris-glycine-buffered polyacrylamide gels and by dot hybridization with a cloned DNA probe for genome segment 2. Analysis of replacements of genome segments in the reassortants allowed construction of a map correlating genome segments providing functions interchangeable between SA11 and Wa. The reassortants revealed a functional correspondence in order of increasing electrophoretic mobility of genome segments. Analysis of the parental origin of genome segments in ts+ SA11/Wa reassortants derived from the crosses SA11 tsB(339) X Wa and SA11 tsE(1400) X Wa revealed that the group B lesion of tsB(339) was located on genome segment 3 and the group E lesion of tsE(1400) was on segment 8.

  13. A top-down manner-based DCNN architecture for semantic image segmentation.

    Directory of Open Access Journals (Sweden)

    Kai Qiao

    Full Text Available Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN and FCN with conditional random field (DeepLab-CRF as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.

  14. MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

    Science.gov (United States)

    Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

    2012-01-01

    Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR

  15. MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

    Energy Technology Data Exchange (ETDEWEB)

    Fei, Baowei, E-mail: bfei@emory.edu [Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road Northeast, Atlanta, Georgia 30329 (United States); Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322 (United States); Department of Mathematics and Computer Sciences, Emory University, Atlanta, Georgia 30322 (United States); Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Aarsvold, John N. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Nuclear Medicine Service, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia 30033 (United States); Cervo, Morgan; Stark, Rebecca [The Medical Physics Graduate Program in the George W. Woodruff School, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Meltzer, Carolyn C. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Department of Neurology and Department of Psychiatry and Behavior Sciences, Emory University School of Medicine, Atlanta, Georgia 30322 (United States)

    2012-10-15

    Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [{sup 11}C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

  16. MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

    International Nuclear Information System (INIS)

    Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R.; Aarsvold, John N.; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.

    2012-01-01

    Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with ["1"1C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

  17. Segmentation and quantification of retinal lesions in age-related macular degeneration using polarization-sensitive optical coherence tomography.

    Science.gov (United States)

    Baumann, Bernhard; Gotzinger, Erich; Pircher, Michael; Sattmann, Harald; Schuutze, Christopher; Schlanitz, Ferdinand; Ahlers, Christian; Schmidt-Erfurth, Ursula; Hitzenberger, Christoph K

    2010-01-01

    We present polarization-sensitive optical coherence tomography (PS-OCT) for quantitative assessment of retinal pathologies in age-related macular degeneration (AMD). On the basis of the polarization scrambling characteristics of the retinal pigment epithelium, novel segmentation algorithms were developed that allow one to segment pathologic features such as drusen and atrophic zones in dry AMD as well as to determine their dimensions. Results from measurements in the eyes of AMD patients prove the ability of PS-OCT for quantitative imaging based on the retinal features polarizing properties. Repeatability measurements were performed in retinas diagnosed with drusen and geographic atrophy in order to evaluate the performance of the described methods. PS-OCT appears as a promising imaging modality for three-dimensional retinal imaging and ranging with additional contrast based on the structures' tissue-inherent polarization properties.

  18. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

    2015-01-01

    with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

  19. Detection and classification of focal liver lesions in patients with colorectal cancer: Retrospective comparison of diffusion-weighted MR imaging and multi-slice CT

    International Nuclear Information System (INIS)

    Eiber, Matthias; Fingerle, Alexander A.; Brügel, Melanie; Gaa, Jochen; Rummeny, Ernst J.; Holzapfel, Konstantin

    2012-01-01

    Objectives: To compare the diagnostic performance of diffusion-weighted MR imaging (DWI) with multi-slice CT (MS-CT) in the detection and classification of focal liver lesions in patients with colorectal cancer. Methods: In a retrospective study 68 patients who underwent DWI at 1.5 T (b-values of 50, 300 and 600 s/mm 2 ) and contrast-enhanced MS-CT were analysed by two radiologists blinded to the clinical results. Imaging results were correlated with intraoperative surgical and ultrasound findings (n = 24), imaging follow-up or PET (n = 44). Sensitivity of DWI and MS-CT in detection of focal liver lesions was compared on a per-lesion and a per-segment basis. Receiver operator-characteristic (ROC) curves to determine the diagnostic performance and the sensitivities of correctly identifying liver metastases on a segmental base were calculated. Results: For lesion detection, DWI was significantly superior to MS-CT both on a per-lesion (difference in sensitivities for reader 1 and 2 22.65% and 19.06%, p < 0.0001) and a per-segment basis (16.86% and 11.76%, p < 0.0001). Especially lesions smaller than 10 mm were better detected with DWI compared to MS-CT (difference 41.10% and 29.45%, p < 0.0001). ROC-analysis showed superiority for lesions classification (p < 0.0001) of DWI (AUC: 0.949 and 0.951) as compared to MS-CT (AUC: 0.879 and 0.892, p < 0.0001 and p = 0.005). DWI was able to filter out metastatic segments with a higher sensitivity (88.2 and 86.5%) compared to MS-CT (68.0 and 67.4%, p < 0.0001 and p = 0.005, respectively). Conclusion: Compared to MS-CT DWI is both more sensitive in the detection of liver lesions and more accurate in determining the extent of metastatic disease in patients with colorectal cancer and therefore might help to optimize therapeutic management in those patients.

  20. TH-E-BRF-08: Subpopulations of Similarly-Responding Lesions in Metastatic Prostate Cancer

    International Nuclear Information System (INIS)

    Lin, C; Harmon, S; Perk, T; Jeraj, R

    2014-01-01

    Purpose: In patients with multiple lesions, resistance to cancer treatments and subsequent disease recurrence may be due to heterogeneity of response across lesions. This study aims to identify subpopulations of similarly-responding metastatic prostate cancer lesions in bone using quantitative PET metrics. Methods: Seven metastatic prostate cancer patients treated with AR-directed therapy received pre-treatment and mid-treatment [F-18]NaF PET/CT scans. Images were registered using an articulated CT registration algorithm and transformations were applied to PET segmentations. Midtreatment response was calculated on PET-based texture features. Hierarchical agglomerative clustering was used to form groups of similarly-responding lesions, with the number of natural clusters (K) determined by the inconsistency coefficient. Lesion clustering was performed within each patient, and for the pooled population. The cophenetic coefficient (C) quantified how well the data was clustered. The Jaccard Index (JI) assessed similarity of cluster assignments from patient clustering and from population clustering. Results: 188 lesions in seven patients were identified for analysis (between 6 to 53 lesions per patient). Lesion response was defined as percent change relative to pre-treatment for 23 uncorrelated PET-based feature identifiers. . High response heterogeneity was found across all lesions (i.e. range ΔSUVmax =−95.98% to 775.00%). For intra-patient clustering, K ranged from 1–20. Population-based clustering resulted in 75 clusters, of 1-6 lesions each. Intra-patient clustering resulted in higher quality clusters than population clustering (mean C=0.95, range=0.89 to 1.00). For all patients, cluster assignments from population clustering showed good agreement to intra-patient clustering (mean JI=0.87, range=0.68 to 1.00). Conclusion: Subpopulations of similarly-responding lesions were identified in patients with multiple metastatic lesions. Good agreement was found between

  1. SEGMENTING RETAIL MARKETS ON STORE IMAGE USING A CONSUMER-BASED METHODOLOGY

    NARCIS (Netherlands)

    STEENKAMP, JBEM; WEDEL, M

    1991-01-01

    Various approaches to segmenting retail markets based on store image are reviewed, including methods that have not yet been applied to retailing problems. It is argued that a recently developed segmentation technique, fuzzy clusterwise regression analysis (FCR), holds high potential for store-image

  2. Betel quid oral lichenoid lesions: a hospital based cross-sectional study.

    Science.gov (United States)

    Arya, Sugandha; Vengal, Manoj; Raju, Bina; Patil, Neelkant; Sathosker, Sujatha; Bateja, Sumit; David, Jamil

    2017-02-01

    The aim of this study was to assess the prevalence and risk indicators of betel quid oral lichenoid lesions in chewers. A total of 1209 chewers were identified and categorized into three main groups based on the type of lesion: betel quid oral lichenoid lesions only, betel quid oral lichenoid lesions in association with quid-induced other oral mucosal lesions, and no lesions. Multinomial regression analyses were used to determine associations between dependent and independent variables. Betel quid oral lichenoid lesions were more common in individuals who chewed quid comprising both tobacco and areca nut, and in those who chewed it two to three, or greater than three, times a day. Betel quid oral lichenoid lesions + quid-induced other oral mucosal lesions were more likely to occur in females, and in individuals who chewed quid containing both tobacco and areca nut, in their processed and unprocessed forms, and greater than three times a day. The prevalence of betel quid oral lichenoid lesions was higher than that reported in previous studies conducted in India. Increase in the frequency and duration of quid chewing was associated with increased likelihood of developing these oral lichenoid lesions. © 2015 Wiley Publishing Asia Pty Ltd.

  3. Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration.

    Science.gov (United States)

    Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T

    2014-07-01

    To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

  4. An ensemble deep learning based approach for red lesion detection in fundus images.

    Science.gov (United States)

    Orlando, José Ignacio; Prokofyeva, Elena; Del Fresno, Mariana; Blaschko, Matthew B

    2018-01-01

    Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our

  5. Human brain lesion-deficit inference remapped.

    Science.gov (United States)

    Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev

    2014-09-01

    Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant

  6. Hierarchical layered and semantic-based image segmentation using ergodicity map

    Science.gov (United States)

    Yadegar, Jacob; Liu, Xiaoqing

    2010-04-01

    Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects

  7. Structural lesions detected by magnetic resonance imaging in the spine of patients with spondyloarthritis - definitions, assessment system, and reference image set

    DEFF Research Database (Denmark)

    Østergaard, Mikkel; Maksymowych, Walter P; Pedersen, Susanne J

    2009-01-01

    are assessed at each vertebral endplate at all 23 spinal levels from C2/3 to L5/S1, whereas facet joint lesions are to be assessed by segmental level (cervical, thoracic, and lumbar). CONCLUSION: An anatomy-based set of definitions and an assessment system for structural lesions in the spine of patients......OBJECTIVE: There is no reliable and sensitive magnetic resonance imaging (MRI) assessment system for structural lesions in patients with spondyloarthritis (SpA). We sought to develop and illustrate a detailed anatomy-based set of MRI definitions and an assessment system for structural lesions...... in the spine of patients with SpA. METHODS: MRI definitions of different structural ("chronic") lesions at various anatomical locations in the spine, and an accompanying assessment system, were agreed by consensus within the Canada-Denmark MRI working group. Subsequently, a reference image set...

  8. Impact of Lesion Placement on Efficacy and Safety of Catheter-Based Radiofrequency Renal Denervation.

    Science.gov (United States)

    Mahfoud, Felix; Tunev, Stefan; Ewen, Sebastian; Cremers, Bodo; Ruwart, Jennifer; Schulz-Jander, Daniel; Linz, Dominik; Davies, Justin; Kandzari, David E; Whitbourn, Robert; Böhm, Michael; Melder, Robert J

    2015-10-20

    Insufficient procedural efficacy has been proposed to explain nonresponse to renal denervation (RDN). The aim of this study was to examine the impact of different patterns of lesion placements on the efficacy and consistency of catheter-based radiofrequency RDN in pigs. The impact of increasing number of lesions versus location of RDN was investigated in a porcine model (Group 1; n = 51). The effect of treating the main artery, the branches, and the 2 combined was compared in Group 2 (n = 48). The durability of response and safety of combined treatment of the main artery plus branches was examined in Group 3 (n = 16). Renal norepinephrine (NE) tissue content and renal cortical axon density were assessed. Increasing the number of RF lesions (4, 8, and 12) in the main renal artery was not sufficient to yield a clear dose-response relationship on NE content and axon density. In contrast, targeted treatment of the renal artery branches or distal segment of the main renal artery resulted in markedly less variability of response and significantly greater reduction of both NE and axon density than conventional treatment of only the main renal artery. Combination treatment (main artery plus branches) produced the greatest change in renal NE and axon density with the least heterogeneity. The changes were durable through 28 days post-treatment. These data provide the rationale for investigation of an optimized approach for RDN in future clinical studies. This may have profound implications for the clinical application of RDN, as this approach may not only achieve greater reductions in sympathetic activity but also reduce treatment effect variability. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  9. Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

    International Nuclear Information System (INIS)

    Gatos, I; Tsantis, S; Kagadis, G; Karamesini, M; Skouroliakou, A

    2015-01-01

    Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive

  10. Comparison of intravascular ultrasound and angiographic assessement of coronary reference segment size in patients with type 2 diabetes mellitus

    DEFF Research Database (Denmark)

    Jensen, Lisette Okkels; Thayssen, Per; Mintz, Gary S

    2008-01-01

    During percutaneous coronary intervention, the reference segment is assessed angiographically. This report described the discrepancy between angiographic and intravascular ultrasound (IVUS) assessment of reference segment size in patients with type 2 diabetes mellitus. Preintervention IVUS was used...... to study 62 de novo lesions in 41 patients with type 2 diabetes mellitus. The lesion site was the image slice with the smallest lumen cross-sectional area (CSA). The proximal and distal reference segments were the most normal-looking segments within 5 mm proximal and distal to the lesion. Plaque burden...... was measured as plaque CSA/external elastic membrane (EEM) CSA. Using IVUS, the reference lumen diameter was 2.80 +/- 0.42 mm and the reference EEM diameter was 4.17 +/- 0.56 mm. The angiographic reference diameter was 2.63 +/- 0.36 mm. Mean difference between the IVUS EEM diameter and angiographic reference...

  11. Segment-based Mass Customization: An Exploration of a New Conceptual Marketing Framework.

    Science.gov (United States)

    Jiang, Pingjun

    2000-01-01

    Suggests that the concept of mass customization should be seen as an integral part of market segmentation theory which offers the best way to satisfy consumers' unique needs and wants while yielding profits to companies. Proposes a new concept of "segment-based based mass customization," and offers a series of propositions which are…

  12. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

    Science.gov (United States)

    Alshehhi, Rasha; Marpu, Prashanth Reddy

    2017-04-01

    Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

  13. A web-based procedure for liver segmentation in CT images

    Science.gov (United States)

    Yuan, Rong; Luo, Ming; Wang, Luyao; Xie, Qingguo

    2015-03-01

    Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.

  14. Multi scales based sparse matrix spectral clustering image segmentation

    Science.gov (United States)

    Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

    2018-04-01

    In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

  15. Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

    Science.gov (United States)

    Pavliha, Denis; Mušič, Maja M; Serša, Gregor; Miklavčič, Damijan

    2013-01-01

    Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.

  16. Metabolically active tumour volume segmentation from dynamic [(18)F]FLT PET studies in non-small cell lung cancer.

    Science.gov (United States)

    Hoyng, Lieke L; Frings, Virginie; Hoekstra, Otto S; Kenny, Laura M; Aboagye, Eric O; Boellaard, Ronald

    2015-01-01

    Positron emission tomography (PET) with (18)F-3'-deoxy-3'-fluorothymidine ([(18)F]FLT) can be used to assess tumour proliferation. A kinetic-filtering (KF) classification algorithm has been suggested for segmentation of tumours in dynamic [(18)F]FLT PET data. The aim of the present study was to evaluate KF segmentation and its test-retest performance in [(18)F]FLT PET in non-small cell lung cancer (NSCLC) patients. Nine NSCLC patients underwent two 60-min dynamic [(18)F]FLT PET scans within 7 days prior to treatment. Dynamic scans were reconstructed with filtered back projection (FBP) as well as with ordered subsets expectation maximisation (OSEM). Twenty-eight lesions were identified by an experienced physician. Segmentation was performed using KF applied to the dynamic data set and a source-to-background corrected 50% threshold (A50%) was applied to the sum image of the last three frames (45- to 60-min p.i.). Furthermore, several adaptations of KF were tested. Both for KF and A50% test-retest (TRT) variability of metabolically active tumour volume and standard uptake value (SUV) were evaluated. KF performed better on OSEM- than on FBP-reconstructed PET images. The original KF implementation segmented 15 out of 28 lesions, whereas A50% segmented each lesion. Adapted KF versions, however, were able to segment 26 out of 28 lesions. In the best performing adapted versions, metabolically active tumour volume and SUV TRT variability was similar to those of A50%. KF misclassified certain tumour areas as vertebrae or liver tissue, which was shown to be related to heterogeneous [(18)F]FLT uptake areas within the tumour. For [(18)F]FLT PET studies in NSCLC patients, KF and A50% show comparable tumour volume segmentation performance. The KF method needs, however, a site-specific optimisation. The A50% is therefore a good alternative for tumour segmentation in NSCLC [(18)F]FLT PET studies in multicentre studies. Yet, it was observed that KF has the potential to subsegment

  17. PET/MR imaging of bone lesions - implications for PET quantification from imperfect attenuation correction

    International Nuclear Information System (INIS)

    Samarin, Andrei; Burger, Cyrill; Crook, David W.; Burger, Irene A.; Schmid, Daniel T.; Schulthess, Gustav K. von; Kuhn, Felix P.; Wollenweber, Scott D.

    2012-01-01

    Accurate attenuation correction (AC) is essential for quantitative analysis of PET tracer distribution. In MR, the lack of cortical bone signal makes bone segmentation difficult and may require implementation of special sequences. The purpose of this study was to evaluate the need for accurate bone segmentation in MR-based AC for whole-body PET/MR imaging. In 22 patients undergoing sequential PET/CT and 3-T MR imaging, modified CT AC maps were produced by replacing pixels with values of >100 HU, representing mostly bone structures, by pixels with a constant value of 36 HU corresponding to soft tissue, thereby simulating current MR-derived AC maps. A total of 141 FDG-positive osseous lesions and 50 soft-tissue lesions adjacent to bones were evaluated. The mean standardized uptake value (SUVmean) was measured in each lesion in PET images reconstructed once using the standard AC maps and once using the modified AC maps. Subsequently, the errors in lesion tracer uptake for the modified PET images were calculated using the standard PET image as a reference. Substitution of bone by soft tissue values in AC maps resulted in an underestimation of tracer uptake in osseous and soft tissue lesions adjacent to bones of 11.2 ± 5.4 % (range 1.5-30.8 %) and 3.2 ± 1.7 % (range 0.2-4 %), respectively. Analysis of the spine and pelvic osseous lesions revealed a substantial dependence of the error on lesion composition. For predominantly sclerotic spine lesions, the mean underestimation was 15.9 ± 3.4 % (range 9.9-23.5 %) and for osteolytic spine lesions, 7.2 ± 1.7 % (range 4.9-9.3 %), respectively. CT data simulating treating bone as soft tissue as is currently done in MR maps for PET AC leads to a substantial underestimation of tracer uptake in bone lesions and depends on lesion composition, the largest error being seen in sclerotic lesions. Therefore, depiction of cortical bone and other calcified areas in MR AC maps is necessary for accurate quantification of tracer uptake

  18. Atherectomy in complex infrainguinal lesions: a review.

    Science.gov (United States)

    Engelberger, S; van den Berg, J C

    2015-02-01

    In the femoropopliteal segment, endovascular revascularization techniques have gained the role as a first line treatment strategy. Nitinol stent placement has improved the short- and mid-term primary patency rates in most lesion types and is therefore widely applied. Stenting has several shortcomings as in-stent restenosis, stent fractures and foreign material being left behind in the vessel. The concept of atherectomy is plaque debulking. This results in a potential reduction of inflation pressure requirements in angioplasty. Stent placement and consecutive in-stent restenosis may be avoided. In this non systematic literature review, the performance of different atherectomy techniques, such as direct atherectomy, orbital atherectomy, laser debulking and rotational atherectomy in the treatment of complex femoropopliteal lesions, including long lesions, moderately to heavily calcified lesions as well as occlusions and in-stent restenosis, has been analyzed.

  19. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

    Science.gov (United States)

    Yan, Zhennan; Zhang, Shaoting; Tan, Chaowei; Qin, Hongxing; Belaroussi, Boubakeur; Yu, Hui Jing; Miller, Colin; Metaxas, Dimitris N

    2015-04-01

    Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Blood vessels segmentation of hatching eggs based on fully convolutional networks

    Science.gov (United States)

    Geng, Lei; Qiu, Ling; Wu, Jun; Xiao, Zhitao

    2018-04-01

    FCN, trained end-to-end, pixels-to-pixels, predict result of each pixel. It has been widely used for semantic segmentation. In order to realize the blood vessels segmentation of hatching eggs, a method based on FCN is proposed in this paper. The training datasets are composed of patches extracted from very few images to augment data. The network combines with lower layer and deconvolution to enables precise segmentation. The proposed method frees from the problem that training deep networks need large scale samples. Experimental results on hatching eggs demonstrate that this method can yield more accurate segmentation outputs than previous researches. It provides a convenient reference for fertility detection subsequently.

  1. Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

    Directory of Open Access Journals (Sweden)

    Huidong He

    2017-01-01

    Full Text Available The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE. Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.

  2. Lung lesion doubling times: values and variability based on method of volume determination

    International Nuclear Information System (INIS)

    Eisenbud Quint, Leslie; Cheng, Joan; Schipper, Matthew; Chang, Andrew C.; Kalemkerian, Gregory

    2008-01-01

    Purpose: To determine doubling times (DTs) of lung lesions based on volumetric measurements from thin-section CT imaging. Methods: Previously untreated patients with ≥ two thin-section CT scans showing a focal lung lesion were identified. Lesion volumes were derived using direct volume measurements and volume calculations based on lesion area and diameter. Growth rates (GRs) were compared by tissue diagnosis and measurement technique. Results: 54 lesions were evaluated including 8 benign lesions, 10 metastases, 3 lymphomas, 15 adenocarcinomas, 11 squamous carcinomas, and 7 miscellaneous lung cancers. Using direct volume measurements, median DTs were 453, 111, 15, 181, 139 and 137 days, respectively. Lung cancer DTs ranged from 23-2239 days. There were no significant differences in GRs among the different lesion types. There was considerable variability among GRs using different volume determination methods. Conclusions: Lung cancer doubling times showed a substantial range, and different volume determination methods gave considerably different DTs

  3. Using tracheal segments for replacement of cervical oesophagus: an experimental study.

    Science.gov (United States)

    Abbasidezfouli, Azizollah; Sharifi, Davood; Sasani, Farhang; Ansari, Damoon; Abarkar, Mohammad; Rahmanijoo, Nasrin; Abbasidezfouli, Golbahar; Sheikhy, Kambiz

    2012-03-01

    Segmental resection and anastomosis of oesophageal lesions are not performed as a routine clinical practice because of complications and associated problems, whereas tracheal resection and anastomosis are a routine clinical practice. In this experimental study, we resected a segment of cervical oesophagus and replaced it with a tracheal segment. In eight dogs (mixed races), weighing 20-30 kg, ageing 1-2 years, under general anaesthesia, through a cervical incision, 5 cm of cervical trachea was separated while preserving its attachments to surrounding fibroareolar tissues. Afterwards, 5 cm of the oesophagus was resected and replaced with a prepared segment of the trachea. Oral liquids were started at the first post-operative day; the animals were kept for 2 months and then euthanized. Quality of swallowing and voice were evaluated. After an autopsy, anastomoses were examined grossly and histopathologically. No complications occurred during surgery. Swallowing function and voice were normal in all eight dogs after the operation. No sign of aspiration was seen in clinical and radiographic examinations after starting oral diet. In autopsy examination, anastomoses were patent without narrowing or abnormal mucosal changes. Remarkable histopathological findings in replaced tracheal segments were squamous metaplasia, atrophy and degeneration of mucosal glands and degeneration of cartilages. Replacement of a segment of the oesophagus with an autogenous tracheal segment is a practical procedure with low complications and can probably be used for the treatment of cervical oesophageal lesions in human beings.

  4. Vessel-guided airway segmentation based on voxel classification

    DEFF Research Database (Denmark)

    Lo, Pechin Chien Pau; Sporring, Jon; Ashraf, Haseem

    2008-01-01

    This paper presents a method for improving airway tree segmentation using vessel orientation information. We use the fact that an airway branch is always accompanied by an artery, with both structures having similar orientations. This work is based on a  voxel classification airway segmentation...... method proposed previously. The probability of a voxel belonging to the airway, from the voxel classification method, is augmented with an orientation similarity measure as a criterion for region growing. The orientation similarity measure of a voxel indicates how similar is the orientation...... of the surroundings of a voxel, estimated based on a tube model, is to that of a neighboring vessel. The proposed method is tested on 20 CT images from different subjects selected randomly from a lung cancer screening study. Length of the airway branches from the results of the proposed method are significantly...

  5. Assessment of Feasibility to Use Computer Aided Texture Analysis Based Tool for Parametric Images of Suspicious Lesions in DCE-MR Mammography

    Directory of Open Access Journals (Sweden)

    Mehmet Cemil Kale

    2013-01-01

    Full Text Available Our aim was to analyze the feasibility of computer aided malignant tumor detection using the traditional texture analysis applied on two-compartment-based parameter pseudoimages of dynamic contrast-enhanced magnetic resonance (DCE-MR breast image data. A major contribution of this research will be the through-plane assessment capability. Texture analysis was performed on two-compartment-based pseudo images of DCE-MRI datasets of breast data of eight subjects. The resulting texture parameter pseudo images were inputted to a feedforward neural network classification system which uses the manual segmentations of a primary radiologist as a gold standard, and each voxel was assigned as malignant or nonmalignant. The classification results were compared with the lesions manually segmented by a second radiologist. Results show that the mean true positive fraction (TPF and false positive fraction (FPF performance of the classifier vs. primary radiologist is statistically as good as the mean TPF and FPF performance of the second radiologist vs. primary radiologist with a confidence interval of 95% using a one-sample -test with . In the experiment implemented on all of the eight subjects, all malignant tumors marked by the primary radiologist were classified to be malignant by the computer classifier. Our results have shown that neural network classification using the textural parameters for automated screening of two-compartment-based parameter pseudo images of DCE-MRI as input data can be a supportive tool for the radiologists in the preassessment stage to show the possible cancerous regions and in the postassessment stage to review the segmentations especially in analyzing complex DCE-MRI cases.

  6. A kind of color image segmentation algorithm based on super-pixel and PCNN

    Science.gov (United States)

    Xu, GuangZhu; Wang, YaWen; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

    2018-04-01

    Image segmentation is a very important step in the low-level visual computing. Although image segmentation has been studied for many years, there are still many problems. PCNN (Pulse Coupled Neural network) has biological background, when it is applied to image segmentation it can be viewed as a region-based method, but due to the dynamics properties of PCNN, many connectionless neurons will pulse at the same time, so it is necessary to identify different regions for further processing. The existing PCNN image segmentation algorithm based on region growing is used for grayscale image segmentation, cannot be directly used for color image segmentation. In addition, the super-pixel can better reserve the edges of images, and reduce the influences resulted from the individual difference between the pixels on image segmentation at the same time. Therefore, on the basis of the super-pixel, the original PCNN algorithm based on region growing is improved by this paper. First, the color super-pixel image was transformed into grayscale super-pixel image which was used to seek seeds among the neurons that hadn't been fired. And then it determined whether to stop growing by comparing the average of each color channel of all the pixels in the corresponding regions of the color super-pixel image. Experiment results show that the proposed algorithm for the color image segmentation is fast and effective, and has a certain effect and accuracy.

  7. Web data display system based on data segment technology of MDSplus

    International Nuclear Information System (INIS)

    Liu Rui; Zhang Ming; Wen Chuqiao; Zheng Wei; Zhuang Ge; Yu Kexun

    2014-01-01

    Long pulse operation is the main character of advanced Tokamak, so the technology of data storage and human-data interaction are vital for dealing with the large data generated in long pulse experiment. The Web data display system was designed. The system is based on the ASP. NET architecture, and it reads segmented-record data from MDSplus database by segmented-record technology and displays the data on Web page by using NI Measurement Studio control library. With the segmented-record technology, long pulse data could be divided into many small units, data segments. Users can read the certain data segments from the long pulse data according to their special needs. Also, the system develops an efficient strategy for reading segmented record data, showing the waveforms required by users accurately and quickly. The data display Web system was tested on J-TEXT Tokamak, and was proved to be reliable and efficient to achieve the initial design goal. (authors)

  8. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

    Science.gov (United States)

    Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

    2017-06-01

    Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

  9. Segmental lichen aureus: a report of two cases treated with methylprednisolone aceponate.

    Science.gov (United States)

    Moche, John; Glassman, Steven; Modi, Deepak; Grayson, Wayne

    2011-05-01

    Two cases of segmental lichen aureus with a response to topical 0.1% methylprednisolone aceponate ointment are reported. A 9-year-old child and a 23-year-old man showed complete resolution of their lesions following treatment with the latter after 7 months and 4 months, respectively. Lichen aureus is a rare form of the pigmented purpuric dermatoses characterized by golden-brown and lichenoid macules and papules, most often on the lower extremities. Segmental presentations have seldom been described. Histology showed a lichenoid infiltrate with extravasation of red blood cells and haemosiderin deposition. The aetiology is unclear and treatment is disappointing. We report an uncommon segmental presentation of lichen aureus with resolution of the lesions after treatment with a topical corticosteroid. © 2010 The Authors. Australasian Journal of Dermatology © 2010 The Australasian College of Dermatologists.

  10. Polyether based segmented copolymers with uniform aramid units

    NARCIS (Netherlands)

    Niesten, M.C.E.J.

    2000-01-01

    Segmented copolymers with short, glassy or crystalline hard segments and long, amorphous soft segments (multi-block copolymers) are thermoplastic elastomers (TPE’s). The hard segments form physical crosslinks for the amorphous (rubbery) soft segments. As a result, this type of materials combines

  11. SU-F-J-112: Clinical Feasibility Test of An RF Pulse-Based MRI Method for the Quantitative Fat-Water Segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Yee, S; Wloch, J; Pirkola, M [William Beaumont Hospital, Royal Oak, MI (United States)

    2016-06-15

    Purpose: Quantitative fat-water segmentation is important not only because of the clinical utility of fat-suppressed MRI images in better detecting lesions of clinical significance (in the midst of bright fat signal) but also because of the possible physical need, in which CT-like images based on the materials’ photon attenuation properties may have to be generated from MR images; particularly, as in the case of MR-only radiation oncology environment to obtain radiation dose calculation or as in the case of hybrid PET/MR modality to obtain attenuation correction map for the quantitative PET reconstruction. The majority of such fat-water quantitative segmentations have been performed by utilizing the Dixon’s method and its variations, which have to enforce the proper settings (often predefined) of echo time (TE) in the pulse sequences. Therefore, such methods have been unable to be directly combined with those ultrashort TE (UTE) sequences that, taking the advantage of very low TE values (∼ 10’s microsecond), might be beneficial to directly detect bones. Recently, an RF pulse-based method (http://dx.doi.org/10.1016/j.mri.2015.11.006), termed as PROD pulse method, was introduced as a method of quantitative fat-water segmentation that does not have to depend on predefined TE settings. Here, the clinical feasibility of this method is verified in brain tumor patients by combining the PROD pulse with several sequences. Methods: In a clinical 3T MRI, the PROD pulse was combined with turbo spin echo (e.g. TR=1500, TE=16 or 60, ETL=15) or turbo field echo (e.g. TR=5.6, TE=2.8, ETL=12) sequences without specifying TE values. Results: The fat-water segmentation was possible without having to set specific TE values. Conclusion: The PROD pulse method is clinically feasible. Although not yet combined with UTE sequences in our laboratory, the method is potentially compatible with UTE sequences, and thus, might be useful to directly segment fat, water, bone and air.

  12. Comparing culprit lesions in ST-segment elevation and non-ST-segment elevation acute coronary syndrome with 64-slice multidetector computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Huang, W.-C. [School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: w.c.huang@yahoo.com.tw; Liu, C.-P. [School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: cpliu@isca.vghks.gov.tw; Wu, M.-T. [School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: wu.mingting@gmail.com; Mar, G.-Y. [Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: gymar@isca.vghks.gov.tw; Lin, S.-K. [Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: skyii89@yahoo.com.tw; Hsiao, S.-H. [Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: a841120@ms3.hinet.net; Lin, S.-L. [School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: sllin@isca.vghks.gov.tw; Chiou, K.-R. [School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Cardiovascular Medical Center, Kaohsiung Veterans General Hospital, No. 386, Dar-Chung First Road, Kaohsiung, Taiwan (China)], E-mail: krchiou@isca.vghks.gov.tw

    2010-01-15

    Background: Classifying acute coronary syndrome (ACS) as ST elevation ACS (STE-ACS) or non-ST elevation ACS (NSTE-ACS) is critical for clinical prognosis and therapeutic decision-making. Assessing the differences in composition and configuration of culprit lesions between STE-ACS and NSTE-ACS can clarify their pathophysiologic differences. Objective: This study focused on evaluating the ability of 64-slice multidetector computed tomography (MDCT) to investigate these differences in culprit lesions in patients with STE-ACS and NSTE-ACS. Methods: Of 161 ACS cases admitted, 120 who fit study criteria underwent MDCT and conventional coronary angiography. The following MDCT data were analyzed: calcium volume, Agatston calcium scores, plaque area, plaque burden, remodeling index, and plaque density. Results: The MDCT angiography had a good correlation with conventional coronary angiography regarding the stenotic severity of culprit lesions (r = 0.86, p < 0.001). The STE-ACS culprit lesions (n = 54) had significantly higher luminal area stenosis (78.6 {+-} 21.2% vs. 66.7 {+-} 23.9%, p = 0.006), larger plaque burden (0.91 {+-} 0.10 vs. 0.84 {+-} 0.12, p = 0.007) and remodeling index (1.28 {+-} 0.34 vs. 1.16 {+-} 0.22, p = 0.021) than those with NSTE-ACS (n = 66). The percentage of expanding remodeling index (remodeling index >1.05) was significantly higher in the STE-ACS group (81.5% vs. 63.6%, p = 0.031). The patients with STE-ACS had significantly lower MDCT density of culprit lesions than patients with NSTE-ACS (25.8 {+-} 13.9 HU vs. 43.5 {+-} 19.1 HU, p < 0.001). Conclusions: Sixty-four-slice MDCT can accurately evaluate the stenotic severity and composition of culprit lesions in selected patients with either STE-ACS or NSTE-ACS. Culprit lesions in NSTE-ACS patients had significantly lower luminal area stenosis, plaque burden, remodeling index and higher MDCT density, which possibly reflect differences in the composition of vulnerable culprit plaques and thrombi.

  13. A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Fangzhao Li

    2018-01-01

    Full Text Available Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

  14. Topological leakage detection and freeze-and-grow propagation for improved CT-based airway segmentation

    Science.gov (United States)

    Nadeem, Syed Ahmed; Hoffman, Eric A.; Sieren, Jered P.; Saha, Punam K.

    2018-03-01

    Numerous large multi-center studies are incorporating the use of computed tomography (CT)-based characterization of the lung parenchyma and bronchial tree to understand chronic obstructive pulmonary disease status and progression. To the best of our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. A failure in even a fraction of segmentation results necessitates manual revision of all segmentation masks which is laborious considering the thousands of image data sets evaluated in large studies. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and freeze-and-grow propagation. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity-based connectivity and a freeze-and-grow propagation algorithm to iteratively grow the airway tree starting from an initial seed inside the trachea. It begins with a conservative parameter and then, gradually shifts toward more generous parameter values. The method was applied on chest CT scans of fifteen subjects at total lung capacity. Airway segmentation results were qualitatively assessed and performed comparably to established airway segmentation method with no major visual leakages.

  15. 18FDG PET scanning of benign and malignant musculoskeletal lesions

    International Nuclear Information System (INIS)

    Feldman, Frieda; Heertum, Ronald van; Manos, Chitra

    2003-01-01

    To describe the technique, applications and advantages of 18 FDG PET scanning in detection, analysis and management of musculoskeletal lesions.Design and patients Forty-five patients (19 males,26 females) aged 9 to 81 years had radiographs, routine radionuclide scans, CT and/or MRI of clinically suspected active benign or malignant musculoskeletal lesions. 18 FDG scans with a Siemens ECAT EXACT 921 dedicated PET unit (Knoxville, Tenn.) and FWH=6 mm images acquired as a 5-6 bed examination (6 min emission and 4 min transmission) used OSEM iterative reconstruction with segmented transmission attenuation correction and a Gaussian filter (cutoff 6.7 mm). Region of interest (ROI) 3 x 3 pixel image analysis based on transverse whole body images (slice thickness 3.37 mm) generated Maximum Standard Uptake Values (Max SUV) with a cutoff of 2.0 used to distinguish benign and malignant lesions. Thirty-nine studies were available for SUV ROI analysis. Overall sensitivity for differentiating malignant from benign osseous and non-osseous lesions was 91.7% (22/24), overall specificity was 100% (11/11) with an accuracy of 91.7%. All aggressive lesions had a Max SUV >2.0. Data separating benign from malignant lesions and aggressive from benign lesions were statistically significant (P 18 FDG PET contributes unique information regarding metabolism of musculoskeletal lesions. By supplying a physiologic basis for more informed treatment and management, it influences prognosis and survival. Moreover, since residual, recurrent or metastatic tumors can be simultaneously documented on a single whole body scan, PET may theoretically prove to be cost-effective. (orig.)

  16. A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

    Directory of Open Access Journals (Sweden)

    A. Hadavand

    2015-12-01

    Full Text Available In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS, a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

  17. MITK-based segmentation of co-registered MRI for subject-related regional anesthesia simulation

    Science.gov (United States)

    Teich, Christian; Liao, Wei; Ullrich, Sebastian; Kuhlen, Torsten; Ntouba, Alexandre; Rossaint, Rolf; Ullisch, Marcus; Deserno, Thomas M.

    2008-03-01

    With a steadily increasing indication, regional anesthesia is still trained directly on the patient. To develop a virtual reality (VR)-based simulation, a patient model is needed containing several tissues, which have to be extracted from individual magnet resonance imaging (MRI) volume datasets. Due to the given modality and the different characteristics of the single tissues, an adequate segmentation can only be achieved by using a combination of segmentation algorithms. In this paper, we present a framework for creating an individual model from MRI scans of the patient. Our work splits in two parts. At first, an easy-to-use and extensible tool for handling the segmentation task on arbitrary datasets is provided. The key idea is to let the user create a segmentation for the given subject by running different processing steps in a purposive order and store them in a segmentation script for reuse on new datasets. For data handling and visualization, we utilize the Medical Imaging Interaction Toolkit (MITK), which is based on the Visualization Toolkit (VTK) and the Insight Segmentation and Registration Toolkit (ITK). The second part is to find suitable segmentation algorithms and respectively parameters for differentiating the tissues required by the RA simulation. For this purpose, a fuzzy c-means clustering algorithm combined with mathematical morphology operators and a geometric active contour-based approach is chosen. The segmentation process itself aims at operating with minimal user interaction, and the gained model fits the requirements of the simulation. First results are shown for both, male and female MRI of the pelvis.

  18. Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

    Science.gov (United States)

    Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

    2018-04-01

    Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

  19. Smart markers for watershed-based cell segmentation.

    Directory of Open Access Journals (Sweden)

    Can Fahrettin Koyuncu

    Full Text Available Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain-specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have potential to greatly improve segmentation results. In this work, we propose a new approach for the effective segmentation of live cells from phase contrast microscopy. This approach introduces a new set of "smart markers" for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain-specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1,954 cells. The experimental results demonstrate that this approach, which uses the proposed definition of smart markers, is quite effective in identifying better markers compared to its counterparts. This will, in turn, be effective in improving the segmentation performance of a marker-controlled watershed algorithm.

  20. Smart markers for watershed-based cell segmentation.

    Science.gov (United States)

    Koyuncu, Can Fahrettin; Arslan, Salim; Durmaz, Irem; Cetin-Atalay, Rengul; Gunduz-Demir, Cigdem

    2012-01-01

    Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain-specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have potential to greatly improve segmentation results. In this work, we propose a new approach for the effective segmentation of live cells from phase contrast microscopy. This approach introduces a new set of "smart markers" for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain-specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1,954 cells. The experimental results demonstrate that this approach, which uses the proposed definition of smart markers, is quite effective in identifying better markers compared to its counterparts. This will, in turn, be effective in improving the segmentation performance of a marker-controlled watershed algorithm.

  1. A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

    Directory of Open Access Journals (Sweden)

    Yaozhong Luo

    2017-01-01

    Full Text Available Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%, the second highest TPVF (85.34%, and the second lowest FPVF (4.48%.

  2. Markerless tracking in nuclear power plants. A line segment-based approach

    International Nuclear Information System (INIS)

    Ishii, Hirotake; Kimura, Taro; Tokumaru, Hiroki; Shimoda, Hiroshi; Koda, Yuya

    2017-01-01

    To develop augmented reality-based support systems, a tracking method that measures the camera's position and orientation in real time is indispensable. A relocalization is one step that is used to (re)start the tracking. A line-segment-based relocalization method that uses a RGB-D camera and coarse-to-fine approach was developed and evaluated for this study. In the preparation stage, the target environment is scanned with a RGB-D camera. Line segments are recognized. Then three-dimensional positions of the line segments are calculated, and statistics of the line segments are calculated and stored in a database. In the relocalization stage, a few images that closely resemble the current RGB-D camera image are chosen from the database by comparing the statistics of the line segments. Then the most similar image is chosen using Normalized Cross-Correlation. This coarse-to-fine approach reduces the computational load to find the most similar image. The method was evaluated in the water purification room of the Fugen nuclear power plant. Results showed that the success rate of the relocalization is 93.6% and processing time is 45.7 ms per frame on average, which is promising for practical use. (author)

  3. Multimodal navigated skull base tumor resection using image-based vascular and cranial nerve segmentation: A prospective pilot study.

    Science.gov (United States)

    Dolati, Parviz; Gokoglu, Abdulkerim; Eichberg, Daniel; Zamani, Amir; Golby, Alexandra; Al-Mefty, Ossama

    2015-01-01

    Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge. To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured. Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases. Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients.

  4. How distributed processing produces false negatives in voxel-based lesion-deficit analyses.

    Science.gov (United States)

    Gajardo-Vidal, Andrea; Lorca-Puls, Diego L; Crinion, Jennifer T; White, Jitrachote; Seghier, Mohamed L; Leff, Alex P; Hope, Thomas M H; Ludersdorfer, Philipp; Green, David W; Bowman, Howard; Price, Cathy J

    2018-07-01

    In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxel-based multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesion-deficit mappings cannot, in isolation, be used to predict outcome in other patients

  5. Determination of the impact of RGB points cloud attribute quality on color-based segmentation process

    Directory of Open Access Journals (Sweden)

    Bartłomiej Kraszewski

    2015-06-01

    Full Text Available The article presents the results of research on the effect that radiometric quality of point cloud RGB attributes have on color-based segmentation. In the research, a point cloud with a resolution of 5 mm, received from FAROARO Photon 120 scanner, described the fragment of an office’s room and color images were taken by various digital cameras. The images were acquired by SLR Nikon D3X, and SLR Canon D200 integrated with the laser scanner, compact camera Panasonic TZ-30 and a mobile phone digital camera. Color information from images was spatially related to point cloud in FAROARO Scene software. The color-based segmentation of testing data was performed with the use of a developed application named “RGB Segmentation”. The application was based on public Point Cloud Libraries (PCL and allowed to extract subsets of points fulfilling the criteria of segmentation from the source point cloud using region growing method.Using the developed application, the segmentation of four tested point clouds containing different RGB attributes from various images was performed. Evaluation of segmentation process was performed based on comparison of segments acquired using the developed application and extracted manually by an operator. The following items were compared: the number of obtained segments, the number of correctly identified objects and the correctness of segmentation process. The best correctness of segmentation and most identified objects were obtained using the data with RGB attribute from Nikon D3X images. Based on the results it was found that quality of RGB attributes of point cloud had impact only on the number of identified objects. In case of correctness of the segmentation, as well as its error no apparent relationship between the quality of color information and the result of the process was found.[b]Keywords[/b]: terrestrial laser scanning, color-based segmentation, RGB attribute, region growing method, digital images, points cloud

  6. Automated segmentation of tumors on bone scans using anatomy-specific thresholding

    Science.gov (United States)

    Chu, Gregory H.; Lo, Pechin; Kim, Hyun J.; Lu, Peiyun; Ramakrishna, Bharath; Gjertson, David; Poon, Cheryce; Auerbach, Martin; Goldin, Jonathan; Brown, Matthew S.

    2012-03-01

    Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069.

  7. Active contour based segmentation of resected livers in CT images

    Science.gov (United States)

    Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

    2015-03-01

    The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

  8. Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques

    Directory of Open Access Journals (Sweden)

    Temitope Mapayi

    2015-01-01

    Full Text Available Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

  9. Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

    Directory of Open Access Journals (Sweden)

    Peng Shao

    2014-08-01

    Full Text Available The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.

  10. BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

    Science.gov (United States)

    Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

    2006-01-01

    Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

  11. Retina image–based optic disc segmentation

    Directory of Open Access Journals (Sweden)

    Ching-Lin Wang

    2016-05-01

    Full Text Available The change of optic disc can be used to diagnose many eye diseases, such as glaucoma, diabetic retinopathy and macular degeneration. Moreover, retinal blood vessel pattern is unique for human beings even for identical twins. It is a highly stable pattern in biometric identification. Since optic disc is the beginning of the optic nerve and main blood vessels in retina, it can be used as a reference point of identification. Therefore, optic disc segmentation is an important technique for developing a human identity recognition system and eye disease diagnostic system. This article hence presents an optic disc segmentation method to extract the optic disc from a retina image. The experimental results show that the optic disc segmentation method can give impressive results in segmenting the optic disc from a retina image.

  12. Algorithm of Defect Segmentation for AFP Based on Prepregs

    Directory of Open Access Journals (Sweden)

    CAI Zhiqiang

    2017-04-01

    Full Text Available In order to ensure the performance of the automated fiber placement forming parts, according to the homogeneity of the image of the prepreg surface along the fiber direction, a defect segmentation algorithm which was the combination of gray compensation and substraction algorithm based on image processing technology was proposed. The gray compensation matrix of image was used to compensate the gray image, and the maximum error point of the image matrix was eliminated according to the characteristics that the gray error obeys the normal distribution. The standard image was established, using the allowed deviation coefficient K as a criterion for substraction segmentation. Experiments show that the algorithm has good effect, fast speed in segmenting two kinds of typical laying defect of bubbles or foreign objects, and provides a good theoretical basis to realize automatic laying defect online monitoring.

  13. A region-based segmentation method for ultrasound images in HIFU therapy

    International Nuclear Information System (INIS)

    Zhang, Dong; Liu, Yu; Yang, Yan; Xu, Menglong; Yan, Yu; Qin, Qianqing

    2016-01-01

    Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient’s body. Therefore, the segmentation becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori

  14. A region-based segmentation method for ultrasound images in HIFU therapy

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Dong, E-mail: dongz@whu.edu.cn; Liu, Yu; Yang, Yan; Xu, Menglong; Yan, Yu [School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Qin, Qianqing [State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072 (China)

    2016-06-15

    Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient’s body. Therefore, the segmentation becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori

  15. Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata.

    Science.gov (United States)

    Ghanizadeh, Afshin; Abarghouei, Amir Atapour; Sinaie, Saman; Saad, Puteh; Shamsuddin, Siti Mariyam

    2011-07-01

    Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

  16. Oral rehabilitation of segmental mandibulectomy patient with osseointegrated dental implant

    Directory of Open Access Journals (Sweden)

    Archana Singh

    2014-01-01

    Full Text Available Surgical management of oral cancer lesions results in explicit aesthetic and functional disfigurement, including facial deformity, loss of hard and soft tissue, impaired speech, swallowing and mastication, which modify the patient′s self-image and quality-of-life. Recent advances in head and neck reconstruction techniques and dental implant based prosthetic rehabilitation may significantly improve the quality-of-life and self-esteem for such post-surgery patients. This clinical report describes rehabilitation of oral cancer patient having segmental mandibulectomy with implant-supported fixed partial denture.

  17. Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

    Science.gov (United States)

    Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

    2018-04-01

    Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

  18. Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

    Science.gov (United States)

    Hoffmann, Sebastian; Shutler, Jamie D.; Lobbes, Marc; Burgeth, Bernhard; Meyer-Bäse, Anke

    2013-12-01

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents an established method for the detection and diagnosis of breast lesions. While mass-like enhancing lesions can be easily categorized according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon, a majority of diagnostically challenging lesions, the so called non-mass-like enhancing lesions, remain both qualitatively as well as quantitatively difficult to analyze. Thus, the evaluation of kinetic and/or morphological characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have no well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these difficulties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphological descriptors separately and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions, and additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

  19. Skin Segmentation Based on Graph Cuts

    Institute of Scientific and Technical Information of China (English)

    HU Zhilan; WANG Guijin; LIN Xinggang; YAN Hong

    2009-01-01

    Skin segmentation is widely used in many computer vision tasks to improve automated visualiza-tion. This paper presents a graph cuts algorithm to segment arbitrary skin regions from images. The detected face is used to determine the foreground skin seeds and the background non-skin seeds with the color probability distributions for the foreground represented by a single Gaussian model and for the background by a Gaussian mixture model. The probability distribution of the image is used for noise suppression to alle-viate the influence of the background regions having skin-like colors. Finally, the skin is segmented by graph cuts, with the regional parameter y optimally selected to adapt to different images. Tests of the algorithm on many real wodd photographs show that the scheme accurately segments skin regions and is robust against illumination variations, individual skin variations, and cluttered backgrounds.

  20. Anatomy-based automatic detection and segmentation of major vessels in thoracic CTA images

    International Nuclear Information System (INIS)

    Zou Xiaotao; Liang Jianming; Wolf, M.; Salganicoff, M.; Krishnan, A.; Nadich, D.P.

    2007-01-01

    Existing approaches for automated computerized detection of pulmonary embolism (PE) using computed tomography angiography (CTA) usually focus on segmental and sub-segmental emboli. The goal of our current research is to extend our existing approach to automated detection of central PE. In order to detect central emboli, the major vessels must be first identified and segmented automatically. This submission presents an anatomy-based method for automatic computerized detection and segmentation of aortas and main pulmonary arteries in CTA images. (orig.)

  1. Speaker segmentation and clustering

    OpenAIRE

    Kotti, M; Moschou, V; Kotropoulos, C

    2008-01-01

    07.08.13 KB. Ok to add the accepted version to Spiral, Elsevier says ok whlile mandate not enforced. This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker...

  2. Design of segmented thermoelectric generator based on cost-effective and light-weight thermoelectric alloys

    International Nuclear Information System (INIS)

    Kim, Hee Seok; Kikuchi, Keiko; Itoh, Takashi; Iida, Tsutomu; Taya, Minoru

    2014-01-01

    Highlights: • Segmented thermoelectric (TE) module operating at 500 °C for combustion engine system. • Si based light-weight TE generator increases the specific power density [W/kg]. • Study of contact resistance at the bonding interfaces maximizing output power. • Accurate agreement of the theoretical predictions with experimental results. - Abstract: A segmented thermoelectric (TE) generator was designed with higher temperature segments composed of n-type Mg 2 Si and p-type higher manganese silicide (HMS) and lower temperature segments composed of n- and p-type Bi–Te based compounds. Since magnesium and silicon based TE alloys have low densities, they produce a TE module with a high specific power density that is suitable for airborne applications. A two-pair segmented π-shaped TE generator was assembled with low contact resistance materials across bonding interfaces. The peak specific power density of this generator was measured at 42.9 W/kg under a 498 °C temperature difference, which has a good agreement with analytical predictions

  3. Automatic comic page image understanding based on edge segment analysis

    Science.gov (United States)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  4. 3D Building Models Segmentation Based on K-Means++ Cluster Analysis

    Science.gov (United States)

    Zhang, C.; Mao, B.

    2016-10-01

    3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model) 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid) and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

  5. 3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS

    Directory of Open Access Journals (Sweden)

    C. Zhang

    2016-10-01

    Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

  6. Texture Segmentation Based on Wavelet and Kohonen Network for Remotely Sensed Images

    NARCIS (Netherlands)

    Chen, Z.; Feng, T.J.; Feng, T.J.; Houkes, Z.

    1999-01-01

    In this paper, an approach based on wavelet decomposition and Kohonen's self-organizing map is developed for image segmentation. After performing the 2D wavelet transform of image, some features are extracted for texture segmentation, and the Kohonen neural network is used to accomplish feature

  7. CT-based needle marking of superficial intracranial lesions for minimal invasive neurosurgery

    International Nuclear Information System (INIS)

    Marquardt, G.; Wolff, R.; Schick, U.; Lorenz, R.

    2000-01-01

    A CT-based method of marking superficial intracranial lesions with a needle is presented. This form of neuronavigation can be applied in every neurosurgical centre. Owing to its rapid application it is also suitable for cases of emergency. The neurosurgical approach can be centred precisely over this lesion providing for a minimally invasive operation. The method has proved its efficacy in numerous cases of haematomas and cystic lesions. (author)

  8. An Alternative to Chaid Segmentation Algorithm Based on Entropy.

    Directory of Open Access Journals (Sweden)

    María Purificación Galindo Villardón

    2010-07-01

    Full Text Available The CHAID (Chi-Squared Automatic Interaction Detection treebased segmentation technique has been found to be an effective approach for obtaining meaningful segments that are predictive of a K-category (nominal or ordinal criterion variable. CHAID was designed to detect, in an automatic way, the  nteraction between several categorical or ordinal predictors in explaining a categorical response, but, this may not be true when Simpson’s paradox is present. This is due to the fact that CHAID is a forward selection algorithm based on the marginal counts. In this paper we propose a backwards elimination algorithm that starts with the full set of predictors (or full tree and eliminates predictors progressively. The elimination procedure is based on Conditional Independence contrasts using the concept of entropy. The proposed procedure is compared to CHAID.

  9. Image segmentation with a novel regularized composite shape prior based on surrogate study

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States)

    2016-05-15

    Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.

  10. Image segmentation with a novel regularized composite shape prior based on surrogate study

    International Nuclear Information System (INIS)

    Zhao, Tingting; Ruan, Dan

    2016-01-01

    Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.

  11. Anatomic distribution of culprit lesions in patients with non-ST-segment elevation myocardial infarction and normal ECG.

    Science.gov (United States)

    Moustafa, Abdelmoniem; Abi-Saleh, Bernard; El-Baba, Mohammad; Hamoui, Omar; AlJaroudi, Wael

    2016-02-01

    In patients presenting with non-ST-elevation myocardial infarction (NSTEMI), left anterior descending (LAD) coronary artery and three-vessel disease are the most commonly encountered culprit lesions in the presence of ST depression, while one third of patients with left circumflex (LCX) artery related infarction have normal ECG. We sought to determine the predictors of presence of culprit lesion in NSTEMI patients based on ECG, echocardiographic, and clinical characteristics. Patients admitted to the coronary care unit with the diagnosis of NSTEMI between June 2012 and December 2013 were retrospectively identified. Admission ECG was interpreted by an electrophysiologist that was blinded to the result of the coronary angiogram. Patients were dichotomized into either normal or abnormal ECG group. The primary endpoint was presence of culprit lesion. Secondary endpoints included length of stay, re-hospitalization within 60 days, and in-hospital mortality. A total of 118 patients that were identified; 47 with normal and 71 with abnormal ECG. At least one culprit lesion was identified in 101 patients (86%), and significantly more among those with abnormal ECG (91.5% vs. 76.6%, P=0.041).The LAD was the most frequently detected culprit lesion in both groups. There was a higher incidence of two and three-vessel disease in the abnormal ECG group (P=0.041).On the other hand, there was a trend of higher LCX involvement (25% vs. 13.8%, P=0.18) and more normal coronary arteries in the normal ECG group (23.4% vs. 8.5%, P=0.041). On multivariate analysis, prior history of coronary artery disease (CAD) [odds ratio (OR) 6.4 (0.8-52)], male gender [OR 5.0 (1.5-17)], and abnormal admission ECG [OR 3.6 (1.12-12)], were independent predictors of a culprit lesion. There was no difference in secondary endpoints between those with normal and abnormal ECG. Among patients presenting with NSTEMI, prior history of CAD, male gender and abnormal admission ECG were independent predictors of a

  12. Comparing Individual Tree Segmentation Based on High Resolution Multispectral Image and Lidar Data

    Science.gov (United States)

    Xiao, P.; Kelly, M.; Guo, Q.

    2014-12-01

    This study compares the use of high-resolution multispectral WorldView images and high density Lidar data for individual tree segmentation. The application focuses on coniferous and deciduous forests in the Sierra Nevada Mountains. The tree objects are obtained in two ways: a hybrid region-merging segmentation method with multispectral images, and a top-down and bottom-up region-growing method with Lidar data. The hybrid region-merging method is used to segment individual tree from multispectral images. It integrates the advantages of global-oriented and local-oriented region-merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region. The merging iterations are constrained within the local vicinity, thus the segmentation is accelerated and can reflect the local context. The top-down region-growing method is adopted in coniferous forest to delineate individual tree from Lidar data. It exploits the spacing between the tops of trees to identify and group points into a single tree based on simple rules of proximity and likely tree shape. The bottom-up region-growing method based on the intensity and 3D structure of Lidar data is applied in deciduous forest. It segments tree trunks based on the intensity and topological relationships of the points, and then allocate other points to exact tree crowns according to distance. The accuracies for each method are evaluated with field survey data in several test sites, covering dense and sparse canopy. Three types of segmentation results are produced: true positive represents a correctly segmented individual tree, false negative represents a tree that is not detected and assigned to a nearby tree, and false positive represents that a point or pixel cluster is segmented as a tree that does not in fact exist. They respectively represent correct-, under-, and over-segmentation. Three types of index are compared for segmenting individual tree

  13. Semiautomated segmentation and analysis of retinal layers in three-dimensional spectral-domain optical coherence tomography images of patients with atrophic age-related macular degeneration.

    Science.gov (United States)

    Hu, Zhihong; Shi, Yue; Nandanan, Kiran; Sadda, Srinivas R

    2017-01-01

    Historically, regular drusen and geographic atrophy (GA) have been recognized as the hallmarks of nonneovascular age-related macular degeneration (AMD). Recent imaging developments have revealed another distinct nonneovascular AMD phenotype, reticular pseudodrusen (RPD). We develop an approach to semiautomatically quantify retinal surfaces associated with various AMD lesions (i.e., regular drusen, RPD, and GA) in spectral domain (SD) optical coherence tomography (OCT) images. More specifically, a graph-based algorithm was used to segment multiple retinal layers in SD-OCT volumes. Varying surface feasibility constraints based on the presegmentation were applied on the double-surface graph search to refine the surface segmentation. The thicknesses of these layers and their correlation with retinal functional measurements, including microperimetry (MP) sensitivity and visual acuity (VA), were investigated. The photoreceptor outer segment layer demonstrated significant thinning with a reduction in MP sensitivity and VA score when atrophic AMD lesions were present. Regular drusen and RPD were separately segmented on SD-OCT images to allow their characteristics and distribution to be studied separately. The mean thickness of regular drusen was found to significantly correlate with the VA score. RPD appeared to be distributed evenly throughout the macula and regular drusen appeared to be more concentrated centrally.

  14. Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

    Science.gov (United States)

    Roy, Sharmili; Chi, Yanling; Liu, Jimin; Venkatesh, Sudhakar K; Brown, Michael S

    2014-11-01

    Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a 3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull's eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system's query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

  15. Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment

    Directory of Open Access Journals (Sweden)

    Muhammad Arsalan

    2017-11-01

    Full Text Available Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN; which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II training database (selected from the UBIRIS.v2 database and mobile iris challenge evaluation (MICHE dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.

  16. Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.

    Science.gov (United States)

    Yasmin, Shamsunnahar; Eluru, Naveen

    2016-10-01

    The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Whole vertebral bone segmentation method with a statistical intensity-shape model based approach

    Science.gov (United States)

    Hanaoka, Shouhei; Fritscher, Karl; Schuler, Benedikt; Masutani, Yoshitaka; Hayashi, Naoto; Ohtomo, Kuni; Schubert, Rainer

    2011-03-01

    An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939 mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar vertebrae.

  18. Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Zaidi, Habib [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva (Switzerland); Geneva University, Geneva Neuroscience Center, Geneva (Switzerland); University of Groningen, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen (Netherlands); Abdoli, Mehrsima [University of Groningen, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen (Netherlands); Fuentes, Carolina Llina [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva (Switzerland); Naqa, Issam M.El [McGill University, Department of Medical Physics, Montreal (Canada)

    2012-05-15

    Several methods have been proposed for the segmentation of {sup 18}F-FDG uptake in PET. In this study, we assessed the performance of four categories of {sup 18}F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark. Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3-T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected ''en bloc'', frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference. The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (-5.9 {+-} 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26-0.72) for the overlap index. The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs

  19. Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

    Science.gov (United States)

    Plassard, Andrew J.; McHugo, Maureen; Heckers, Stephan; Landman, Bennett A.

    2017-02-01

    Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p develop a robust tool for measurement of the hippocampus and other temporal lobe structures.

  20. Multiatlas segmentation of thoracic and abdominal anatomy with level set-based local search.

    Science.gov (United States)

    Schreibmann, Eduard; Marcus, David M; Fox, Tim

    2014-07-08

    Segmentation of organs at risk (OARs) remains one of the most time-consuming tasks in radiotherapy treatment planning. Atlas-based segmentation methods using single templates have emerged as a practical approach to automate the process for brain or head and neck anatomy, but pose significant challenges in regions where large interpatient variations are present. We show that significant changes are needed to autosegment thoracic and abdominal datasets by combining multi-atlas deformable registration with a level set-based local search. Segmentation is hierarchical, with a first stage detecting bulk organ location, and a second step adapting the segmentation to fine details present in the patient scan. The first stage is based on warping multiple presegmented templates to the new patient anatomy using a multimodality deformable registration algorithm able to cope with changes in scanning conditions and artifacts. These segmentations are compacted in a probabilistic map of organ shape using the STAPLE algorithm. Final segmentation is obtained by adjusting the probability map for each organ type, using customized combinations of delineation filters exploiting prior knowledge of organ characteristics. Validation is performed by comparing automated and manual segmentation using the Dice coefficient, measured at an average of 0.971 for the aorta, 0.869 for the trachea, 0.958 for the lungs, 0.788 for the heart, 0.912 for the liver, 0.884 for the kidneys, 0.888 for the vertebrae, 0.863 for the spleen, and 0.740 for the spinal cord. Accurate atlas segmentation for abdominal and thoracic regions can be achieved with the usage of a multi-atlas and perstructure refinement strategy. To improve clinical workflow and efficiency, the algorithm was embedded in a software service, applying the algorithm automatically on acquired scans without any user interaction.

  1. Analysis of lesions in patients with unilateral tactile agnosia using cytoarchitectonic probabilistic maps.

    Science.gov (United States)

    Hömke, Lars; Amunts, Katrin; Bönig, Lutz; Fretz, Christian; Binkofski, Ferdinand; Zilles, Karl; Weder, Bruno

    2009-05-01

    We propose a novel methodical approach to lesion analyses involving high-resolution MR images in combination with probabilistic cytoarchitectonic maps. 3D-MR images of the whole brain and the manually segmented lesion mask are spatially normalized to the reference brain of a stereotaxic probabilistic cytoarchitectonic atlas using a multiscale registration algorithm based on an elastic model. The procedure is demonstrated in three patients suffering from aperceptive tactile agnosia of the right hand due to chronic infarction of the left parietal cortex. Patient 1 presents a lesion in areas of the postcentral sulcus, Patient 3 in areas of the superior parietal lobule and adjacent intraparietal sulcus, and Patient 2 lesions in both regions. On the basis of neurobehavioral data, we conjectured degradation of sequential elementary sensory information processing within the postcentral gyrus, impeding texture recognition in Patients 1 and 2, and disturbed kinaesthetic information processing in the posterior parietal lobe, causing degraded shape recognition in the patients 2 and 3. The involvement of Brodmann areas 4a, 4p, 3a, 3b, 1, 2, and areas IP1 and IP2 of the intraparietal sulcus was assessed in terms of the voxel overlap between the spatially transformed lesion masks and the 50%-isocontours of the cytoarchitectonic maps. The disruption of the critical cytoarchitectonic areas and the impaired subfunctions, texture and shape recognition, relate as conjectured above. We conclude that the proposed method represents a promising approach to hypothesis-driven lesion analyses, yielding lesion-function correlates based on a cytoarchitectonic model. Finally, the lesion-function correlates are validated by functional imaging reference data. (c) 2008 Wiley-Liss, Inc.

  2. A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

    International Nuclear Information System (INIS)

    Martin, Spencer; Rodrigues, George; Gaede, Stewart; Brophy, Mark; Barron, John L; Beauchemin, Steven S; Palma, David; Louie, Alexander V; Yu, Edward; Yaremko, Brian; Ahmad, Belal

    2015-01-01

    This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development. (paper)

  3. A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

    Science.gov (United States)

    Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L.; Beauchemin, Steven S.; Rodrigues, George; Gaede, Stewart

    2015-02-01

    This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

  4. Central nervous system involvement in a case of segmental nevus depigmentosus

    Directory of Open Access Journals (Sweden)

    Ishita Majumdar

    2016-01-01

    Full Text Available Central nervous system involvement in segmental nevus depigmentosus (SND is rare. A 7-month-old boy having convulsion and segmental hypopigmented patch in the right inguinal region. Magnetic resonance imaging of brain showed bilateral periventricular white matter hypoplasia with prominent subarachnoid spaces and mild dilation of ventricles with mild left cerebral hemispheric atrophy. Association of SND with seizure and white matter lesion has been rarely reported.

  5. Automated seeding-based nuclei segmentation in nonlinear optical microscopy.

    Science.gov (United States)

    Medyukhina, Anna; Meyer, Tobias; Heuke, Sandro; Vogler, Nadine; Dietzek, Benjamin; Popp, Jürgen

    2013-10-01

    Nonlinear optical (NLO) microscopy based, e.g., on coherent anti-Stokes Raman scattering (CARS) or two-photon-excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy; there is a lack of robust and durable nuclei segmentation methods capable of accurate image processing in cases of variable image contrast, nuclear density, and type of investigated tissue. Nonetheless, such algorithms specifically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e.g., in pathology or intraoperatively for surgical guidance. In this paper, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian filter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods followed by the verification of the detected nuclei performs high average sensitivity and specificity when applied to various types of NLO microscopy images.

  6. Template-based automatic breast segmentation on MRI by excluding the chest region

    International Nuclear Information System (INIS)

    Lin, Muqing; Chen, Jeon-Hor; Wang, Xiaoyong; Su, Min-Ying; Chan, Siwa; Chen, Siping

    2013-01-01

    Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The

  7. Template-based automatic breast segmentation on MRI by excluding the chest region

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Muqing [Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 and National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, School of Medicine, Shenzhen University, 518060 China (China); Chen, Jeon-Hor [Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 and Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan (China); Wang, Xiaoyong; Su, Min-Ying, E-mail: msu@uci.edu [Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 (United States); Chan, Siwa [Department of Radiology, Taichung Veterans General Hospital, Taichung 40407, Taiwan (China); Chen, Siping [National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, School of Medicine, Shenzhen University, 518060 China (China)

    2013-12-15

    Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1

  8. Tracking the Evolution of Cerebral Gadolinium-Enhancing Lesions to Persistent T1 Black Holes in Multiple Sclerosis: Validation of a Semiautomated Pipeline.

    Science.gov (United States)

    Andermatt, Simon; Papadopoulou, Athina; Radue, Ernst-Wilhelm; Sprenger, Till; Cattin, Philippe

    2017-09-01

    Some gadolinium-enhancing multiple sclerosis (MS) lesions remain T1-hypointense over months ("persistent black holes, BHs") and represent areas of pronounced tissue loss. A reduced conversion of enhancing lesions to persistent BHs could suggest a favorable effect of a medication on tissue repair. However, the individual tracking of enhancing lesions can be very time-consuming in large clinical trials. We created a semiautomated workflow for tracking the evolution of individual MS lesions, to calculate the proportion of enhancing lesions becoming persistent BHs at follow-up. Our workflow automatically coregisters, compares, and detects overlaps between lesion masks at different time points. We tested the algorithm in a data set of Magnetic Resonance images (1.5 and 3T; spin-echo T1-sequences) from a phase 3 clinical trial (n = 1,272), in which all enhancing lesions and all BHs had been previously segmented at baseline and year 2. The algorithm analyzed the segmentation masks in a longitudinal fashion to determine which enhancing lesions at baseline turned into BHs at year 2. Images of 50 patients (192 enhancing lesions) were also reviewed by an experienced MRI rater, blinded to the algorithm results. In this MRI data set, there were no cases that could not be processed by the algorithm. At year 2, 417 lesions were classified as persistent BHs (417/1,613 = 25.9%). The agreement between the rater and the algorithm was > 98%. Due to the semiautomated procedure, this algorithm can be of great value in the analysis of large clinical trials, when a rater-based analysis would be time-consuming. Copyright © 2017 by the American Society of Neuroimaging.

  9. A gradient-based method for segmenting FDG-PET images: methodology and validation

    International Nuclear Information System (INIS)

    Geets, Xavier; Lee, John A.; Gregoire, Vincent; Bol, Anne; Lonneux, Max

    2007-01-01

    A new gradient-based method for segmenting FDG-PET images is described and validated. The proposed method relies on the watershed transform and hierarchical cluster analysis. To allow a better estimation of the gradient intensity, iteratively reconstructed images were first denoised and deblurred with an edge-preserving filter and a constrained iterative deconvolution algorithm. Validation was first performed on computer-generated 3D phantoms containing spheres, then on a real cylindrical Lucite phantom containing spheres of different volumes ranging from 2.1 to 92.9 ml. Moreover, laryngeal tumours from seven patients were segmented on PET images acquired before laryngectomy by the gradient-based method and the thresholding method based on the source-to-background ratio developed by Daisne (Radiother Oncol 2003;69:247-50). For the spheres, the calculated volumes and radii were compared with the known values; for laryngeal tumours, the volumes were compared with the macroscopic specimens. Volume mismatches were also analysed. On computer-generated phantoms, the deconvolution algorithm decreased the mis-estimate of volumes and radii. For the Lucite phantom, the gradient-based method led to a slight underestimation of sphere volumes (by 10-20%), corresponding to negligible radius differences (0.5-1.1 mm); for laryngeal tumours, the segmented volumes by the gradient-based method agreed with those delineated on the macroscopic specimens, whereas the threshold-based method overestimated the true volume by 68% (p = 0.014). Lastly, macroscopic laryngeal specimens were totally encompassed by neither the threshold-based nor the gradient-based volumes. The gradient-based segmentation method applied on denoised and deblurred images proved to be more accurate than the source-to-background ratio method. (orig.)

  10. Comparison of features response in texture-based iris segmentation

    CSIR Research Space (South Africa)

    Bachoo, A

    2009-03-01

    Full Text Available the Fisher linear discriminant and the iris region of interest is extracted. Four texture description methods are compared for segmenting iris texture using a region based pattern classification approach: Grey Level Co-occurrence Matrix (GLCM), Discrete...

  11. Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics

    Energy Technology Data Exchange (ETDEWEB)

    Xie, Qiuliang; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California Los Angeles, California 90095 (United States)

    2014-04-15

    Purpose: To improve the efficiency of atlas-based segmentation without compromising accuracy, and to demonstrate the validity of the proposed method on MRI-based prostate segmentation application. Methods: Accurate and efficient automatic structure segmentation is an important task in medical image processing. Atlas-based methods, as the state-of-the-art, provide good segmentation at the cost of a large number of computationally intensive nonrigid registrations, for anatomical sites/structures that are subject to deformation. In this study, the authors propose to utilize a combination of global, regional, and local metrics to improve the accuracy yet significantly reduce the number of required nonrigid registrations. The authors first perform an affine registration to minimize the global mean squared error (gMSE) to coarsely align each atlas image to the target. Subsequently, atarget-specific regional MSE (rMSE), demonstrated to be a good surrogate for dice similarity coefficient (DSC), is used to select a relevant subset from the training atlas. Only within this subset are nonrigid registrations performed between the training images and the target image, to minimize a weighted combination of gMSE and rMSE. Finally, structure labels are propagated from the selected training samples to the target via the estimated deformation fields, and label fusion is performed based on a weighted combination of rMSE and local MSE (lMSE) discrepancy, with proper total-variation-based spatial regularization. Results: The proposed method was applied to a public database of 30 prostate MR images with expert-segmented structures. The authors’ method, utilizing only eight nonrigid registrations, achieved a performance with a median/mean DSC of over 0.87/0.86, outperforming the state-of-the-art full-fledged atlas-based segmentation approach of which the median/mean DSC was 0.84/0.82 when applying to their data set. Conclusions: The proposed method requires a fixed number of nonrigid

  12. Interactive segmentation: a scalable superpixel-based method

    Science.gov (United States)

    Mathieu, Bérengère; Crouzil, Alain; Puel, Jean-Baptiste

    2017-11-01

    This paper addresses the problem of interactive multiclass segmentation of images. We propose a fast and efficient new interactive segmentation method called superpixel α fusion (SαF). From a few strokes drawn by a user over an image, this method extracts relevant semantic objects. To get a fast calculation and an accurate segmentation, SαF uses superpixel oversegmentation and support vector machine classification. We compare SαF with competing algorithms by evaluating its performances on reference benchmarks. We also suggest four new datasets to evaluate the scalability of interactive segmentation methods, using images from some thousand to several million pixels. We conclude with two applications of SαF.

  13. Assessment of automatic segmentation of teeth using a watershed-based method.

    Science.gov (United States)

    Galibourg, Antoine; Dumoncel, Jean; Telmon, Norbert; Calvet, Adèle; Michetti, Jérôme; Maret, Delphine

    2018-01-01

    Tooth 3D automatic segmentation (AS) is being actively developed in research and clinical fields. Here, we assess the effect of automatic segmentation using a watershed-based method on the accuracy and reproducibility of 3D reconstructions in volumetric measurements by comparing it with a semi-automatic segmentation(SAS) method that has already been validated. The study sample comprised 52 teeth, scanned with micro-CT (41 µm voxel size) and CBCT (76; 200 and 300 µm voxel size). Each tooth was segmented by AS based on a watershed method and by SAS. For all surface reconstructions, volumetric measurements were obtained and analysed statistically. Surfaces were then aligned using the SAS surfaces as the reference. The topography of the geometric discrepancies was displayed by using a colour map allowing the maximum differences to be located. AS reconstructions showed similar tooth volumes when compared with SAS for the 41 µm voxel size. A difference in volumes was observed, and increased with the voxel size for CBCT data. The maximum differences were mainly found at the cervical margins and incisal edges but the general form was preserved. Micro-CT, a modality used in dental research, provides data that can be segmented automatically, which is timesaving. AS with CBCT data enables the general form of the region of interest to be displayed. However, our AS method can still be used for metrically reliable measurements in the field of clinical dentistry if some manual refinements are applied.

  14. Segmentation of low‐cost high efficiency oxide‐based thermoelectric materials

    DEFF Research Database (Denmark)

    Le, Thanh Hung; Van Nong, Ngo; Linderoth, Søren

    2015-01-01

    Thermoelectric (TE) oxide materials have attracted great interest in advanced renewable energy research owing to the fact that they consist of abundant elements, can be manufactured by low-cost processing, sustain high temperatures, be robust and provide long lifetime. However, the low conversion...... efficiency of TE oxides has been a major drawback limiting these materials to broaden applications. In this work, theoretical calculations are used to predict how segmentation of oxide and semimetal materials, utilizing the benefits of both types of materials, can provide high efficiency, high temperature...... oxide-based segmented legs. The materials for segmentation are selected by their compatibility factors and their conversion efficiency versus material cost, i.e., “efficiency ratio”. Numerical modelling results showed that conversion efficiency could reach values of more than 10% for unicouples using...

  15. Breast tumor segmentation in high resolution x-ray phase contrast analyzer based computed tomography.

    Science.gov (United States)

    Brun, E; Grandl, S; Sztrókay-Gaul, A; Barbone, G; Mittone, A; Gasilov, S; Bravin, A; Coan, P

    2014-11-01

    Phase contrast computed tomography has emerged as an imaging method, which is able to outperform present day clinical mammography in breast tumor visualization while maintaining an equivalent average dose. To this day, no segmentation technique takes into account the specificity of the phase contrast signal. In this study, the authors propose a new mathematical framework for human-guided breast tumor segmentation. This method has been applied to high-resolution images of excised human organs, each of several gigabytes. The authors present a segmentation procedure based on the viscous watershed transform and demonstrate the efficacy of this method on analyzer based phase contrast images. The segmentation of tumors inside two full human breasts is then shown as an example of this procedure's possible applications. A correct and precise identification of the tumor boundaries was obtained and confirmed by manual contouring performed independently by four experienced radiologists. The authors demonstrate that applying the watershed viscous transform allows them to perform the segmentation of tumors in high-resolution x-ray analyzer based phase contrast breast computed tomography images. Combining the additional information provided by the segmentation procedure with the already high definition of morphological details and tissue boundaries offered by phase contrast imaging techniques, will represent a valuable multistep procedure to be used in future medical diagnostic applications.

  16. Strategic market segmentation

    Directory of Open Access Journals (Sweden)

    Maričić Branko R.

    2015-01-01

    Full Text Available Strategic planning of marketing activities is the basis of business success in modern business environment. Customers are not homogenous in their preferences and expectations. Formulating an adequate marketing strategy, focused on realization of company's strategic objectives, requires segmented approach to the market that appreciates differences in expectations and preferences of customers. One of significant activities in strategic planning of marketing activities is market segmentation. Strategic planning imposes a need to plan marketing activities according to strategically important segments on the long term basis. At the same time, there is a need to revise and adapt marketing activities on the short term basis. There are number of criteria based on which market segmentation is performed. The paper will consider effectiveness and efficiency of different market segmentation criteria based on empirical research of customer expectations and preferences. The analysis will include traditional criteria and criteria based on behavioral model. The research implications will be analyzed from the perspective of selection of the most adequate market segmentation criteria in strategic planning of marketing activities.

  17. Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

    Science.gov (United States)

    Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

    2017-12-01

    The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

  18. Alport’s syndrome with focal segmental glomerulosclerosis lesion – Pattern to recognize

    Directory of Open Access Journals (Sweden)

    Afnan A Alsahli

    2018-01-01

    Full Text Available The association between Alport’s syndrome (AS and focal segmental glomerulosclerosis (FSGS in the same patient is complex and rarely reported. We report a case of a 42-year-old male presenting with proteinuria, microscopic hematuria, elevated serum creatinine and hypertension with unremarkable physical examination apart from obesity. The renal biopsy showed well-established FSGS pattern of injury with mild interstitial fibrosis and tubular atrophy, while the electron microscopic examination demonstrated glomerular basement membranes (GBM changes compatible with AS. AS can be complicated by segmental glomerular scarring, which can mimic primary FSGS, while familial FSGS can result from mutations in collagen IV network of the GBM. This overlap can complicate histopathological interpretation of renal biopsy, which should be accompanied by mutational analysis for accurate diagnosis and proper therapeutic intervention.

  19. Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

    Directory of Open Access Journals (Sweden)

    Weimin Wang

    2016-11-01

    Full Text Available The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme—the Incremental Recursive Segmentation (IRIS, that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on three datasets acquired from a handheld Velodyne HDL-32E LiDAR device. The experimental results verify the efficiency of IRIS for any segmentation method and the advantages of SLCC for processing mobile LiDAR data.

  20. State-of-the-Art Methods for Brain Tissue Segmentation: A Review.

    Science.gov (United States)

    Dora, Lingraj; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

    2017-01-01

    Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.

  1. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

    Directory of Open Access Journals (Sweden)

    Pradipta Maji

    Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

  2. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

    Science.gov (United States)

    Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

    2018-05-01

    Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

  3. MRI of peripheral nerve lesions of the lower limbs

    Energy Technology Data Exchange (ETDEWEB)

    Lacour-Petit, M.C.; Ducreux, D. [Dept. of Neuroradiology, Hopital Bicetre, Kremlin-Bicetre (France); Lozeron, P. [Dept. of Neurology, Hopital Bicetre, Kremlin-Bicetre (France)

    2003-03-01

    Our aim is to illustrate the contribution of MRI to diagnosis of lesions of the lower-limb nerve trunks. We report six patients who had clinical and electrophysiological examination for a peroneal or tibial nerve palsy. MRI of the knee showed in three cases a nonenhancing cystic lesion of the peroneal nerve suggesting an intraneural ganglion cyst, confirmed by histological study in one case. One patient with known neurofibromatosis had an enhancing nodular lesion of the peroneal nerve compatible with a neurofibroma. Two patients had diffuse hypertrophy with high signal on T2-weighted images, without contrast enhancement of the sciatic nerve or its branches. These lesions were compatible with localised hypertrophic neuropathy. In one case, biopsy of the superficial branch of the peroneal nerve showed insignificant axonal degeneration. MRI can provide information about the size and site of the abnormal segment of a nerve before treatment and can be used to distinguish different patterns of focal lesion. (orig.)

  4. Color Image Segmentation Based on Statistics of Location and Feature Similarity

    Science.gov (United States)

    Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

    The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

  5. Comparison of Positron Emission Tomography Quantification Using Magnetic Resonance- and Computed Tomography-Based Attenuation Correction in Physiological Tissues and Lesions: A Whole-Body Positron Emission Tomography/Magnetic Resonance Study in 66 Patients.

    Science.gov (United States)

    Seith, Ferdinand; Gatidis, Sergios; Schmidt, Holger; Bezrukov, Ilja; la Fougère, Christian; Nikolaou, Konstantin; Pfannenberg, Christina; Schwenzer, Nina

    2016-01-01

    Attenuation correction (AC) in fully integrated positron emission tomography (PET)/magnetic resonance (MR) systems plays a key role for the quantification of tracer uptake. The aim of this prospective study was to assess the accuracy of standardized uptake value (SUV) quantification using MR-based AC in direct comparison with computed tomography (CT)-based AC of the same PET data set on a large patient population. Sixty-six patients (22 female; mean [SD], 61 [11] years) were examined by means of combined PET/CT and PET/MR (11C-choline, 18F-FDG, or 68Ga-DOTATATE) subsequently. Positron emission tomography images from PET/MR examinations were corrected with MR-derived AC based on tissue segmentation (PET(MR)). The same PET data were corrected using CT-based attenuation maps (μ-maps) derived from PET/CT after nonrigid registration of the CT to the MR-based μ-map (PET(MRCT)). Positron emission tomography SUVs were quantified placing regions of interest or volumes of interest in 6 different body regions as well as PET-avid lesions, respectively. The relative differences of quantitative PET values when using MR-based AC versus CT-based AC were varying depending on the organs and body regions assessed. In detail, the mean (SD) relative differences of PET SUVs were as follows: -7.8% (11.5%), blood pool; -3.6% (5.8%), spleen; -4.4% (5.6%)/-4.1% (6.2%), liver; -0.6% (5.0%), muscle; -1.3% (6.3%), fat; -40.0% (18.7%), bone; 1.6% (4.4%), liver lesions; -6.2% (6.8%), bone lesions; and -1.9% (6.2%), soft tissue lesions. In 10 liver lesions, distinct overestimations greater than 5% were found (up to 10%). In addition, overestimations were found in 2 bone lesions and 1 soft tissue lesion adjacent to the lung (up to 28.0%). Results obtained using different PET tracers show that MR-based AC is accurate in most tissue types, with SUV deviations generally of less than 10%. In bone, however, underestimations can be pronounced, potentially leading to inaccurate SUV quantifications. In

  6. A Decision-Tree-Based Algorithm for Speech/Music Classification and Segmentation

    Directory of Open Access Journals (Sweden)

    Lavner Yizhar

    2009-01-01

    Full Text Available We present an efficient algorithm for segmentation of audio signals into speech or music. The central motivation to our study is consumer audio applications, where various real-time enhancements are often applied. The algorithm consists of a learning phase and a classification phase. In the learning phase, predefined training data is used for computing various time-domain and frequency-domain features, for speech and music signals separately, and estimating the optimal speech/music thresholds, based on the probability density functions of the features. An automatic procedure is employed to select the best features for separation. In the test phase, initial classification is performed for each segment of the audio signal, using a three-stage sieve-like approach, applying both Bayesian and rule-based methods. To avoid erroneous rapid alternations in the classification, a smoothing technique is applied, averaging the decision on each segment with past segment decisions. Extensive evaluation of the algorithm, on a database of more than 12 hours of speech and more than 22 hours of music showed correct identification rates of 99.4% and 97.8%, respectively, and quick adjustment to alternating speech/music sections. In addition to its accuracy and robustness, the algorithm can be easily adapted to different audio types, and is suitable for real-time operation.

  7. [Cardiac Synchronization Function Estimation Based on ASM Level Set Segmentation Method].

    Science.gov (United States)

    Zhang, Yaonan; Gao, Yuan; Tang, Liang; He, Ying; Zhang, Huie

    At present, there is no accurate and quantitative methods for the determination of cardiac mechanical synchronism, and quantitative determination of the synchronization function of the four cardiac cavities with medical images has a great clinical value. This paper uses the whole heart ultrasound image sequence, and segments the left & right atriums and left & right ventricles of each frame. After the segmentation, the number of pixels in each cavity and in each frame is recorded, and the areas of the four cavities of the image sequence are therefore obtained. The area change curves of the four cavities are further extracted, and the synchronous information of the four cavities is obtained. Because of the low SNR of Ultrasound images, the boundary lines of cardiac cavities are vague, so the extraction of cardiac contours is still a challenging problem. Therefore, the ASM model information is added to the traditional level set method to force the curve evolution process. According to the experimental results, the improved method improves the accuracy of the segmentation. Furthermore, based on the ventricular segmentation, the right and left ventricular systolic functions are evaluated, mainly according to the area changes. The synchronization of the four cavities of the heart is estimated based on the area changes and the volume changes.

  8. Segmentation-Based And Segmentation-Free Methods for Spotting Handwritten Arabic Words

    OpenAIRE

    Ball , Gregory R.; Srihari , Sargur N.; Srinivasan , Harish

    2006-01-01

    http://www.suvisoft.com; Given a set of handwritten documents, a common goal is to search for a relevant subset. Attempting to find a query word or image in such a set of documents is called word spotting. Spotting handwritten words in documents written in the Latin alphabet, and more recently in Arabic, has received considerable attention. One issue is generating candidate word regions on a page. Attempting to definitely segment the document into such regions (automatic segmentation) can mee...

  9. Detection of small human cerebral cortical lesions with MRI under different levels of Gaussian smoothing: applications in epilepsy

    Science.gov (United States)

    Cantor-Rivera, Diego; Goubran, Maged; Kraguljac, Alan; Bartha, Robert; Peters, Terry

    2010-03-01

    The main objective of this study was to assess the effect of smoothing filter selection in Voxel-Based Morphometry studies on structural T1-weighted magnetic resonance images. Gaussian filters of 4 mm, 8 mm or 10 mm Full Width at High Maximum are commonly used, based on the assumption that the filter size should be at least twice the voxel size to obtain robust statistical results. The hypothesis of the presented work was that the selection of the smoothing filter influenced the detectability of small lesions in the brain. Mesial Temporal Sclerosis associated to Epilepsy was used as the case to demonstrate this effect. Twenty T1-weighted MRIs from the BrainWeb database were selected. A small phantom lesion was placed in the amygdala, hippocampus, or parahippocampal gyrus of ten of the images. Subsequently the images were registered to the ICBM/MNI space. After grey matter segmentation, a T-test was carried out to compare each image containing a phantom lesion with the rest of the images in the set. For each lesion the T-test was repeated with different Gaussian filter sizes. Voxel-Based Morphometry detected some of the phantom lesions. Of the three parameters considered: location,size, and intensity; it was shown that location is the dominant factor for the detection of the lesions.

  10. Adaptive Binary Arithmetic Coder-Based Image Feature and Segmentation in the Compressed Domain

    Directory of Open Access Journals (Sweden)

    Hsi-Chin Hsin

    2012-01-01

    Full Text Available Image compression is necessary in various applications, especially for efficient transmission over a band-limited channel. It is thus desirable to be able to segment an image in the compressed domain directly such that the burden of decompressing computation can be avoided. Motivated by the adaptive binary arithmetic coder (MQ coder of JPEG2000, we propose an efficient scheme to segment the feature vectors that are extracted from the code stream of an image. We modify the Compression-based Texture Merging (CTM algorithm to alleviate the influence of overmerging problem by making use of the rate distortion information. Experimental results show that the MQ coder-based image segmentation is preferable in terms of the boundary displacement error (BDE measure. It has the advantage of saving computational cost as the segmentation results even at low rates of bits per pixel (bpp are satisfactory.

  11. An evidence-based review for the management of cystic pancreatic lesions

    International Nuclear Information System (INIS)

    Planner, A.C.; Anderson, E.M.; Slater, A.; Phillips-Hughes, J.; Bungay, H.K.; Betts, M.

    2007-01-01

    The incidental finding of pancreatic cysts is becoming more common because of the increased use of cross-sectional imaging. As a result, the perspective from historical series of symptomatic patients is not always applicable to the current cohort of patients with cystic lesions in their pancreas. In this review, the characteristic radiological features that aid diagnosis are highlighted, and the complementary role of different imaging methods and the appropriate use of tissue sampling are identified. Based on the literature regarding the diagnostic role of imaging in characterizing cystic pancreatic lesions, it is possible to recommend a practical imaging algorithm for the diagnosis of cystic pancreatic lesions

  12. Natural history of chondroid skull base lesions - case report and review

    International Nuclear Information System (INIS)

    Schmidinger, A.; Rosahl, S.K.; Vorkapic, P.; Samii, M.

    2002-01-01

    Long-term follow-up reports on chondroid lesions of the skull base are rarely presented in the literature. There are virtually no data on natural growth rates of these tumors based on MRI obtained over a period of 10 years or longer. We followed a patient who has had such a lesion for more than 12 years. A non-progressive, slight abducens palsy has been the only associated symptom so far. Even though the patient was operated on for an additional intracranial arterio-venous malformation, clinical features and chromosomal testing excluded Maffucci's syndrome. The MRI follow-up in this case provides an extraordinary perspective on the natural history of chondroid skull base tumors. (orig.)

  13. A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

    Directory of Open Access Journals (Sweden)

    Miso Ju

    2018-05-01

    Full Text Available Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96% and execution time (i.e., real-time execution, even with low-contrast images obtained using a Kinect depth sensor.

  14. Breast tumor segmentation in high resolution x-ray phase contrast analyzer based computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Brun, E., E-mail: emmanuel.brun@esrf.fr [European Synchrotron Radiation Facility (ESRF), Grenoble 380000, France and Department of Physics, Ludwig-Maximilians University, Garching 85748 (Germany); Grandl, S.; Sztrókay-Gaul, A.; Gasilov, S. [Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, 81377 Munich (Germany); Barbone, G. [Department of Physics, Harvard University, Cambridge, Massachusetts 02138 (United States); Mittone, A.; Coan, P. [Department of Physics, Ludwig-Maximilians University, Garching 85748, Germany and Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, 81377 Munich (Germany); Bravin, A. [European Synchrotron Radiation Facility (ESRF), Grenoble 380000 (France)

    2014-11-01

    Purpose: Phase contrast computed tomography has emerged as an imaging method, which is able to outperform present day clinical mammography in breast tumor visualization while maintaining an equivalent average dose. To this day, no segmentation technique takes into account the specificity of the phase contrast signal. In this study, the authors propose a new mathematical framework for human-guided breast tumor segmentation. This method has been applied to high-resolution images of excised human organs, each of several gigabytes. Methods: The authors present a segmentation procedure based on the viscous watershed transform and demonstrate the efficacy of this method on analyzer based phase contrast images. The segmentation of tumors inside two full human breasts is then shown as an example of this procedure’s possible applications. Results: A correct and precise identification of the tumor boundaries was obtained and confirmed by manual contouring performed independently by four experienced radiologists. Conclusions: The authors demonstrate that applying the watershed viscous transform allows them to perform the segmentation of tumors in high-resolution x-ray analyzer based phase contrast breast computed tomography images. Combining the additional information provided by the segmentation procedure with the already high definition of morphological details and tissue boundaries offered by phase contrast imaging techniques, will represent a valuable multistep procedure to be used in future medical diagnostic applications.

  15. Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis

    Science.gov (United States)

    Mai, Fei; Chang, Chunqi; Liu, Wenqing; Xu, Weichao; Hung, Yeung S.

    2009-10-01

    Due to the inherent imperfections in the imaging process, fluorescence microscopy images often suffer from spurious intensity variations, which is usually referred to as intensity inhomogeneity, intensity non uniformity, shading or bias field. In this paper, a retrospective shading correction method for fluorescence microscopy Escherichia coli (E. Coli) images is proposed based on segmentation result. Segmentation and shading correction are coupled together, so we iteratively correct the shading effects based on segmentation result and refine the segmentation by segmenting the image after shading correction. A fluorescence microscopy E. Coli image can be segmented (based on its intensity value) into two classes: the background and the cells, where the intensity variation within each class is close to zero if there is no shading. Therefore, we make use of this characteristics to correct the shading in each iteration. Shading is mathematically modeled as a multiplicative component and an additive noise component. The additive component is removed by a denoising process, and the multiplicative component is estimated using a fast algorithm to minimize the intra-class intensity variation. We tested our method on synthetic images and real fluorescence E.coli images. It works well not only for visual inspection, but also for numerical evaluation. Our proposed method should be useful for further quantitative analysis especially for protein expression value comparison.

  16. Multidimensional segmentation of coronary intravascular ultrasound images using knowledge-based methods

    Science.gov (United States)

    Olszewski, Mark E.; Wahle, Andreas; Vigmostad, Sarah C.; Sonka, Milan

    2005-04-01

    In vivo studies of the relationships that exist among vascular geometry, plaque morphology, and hemodynamics have recently been made possible through the development of a system that accurately reconstructs coronary arteries imaged by x-ray angiography and intravascular ultrasound (IVUS) in three dimensions. Currently, the bottleneck of the system is the segmentation of the IVUS images. It is well known that IVUS images contain numerous artifacts from various sources. Previous attempts to create automated IVUS segmentation systems have suffered from either a cost function that does not include enough information, or from a non-optimal segmentation algorithm. The approach presented in this paper seeks to strengthen both of those weaknesses -- first by building a robust, knowledge-based cost function, and then by using a fully optimal, three-dimensional segmentation algorithm. The cost function contains three categories of information: a compendium of learned border patterns, information theoretic and statistical properties related to the imaging physics, and local image features. By combining these criteria in an optimal way, weaknesses associated with cost functions that only try to optimize a single criterion are minimized. This cost function is then used as the input to a fully optimal, three-dimensional, graph search-based segmentation algorithm. The resulting system has been validated against a set of manually traced IVUS image sets. Results did not show any bias, with a mean unsigned luminal border positioning error of 0.180 +/- 0.027 mm and an adventitial border positioning error of 0.200 +/- 0.069 mm.

  17. Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

    Science.gov (United States)

    Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

    2016-01-01

    Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

  18. Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods.

    Science.gov (United States)

    van der Kleij, Lisa A; de Bresser, Jeroen; Hendrikse, Jeroen; Siero, Jeroen C W; Petersen, Esben T; De Vis, Jill B

    2018-01-01

    In previous work we have developed a fast sequence that focusses on cerebrospinal fluid (CSF) based on the long T2 of CSF. By processing the data obtained with this CSF MRI sequence, brain parenchymal volume (BPV) and intracranial volume (ICV) can be automatically obtained. The aim of this study was to assess the precision of the BPV and ICV measurements of the CSF MRI sequence and to validate the CSF MRI sequence by comparison with 3D T1-based brain segmentation methods. Ten healthy volunteers (2 females; median age 28 years) were scanned (3T MRI) twice with repositioning in between. The scan protocol consisted of a low resolution (LR) CSF sequence (0:57min), a high resolution (HR) CSF sequence (3:21min) and a 3D T1-weighted sequence (6:47min). Data of the HR 3D-T1-weighted images were downsampled to obtain LR T1-weighted images (reconstructed imaging time: 1:59 min). Data of the CSF MRI sequences was automatically segmented using in-house software. The 3D T1-weighted images were segmented using FSL (5.0), SPM12 and FreeSurfer (5.3.0). The mean absolute differences for BPV and ICV between the first and second scan for CSF LR (BPV/ICV: 12±9/7±4cc) and CSF HR (5±5/4±2cc) were comparable to FSL HR (9±11/19±23cc), FSL LR (7±4, 6±5cc), FreeSurfer HR (5±3/14±8cc), FreeSurfer LR (9±8, 12±10cc), and SPM HR (5±3/4±7cc), and SPM LR (5±4, 5±3cc). The correlation between the measured volumes of the CSF sequences and that measured by FSL, FreeSurfer and SPM HR and LR was very good (all Pearson's correlation coefficients >0.83, R2 .67-.97). The results from the downsampled data and the high-resolution data were similar. Both CSF MRI sequences have a precision comparable to, and a very good correlation with established 3D T1-based automated segmentations methods for the segmentation of BPV and ICV. However, the short imaging time of the fast CSF MRI sequence is superior to the 3D T1 sequence on which segmentation with established methods is performed.

  19. Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.

    Science.gov (United States)

    Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

    2014-08-01

    A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

  20. Breast ultrasound image segmentation: an optimization approach based on super-pixels and high-level descriptors

    Science.gov (United States)

    Massich, Joan; Lemaître, Guillaume; Martí, Joan; Mériaudeau, Fabrice

    2015-04-01

    Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level descriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art.

  1. Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field

    Directory of Open Access Journals (Sweden)

    Penghui Wang

    2017-01-01

    Full Text Available Potholes are one type of pavement surface distresses whose assessment is essential for developing road network maintenance strategies. Existing methods for automatic pothole detection either rely on expensive and high-maintenance equipment or could not segment the pothole accurately. In this paper, an asphalt pavement pothole detection and segmentation method based on energy field is put forward. The proposed method mainly includes two processes. Firstly, the wavelet energy field of the pavement image is constructed to detect the pothole by morphological processing and geometric criterions. Secondly, the detected pothole is segmented by Markov random field model and the pothole edge is extracted accurately. This methodology has been implemented in a MATLAB prototype, trained, and tested on 120 pavement images. The results show that it can effectively distinguish potholes from cracks, patches, greasy dirt, shadows, and manhole covers and accurately segment the pothole. For pothole detection, the method reaches an overall accuracy of 86.7%, with 83.3% precision and 87.5% recall. For pothole segmentation, the overlap degree between the extracted pothole region and the original pothole region is mostly more than 85%, which accounts for 88.6% of the total detected pavement pothole images.

  2. 3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Yuru, E-mail: peiyuru@cis.pku.edu.cn; Ai, Xingsheng; Zha, Hongbin [Department of Machine Intelligence, School of EECS, Peking University, Beijing 100871 (China); Xu, Tianmin [School of Stomatology, Stomatology Hospital, Peking University, Beijing 100081 (China); Ma, Gengyu [uSens, Inc., San Jose, California 95110 (United States)

    2016-09-15

    Purpose: Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. Methods: The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. Results: The proposed method was applied for tooth segmentation of twenty clinically captured CBCT

  3. An approach to melodic segmentation and classification based on filtering with the Haar-wavelet

    DEFF Research Database (Denmark)

    Velarde, Gissel; Weyde, Tillman; Meredith, David

    2013-01-01

    -based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based...

  4. Recurrent neural networks for breast lesion classification based on DCE-MRIs

    Science.gov (United States)

    Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

    2018-02-01

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03).

  5. MIN-CUT BASED SEGMENTATION OF AIRBORNE LIDAR POINT CLOUDS

    Directory of Open Access Journals (Sweden)

    S. Ural

    2012-07-01

    Full Text Available Introducing an organization to the unstructured point cloud before extracting information from airborne lidar data is common in many applications. Aggregating the points with similar features into segments in 3-D which comply with the nature of actual objects is affected by the neighborhood, scale, features and noise among other aspects. In this study, we present a min-cut based method for segmenting the point cloud. We first assess the neighborhood of each point in 3-D by investigating the local geometric and statistical properties of the candidates. Neighborhood selection is essential since point features are calculated within their local neighborhood. Following neighborhood determination, we calculate point features and determine the clusters in the feature space. We adapt a graph representation from image processing which is especially used in pixel labeling problems and establish it for the unstructured 3-D point clouds. The edges of the graph that are connecting the points with each other and nodes representing feature clusters hold the smoothness costs in the spatial domain and data costs in the feature domain. Smoothness costs ensure spatial coherence, while data costs control the consistency with the representative feature clusters. This graph representation formalizes the segmentation task as an energy minimization problem. It allows the implementation of an approximate solution by min-cuts for a global minimum of this NP hard minimization problem in low order polynomial time. We test our method with airborne lidar point cloud acquired with maximum planned post spacing of 1.4 m and a vertical accuracy 10.5 cm as RMSE. We present the effects of neighborhood and feature determination in the segmentation results and assess the accuracy and efficiency of the implemented min-cut algorithm as well as its sensitivity to the parameters of the smoothness and data cost functions. We find that smoothness cost that only considers simple distance

  6. Super-Segments Based Classification of 3D Urban Street Scenes

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2012-12-01

    Full Text Available We address the problem of classifying 3D point clouds: given 3D urban street scenes gathered by a lidar sensor, we wish to assign a class label to every point. This work is a key step toward realizing applications in robots and cars, for example. In this paper, we present a novel approach to the classification of 3D urban scenes based on super-segments, which are generated from point clouds by two stages of segmentation: a clustering stage and a grouping stage. Then, six effective normal and dimension features that vary with object class are extracted at the super-segment level for training some general classifiers. We evaluate our method both quantitatively and qualitatively using the challenging Velodyne lidar data set. The results show that by only using normal and dimension features we can achieve better recognition than can be achieved with high-dimensional shape descriptors. We also evaluate the adopting of the MRF framework in our approach, but the experimental results indicate that thisbarely improved the accuracy of the classified results due to the sparse property of the super-segments.

  7. Event-Based Color Segmentation With a High Dynamic Range Sensor

    Directory of Open Access Journals (Sweden)

    Alexandre Marcireau

    2018-04-01

    Full Text Available This paper introduces a color asynchronous neuromorphic event-based camera and a methodology to process color output from the device to perform color segmentation and tracking at the native temporal resolution of the sensor (down to one microsecond. Our color vision sensor prototype is a combination of three Asynchronous Time-based Image Sensors, sensitive to absolute color information. We devise a color processing algorithm leveraging this information. It is designed to be computationally cheap, thus showing how low level processing benefits from asynchronous acquisition and high temporal resolution data. The resulting color segmentation and tracking performance is assessed both with an indoor controlled scene and two outdoor uncontrolled scenes. The tracking's mean error to the ground truth for the objects of the outdoor scenes ranges from two to twenty pixels.

  8. Registration-based segmentation with articulated model from multipostural magnetic resonance images for hand bone motion animation.

    Science.gov (United States)

    Chen, Hsin-Chen; Jou, I-Ming; Wang, Chien-Kuo; Su, Fong-Chin; Sun, Yung-Nien

    2010-06-01

    The quantitative measurements of hand bones, including volume, surface, orientation, and position are essential in investigating hand kinematics. Moreover, within the measurement stage, bone segmentation is the most important step due to its certain influences on measuring accuracy. Since hand bones are small and tubular in shape, magnetic resonance (MR) imaging is prone to artifacts such as nonuniform intensity and fuzzy boundaries. Thus, greater detail is required for improving segmentation accuracy. The authors then propose using a novel registration-based method on an articulated hand model to segment hand bones from multipostural MR images. The proposed method consists of the model construction and registration-based segmentation stages. Given a reference postural image, the first stage requires construction of a drivable reference model characterized by hand bone shapes, intensity patterns, and articulated joint mechanism. By applying the reference model to the second stage, the authors initially design a model-based registration pursuant to intensity distribution similarity, MR bone intensity properties, and constraints of model geometry to align the reference model to target bone regions of the given postural image. The authors then refine the resulting surface to improve the superimposition between the registered reference model and target bone boundaries. For each subject, given a reference postural image, the proposed method can automatically segment all hand bones from all other postural images. Compared to the ground truth from two experts, the resulting surface image had an average margin of error within 1 mm (mm) only. In addition, the proposed method showed good agreement on the overlap of bone segmentations by dice similarity coefficient and also demonstrated better segmentation results than conventional methods. The proposed registration-based segmentation method can successfully overcome drawbacks caused by inherent artifacts in MR images and

  9. A phylogenetic framework for root lesion nematodes of the genus Pratylenchus (Nematoda): Evidence from 18S and D2-D3 expansion segments of 28S ribosomal RNA genes and morphological characters.

    Science.gov (United States)

    Subbotin, Sergei A; Ragsdale, Erik J; Mullens, Teresa; Roberts, Philip A; Mundo-Ocampo, Manuel; Baldwin, James G

    2008-08-01

    The root lesion nematodes of the genus Pratylenchus Filipjev, 1936 are migratory endoparasites of plant roots, considered among the most widespread and important nematode parasites in a variety of crops. We obtained gene sequences from the D2 and D3 expansion segments of 28S rRNA partial and 18S rRNA from 31 populations belonging to 11 valid and two unidentified species of root lesion nematodes and five outgroup taxa. These datasets were analyzed using maximum parsimony and Bayesian inference. The alignments were generated using the secondary structure models for these molecules and analyzed with Bayesian inference under the standard models and the complex model, considering helices under the doublet model and loops and bulges under the general time reversible model. The phylogenetic informativeness of morphological characters is tested by reconstruction of their histories on rRNA based trees using parallel parsimony and Bayesian approaches. Phylogenetic and sequence analyses of the 28S D2-D3 dataset with 145 accessions for 28 species and 18S dataset with 68 accessions for 15 species confirmed among large numbers of geographical diverse isolates that most classical morphospecies are monophyletic. Phylogenetic analyses revealed at least six distinct major clades of examined Pratylenchus species and these clades are generally congruent with those defined by characters derived from lip patterns, numbers of lip annules, and spermatheca shape. Morphological results suggest the need for sophisticated character discovery and analysis for morphology based phylogenetics in nematodes.

  10. A wavelet-based Bayesian framework for 3D object segmentation in microscopy

    Science.gov (United States)

    Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

    2012-03-01

    In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

  11. Lesion progression in post-treatment persistent endodontic lesions.

    Science.gov (United States)

    Yu, Victoria Soo Hoon; Messer, Harold Henry; Shen, Liang; Yee, Robert; Hsu, Chin-ying Stephen

    2012-10-01

    Radiographic lesions related to root-filled teeth may persist for long periods after treatment and are considered to indicate failure of initial treatment. Persistent lesions are found in a proportion of cases, but information on lesion progression is lacking. This study examined the incidence of lesion improvement, remaining unchanged, and deterioration among persistent lesions in a group of patients recruited from a university-based clinic and identified potential predictors for lesion progression. Patients of a university clinic with persistent endodontic lesions at least 4 years since treatment and with original treatment radiographs available were recruited with informed consent. Data were obtained by interview and from dental records and clinical and radiographic examinations. Univariate and multivariate statistical analyses were carried out by using SPSS (version 19). One hundred fifty-one persistent lesions were identified in 114 patients. A majority of the lesions (107, 70.9%) received treatment between 4 and 5 years prior. Eighty-six lesions (57.0%) improved, 18 (11.9%) remained unchanged, and 47 (31.1%) deteriorated since treatment. Potential predictors for lesions that did not improve included recall lesion size, pain on biting at recall examination, history of a postobturation flare-up, and a non-ideal root-filling length (P < .05). Lesions that had persisted for a longer period appeared less likely to be improving (relative risk, 1.038; 95% confidence interval, 1.000-1.077). A specific time interval alone should not be used to conclude that a lesion will not resolve without intervention. This study identified several clinical factors that are associated with deteriorating persistent lesions, which should aid in identifying lesions that require further intervention. Copyright © 2012 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  12. Multi-scale Gaussian representation and outline-learning based cell image segmentation

    Science.gov (United States)

    2013-01-01

    Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

  13. ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

    Science.gov (United States)

    Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

    2013-05-01

    In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

  14. Entropy Based Classifier Combination for Sentence Segmentation

    Science.gov (United States)

    2007-01-01

    speaker diarization system to divide the audio data into hypothetical speakers [17...the prosodic feature also includes turn-based features which describe the position of a word in relation to diarization seg- mentation. The speaker ...ro- bust speaker segmentation: the ICSI-SRI fall 2004 diarization system,” in Proc. RT-04F Workshop, 2004. [18] “The rich transcription fall 2003,” http://nist.gov/speech/tests/rt/rt2003/fall/docs/rt03-fall-eval- plan-v9.pdf.

  15. 3D surface rendering of images from multiple MR pulse sequences in the pre-operative evaluation of hepatic lesions

    International Nuclear Information System (INIS)

    Bjerner, T.; Johansson, L.; Ahlstroem, H.; Haglund, U.

    1998-01-01

    Purpose: To develop a method for making three-dimensional (3D) reconstructions of liver vessels and hepatic lesions from different MR data sets. Material and Methods: To reduce the time required for segmentation and reconstructions, we used T1, T2 and phase contrast angiography, optimised for liver, lesion and vessels respectively. Following segmentation and reconstruction, the different volumes were combined on the same workstation and presented to the surgeon. Results and Conclusion: Segmentation and reconstruction took 1-2 h. To be able to combine the volumes from the different data sets, certain criteria had to be fulfilled: (a) the field of view had to be constant; (b) the same volume had to be scanned every time which meant that the slice thickness and the number of slices could be adjusted as long as the volume covered was the same; and (c) the positioning of each volume had to be identical between every scan. The resulting 3D reconstruction gave the surgeon a clear appreciation of the different lesions and their relation to the different liver segments in the pre-operative planning of hepatic resections. (orig.)

  16. SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

    International Nuclear Information System (INIS)

    Gou, S; Rapacchi, S; Hu, P; Sheng, K

    2014-01-01

    Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagated to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on

  17. Improved characterization of molecular phenotypes in breast lesions using 18F-FDG PET image homogeneity

    Science.gov (United States)

    Cao, Kunlin; Bhagalia, Roshni; Sood, Anup; Brogi, Edi; Mellinghoff, Ingo K.; Larson, Steven M.

    2015-03-01

    Positron emission tomography (PET) using uorodeoxyglucose (18F-FDG) is commonly used in the assessment of breast lesions by computing voxel-wise standardized uptake value (SUV) maps. Simple metrics derived from ensemble properties of SUVs within each identified breast lesion are routinely used for disease diagnosis. The maximum SUV within the lesion (SUVmax) is the most popular of these metrics. However these simple metrics are known to be error-prone and are susceptible to image noise. Finding reliable SUV map-based features that correlate to established molecular phenotypes of breast cancer (viz. estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression) will enable non-invasive disease management. This study investigated 36 SUV features based on first and second order statistics, local histograms and texture of segmented lesions to predict ER and PR expression in 51 breast cancer patients. True ER and PR expression was obtained via immunohistochemistry (IHC) of tissue samples from each lesion. A supervised learning, adaptive boosting-support vector machine (AdaBoost-SVM), framework was used to select a subset of features to classify breast lesions into distinct phenotypes. Performance of the trained multi-feature classifier was compared against the baseline single-feature SUVmax classifier using receiver operating characteristic (ROC) curves. Results show that texture features encoding local lesion homogeneity extracted from gray-level co-occurrence matrices are the strongest discriminator of lesion ER expression. In particular, classifiers including these features increased prediction accuracy from 0.75 (baseline) to 0.82 and the area under the ROC curve from 0.64 (baseline) to 0.75.

  18. Point based interactive image segmentation using multiquadrics splines

    Science.gov (United States)

    Meena, Sachin; Duraisamy, Prakash; Palniappan, Kannappan; Seetharaman, Guna

    2017-05-01

    Multiquadrics (MQ) are radial basis spline function that can provide an efficient interpolation of data points located in a high dimensional space. MQ were developed by Hardy to approximate geographical surfaces and terrain modelling. In this paper we frame the task of interactive image segmentation as a semi-supervised interpolation where an interpolating function learned from the user provided seed points is used to predict the labels of unlabeled pixel and the spline function used in the semi-supervised interpolation is MQ. This semi-supervised interpolation framework has a nice closed form solution which along with the fact that MQ is a radial basis spline function lead to a very fast interactive image segmentation process. Quantitative and qualitative results on the standard datasets show that MQ outperforms other regression based methods, GEBS, Ridge Regression and Logistic Regression, and popular methods like Graph Cut,4 Random Walk and Random Forest.6

  19. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

    2015-06-15

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

  20. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

    Science.gov (United States)

    Seoud, Lama; Hurtut, Thomas; Chelbi, Jihed; Cheriet, Farida; Langlois, J M Pierre

    2016-04-01

    The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

  1. WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    P. Mathivanan

    2014-02-01

    Full Text Available In this paper we develop a system for writer identification which involves four processing steps like preprocessing, segmentation, feature extraction and writer identification using neural network. In the preprocessing phase the handwritten text is subjected to slant removal process for segmentation and feature extraction. After this step the text image enters into the process of noise removal and gray level conversion. The preprocessed image is further segmented by using morphological watershed algorithm, where the text lines are segmented into single words and then into single letters. The segmented image is feature extracted by Daubechies’5/3 integer wavelet transform to reduce training complexity [1, 6]. This process is lossless and reversible [10], [14]. These extracted features are given as input to our neural network for writer identification process and a target image is selected for each training process in the 2-layer neural network. With the several trained output data obtained from different target help in text identification. It is a multilingual text analysis which provides simple and efficient text segmentation.

  2. Candidate Smoke Region Segmentation of Fire Video Based on Rough Set Theory

    Directory of Open Access Journals (Sweden)

    Yaqin Zhao

    2015-01-01

    Full Text Available Candidate smoke region segmentation is the key link of smoke video detection; an effective and prompt method of candidate smoke region segmentation plays a significant role in a smoke recognition system. However, the interference of heavy fog and smoke-color moving objects greatly degrades the recognition accuracy. In this paper, a novel method of candidate smoke region segmentation based on rough set theory is presented. First, Kalman filtering is used to update video background in order to exclude the interference of static smoke-color objects, such as blue sky. Second, in RGB color space smoke regions are segmented by defining the upper approximation, lower approximation, and roughness of smoke-color distribution. Finally, in HSV color space small smoke regions are merged by the definition of equivalence relation so as to distinguish smoke images from heavy fog images in terms of V component value variety from center to edge of smoke region. The experimental results on smoke region segmentation demonstrated the effectiveness and usefulness of the proposed scheme.

  3. Surface characterization, hemo- and cytocompatibility of segmented poly(dimethylsiloxane-based polyurethanes

    Directory of Open Access Journals (Sweden)

    Pergal Marija V.

    2014-01-01

    Full Text Available Segmented polyurethanes based on poly(dimethylsiloxane, currently used for biomedical applications, have sub-optimal biocompatibility which reduces their efficacy. Improving the endothelial cell attachment and blood-contacting properties of PDMS-based copolymers would substantially improve their clinical applications. We have studied the surface properties and in vitro biocompatibility of two series of segmented poly(urethane-dimethylsiloxanes (SPU-PDMS based on hydroxypropyl- and hydroxyethoxypropyl- terminated PDMS with potential applications in blood-contacting medical devices. SPU-PDMS copolymers were characterized by contact angle measurements, surface free energy determination (calculated using the van Oss-Chaudhury-Good and Owens-Wendt methods, and atomic force microscopy. The biocompatibility of copolymers was evaluated using an endothelial EA.hy926 cell line by direct contact assay, before and after pre-treatment of copolymers with multicomponent protein mixture, as well as by a competitive blood-protein adsorption assay. The obtained results suggested good blood compatibility of synthesized copolymers. All copolymers exhibited good resistance to fibrinogen adsorption and all favored albumin adsorption. Copolymers based on hydroxyethoxypropyl-PDMS had lower hydrophobicity, higher surface free energy, and better microphase separation in comparison with hydroxypropyl-PDMS-based copolymers, which promoted better endothelial cell attachment and growth on the surface of these polymers as compared to hydroxypropyl-PDMS-based copolymers. The results showed that SPU-PDMS copolymers display good surface properties, depending on the type of soft PDMS segments, which can be tailored for biomedical application requirements such as biomedical devices for short- and long-term uses. [Projekat Ministarstva nauke Republike Srbije, br. 172062

  4. Objective Ventricle Segmentation in Brain CT with Ischemic Stroke Based on Anatomical Knowledge

    Directory of Open Access Journals (Sweden)

    Xiaohua Qian

    2017-01-01

    Full Text Available Ventricle segmentation is a challenging technique for the development of detection system of ischemic stroke in computed tomography (CT, as ischemic stroke regions are adjacent to the brain ventricle with similar intensity. To address this problem, we developed an objective segmentation system of brain ventricle in CT. The intensity distribution of the ventricle was estimated based on clustering technique, connectivity, and domain knowledge, and the initial ventricle segmentation results were then obtained. To exclude the stroke regions from initial segmentation, a combined segmentation strategy was proposed, which is composed of three different schemes: (1 the largest three-dimensional (3D connected component was considered as the ventricular region; (2 the big stroke areas were removed by the image difference methods based on searching optimal threshold values; (3 the small stroke regions were excluded by the adaptive template algorithm. The proposed method was evaluated on 50 cases of patients with ischemic stroke. The mean Dice, sensitivity, specificity, and root mean squared error were 0.9447, 0.969, 0.998, and 0.219 mm, respectively. This system can offer a desirable performance. Therefore, the proposed system is expected to bring insights into clinic research and the development of detection system of ischemic stroke in CT.

  5. Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

    Science.gov (United States)

    Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

    2015-01-01

    Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

  6. A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation - With Application to Tumor and Stroke

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial

    2016-01-01

    jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model......), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions...

  7. Market Segmentation Using Bayesian Model Based Clustering

    NARCIS (Netherlands)

    Van Hattum, P.

    2009-01-01

    This dissertation deals with two basic problems in marketing, that are market segmentation, which is the grouping of persons who share common aspects, and market targeting, which is focusing your marketing efforts on one or more attractive market segments. For the grouping of persons who share

  8. Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Joanna Jaworek-Korjakowska

    2016-01-01

    Full Text Available Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

  9. How Should We Perform Rotational Atherectomy to an Angulated Calcified Lesion?

    Science.gov (United States)

    Sakakura, Kenichi; Taniguchi, Yousuke; Matsumoto, Mitsunari; Wada, Hiroshi; Momomura, Shin-Ichi; Fujita, Hideo

    2016-05-25

    Rotational atherectomy to an angulated calcified lesion is always challenging. The risk of catastrophic complications such as a burr becoming stuck or vessel perforation is greater when the calcified lesion is angulated. We describe the case of an 83-year-old female suffering from unstable angina. Diagnostic coronary angiography revealed an angulated calcified lesion in the proximal segment of the right coronary artery. We performed rotational atherectomy to the lesion, but intentionally did not advance the rotational atherectomy burr beyond the top of the angulation. We controlled the rotational atherectomy burr and stopped it just before the top of the angulation to avoid complications. Following rotational atherectomy, balloon dilatation with a non-compliant balloon was performed, and drug-eluting stents were successfully deployed. In this manuscript, we provide a review of the literature on this topic, and discuss how rotational atherectomy to an angulated calcified lesion should be performed.

  10. Brain tumor segmentation based on a hybrid clustering technique

    Directory of Open Access Journals (Sweden)

    Eman Abdel-Maksoud

    2015-03-01

    This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

  11. Multicenter assessment of the reproducibility of volumetric radiofrequency-based intravascular ultrasound measurements in coronary lesions that were consecutively stented

    DEFF Research Database (Denmark)

    Huisman, Jennifer; Egede, Rasmus; Rdzanek, Adam

    2012-01-01

    To assess in a multicenter design the between-center reproducibility of volumetric virtual histology intravascular ultrasound (VH-IVUS) measurements with a semi-automated, computer-assisted contour detection system in coronary lesions that were consecutively stented. To evaluate the reproducibility...... of volumetric VH-IVUS measurements, experienced analysts of 4 European IVUS centers performed independent analyses (in total 8,052 cross-sectional analyses) to obtain volumetric data of 40 coronary segments (length 20.0 ± 0.3 mm) from target lesions prior to percutaneous intervention that were performed...... in the setting of stable (65%) or unstable angina pectoris (35%). Geometric and compositional VH-IVUS measurements were highly correlated for the different comparisons. Overall intraclass correlation for vessel, lumen, plaque volume and plaque burden was 0.99, 0.92, 0.96, and 0.83, respectively; for fibrous...

  12. SU-C-207B-03: A Geometrical Constrained Chan-Vese Based Tumor Segmentation Scheme for PET

    International Nuclear Information System (INIS)

    Chen, L; Zhou, Z; Wang, J

    2016-01-01

    Purpose: Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan-Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices. Methods: The proposed scheme performs segmentation in a slice-by-slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering-based segmentation method under user’s guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan-Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan-Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme. Results: Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively. Conclusion: We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

  13. SU-C-207B-03: A Geometrical Constrained Chan-Vese Based Tumor Segmentation Scheme for PET

    Energy Technology Data Exchange (ETDEWEB)

    Chen, L; Zhou, Z; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan-Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices. Methods: The proposed scheme performs segmentation in a slice-by-slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering-based segmentation method under user’s guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan-Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan-Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme. Results: Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively. Conclusion: We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

  14. A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy.

    Directory of Open Access Journals (Sweden)

    Menglong Xu

    Full Text Available High-intensity focused ultrasound (HIFU therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.

  15. Automated lesion detection on MRI scans using combined unsupervised and supervised methods

    International Nuclear Information System (INIS)

    Guo, Dazhou; Fridriksson, Julius; Fillmore, Paul; Rorden, Christopher; Yu, Hongkai; Zheng, Kang; Wang, Song

    2015-01-01

    Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a

  16. Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches.

    Science.gov (United States)

    Le Troter, Arnaud; Fouré, Alexandre; Guye, Maxime; Confort-Gouny, Sylviane; Mattei, Jean-Pierre; Gondin, Julien; Salort-Campana, Emmanuelle; Bendahan, David

    2016-04-01

    Atlas-based segmentation is a powerful method for automatic structural segmentation of several sub-structures in many organs. However, such an approach has been very scarcely used in the context of muscle segmentation, and so far no study has assessed such a method for the automatic delineation of individual muscles of the quadriceps femoris (QF). In the present study, we have evaluated a fully automated multi-atlas method and a semi-automated single-atlas method for the segmentation and volume quantification of the four muscles of the QF and for the QF as a whole. The study was conducted in 32 young healthy males, using high-resolution magnetic resonance images (MRI) of the thigh. The multi-atlas-based segmentation method was conducted in 25 subjects. Different non-linear registration approaches based on free-form deformable (FFD) and symmetric diffeomorphic normalization algorithms (SyN) were assessed. Optimal parameters of two fusion methods, i.e., STAPLE and STEPS, were determined on the basis of the highest Dice similarity index (DSI) considering manual segmentation (MSeg) as the ground truth. Validation and reproducibility of this pipeline were determined using another MRI dataset recorded in seven healthy male subjects on the basis of additional metrics such as the muscle volume similarity values, intraclass coefficient, and coefficient of variation. Both non-linear registration methods (FFD and SyN) were also evaluated as part of a single-atlas strategy in order to assess longitudinal muscle volume measurements. The multi- and the single-atlas approaches were compared for the segmentation and the volume quantification of the four muscles of the QF and for the QF as a whole. Considering each muscle of the QF, the DSI of the multi-atlas-based approach was high 0.87 ± 0.11 and the best results were obtained with the combination of two deformation fields resulting from the SyN registration method and the STEPS fusion algorithm. The optimal variables for FFD

  17. Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

    Science.gov (United States)

    Parida, G.; Rajan, K. S.

    2017-05-01

    The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

  18. LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA

    Directory of Open Access Journals (Sweden)

    G. Parida

    2017-05-01

    Full Text Available The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

  19. Vertebral lesion distribution in multiple myeloma - assessed by reduced-dose whole-body MDCT

    International Nuclear Information System (INIS)

    Bier, Georg; Kloth, Christopher; Schabel, Christoph; Bongers, Malte; Nikolaou, Konstantin; Horger, Marius

    2016-01-01

    To observe the distribution and potential distribution patterns of osteolytic and sclerotic vertebral involvement in a representative collective of multiple myeloma patients. A total of 66 consecutive patients with a diagnosis of multiple myeloma at initial diagnosis or during follow-up were examined by multidetector reduced-dose computed tomography to evaluate the distribution of bone lesions along the spine with focus on size, location, and lesion character. Confirmation of diagnosis was performed by comparison to follow-up computed tomography or magnetic resonance tomography. If >50 % of all detected malignant lesions occurred in one spinal segment, the distribution pattern was called cervical, thoracic, lumbar, or sacral, otherwise a ''mixed'' pattern was classified. Of a total number of 933 osseous spine lesions, 632 (67.7 %) were classified as malignant (98.9 % of them osteolytic) and 293 (31.5 %) as benign. The distribution pattern analysis yielded two patients (3.8 %) with a cervical, 26 (50 %) with a thoracic, 4 (7.7 %) with a lumbar, one (1.9 %) with a sacral pattern, and 19 cases (36.6 %) showed a mixed distribution pattern. Segment-wise, the mean lesion size was 6.52 ± 2.76 mm (cervical), 8.97 ± 5.43 mm (thoracic), 11.97 ± 7.11 mm (lumbar), and 17.5 ± 16.465 (sacral), whilst, related to the vertebra size, the lesion/vertebra size ratio is decreasing through the whole spine beginning from the top. Multiple myeloma bone lesions occur preferably and are larger in the thoracic and lumbar spine. Moreover, a specific distribution pattern is present in about 60 %. (orig.)

  20. Vertebral lesion distribution in multiple myeloma - assessed by reduced-dose whole-body MDCT

    Energy Technology Data Exchange (ETDEWEB)

    Bier, Georg; Kloth, Christopher; Schabel, Christoph; Bongers, Malte; Nikolaou, Konstantin; Horger, Marius [Eberhard-Karls-University Tuebingen, Department of Diagnostic and Interventional Radiology, Tuebingen (Germany)

    2016-01-15

    To observe the distribution and potential distribution patterns of osteolytic and sclerotic vertebral involvement in a representative collective of multiple myeloma patients. A total of 66 consecutive patients with a diagnosis of multiple myeloma at initial diagnosis or during follow-up were examined by multidetector reduced-dose computed tomography to evaluate the distribution of bone lesions along the spine with focus on size, location, and lesion character. Confirmation of diagnosis was performed by comparison to follow-up computed tomography or magnetic resonance tomography. If >50 % of all detected malignant lesions occurred in one spinal segment, the distribution pattern was called cervical, thoracic, lumbar, or sacral, otherwise a ''mixed'' pattern was classified. Of a total number of 933 osseous spine lesions, 632 (67.7 %) were classified as malignant (98.9 % of them osteolytic) and 293 (31.5 %) as benign. The distribution pattern analysis yielded two patients (3.8 %) with a cervical, 26 (50 %) with a thoracic, 4 (7.7 %) with a lumbar, one (1.9 %) with a sacral pattern, and 19 cases (36.6 %) showed a mixed distribution pattern. Segment-wise, the mean lesion size was 6.52 ± 2.76 mm (cervical), 8.97 ± 5.43 mm (thoracic), 11.97 ± 7.11 mm (lumbar), and 17.5 ± 16.465 (sacral), whilst, related to the vertebra size, the lesion/vertebra size ratio is decreasing through the whole spine beginning from the top. Multiple myeloma bone lesions occur preferably and are larger in the thoracic and lumbar spine. Moreover, a specific distribution pattern is present in about 60 %. (orig.)

  1. A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

    Science.gov (United States)

    Rundo, Leonardo; Stefano, Alessandro; Militello, Carmelo; Russo, Giorgio; Sabini, Maria Gabriella; D'Arrigo, Corrado; Marletta, Francesco; Ippolito, Massimo; Mauri, Giancarlo; Vitabile, Salvatore; Gilardi, Maria Carla

    2017-06-01

    Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [ 11 C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife ® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV MRI . A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment

  2. The parietal epithelial cell is crucially involved in human idiopathic focal segmental glomerulosclerosis.

    Science.gov (United States)

    Dijkman, Henry; Smeets, Bart; van der Laak, Jeroen; Steenbergen, Eric; Wetzels, Jack

    2005-10-01

    Focal segmental glomerulosclerosis (FSGS) is one of the most common patterns of glomerular injury encountered in human renal biopsies. Epithelial hyperplasia, which can be prominent in FSGS, has been attributed to dedifferentiation and proliferation of podocytes. Based on observations in a mouse model of FSGS, we pointed to the role of parietal epithelial cells (PECs). In the present study we investigated the relative role of PECs and podocytes in human idiopathic FSGS. We performed a detailed study of lesions from a patient with recurrent idiopathic FSGS by serial sectioning, marker analysis and three-dimensional reconstruction of glomeruli. We have studied the expression of markers for podocytes, PECs, mesangial cells, endothelium, and myofibroblasts. We also looked at proliferation and composition of the deposited extracellular matrix (ECM). We found that proliferating epithelial cells in FSGS lesions are negative for podocyte and macrophage markers, but stain for PEC markers. The composition of the matrix deposited by these cells is identical to Bowman's capsule. Our study demonstrates that PECs are crucially involved in the pathogenesis of FSGS lesions.

  3. Proposal of a novel ensemble learning based segmentation with a shape prior and its application to spleen segmentation from a 3D abdominal CT volume

    International Nuclear Information System (INIS)

    Shindo, Kiyo; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru; Shinozaki, Kenji

    2010-01-01

    An organ segmentation learned by a conventional ensemble learning algorithm suffers from unnatural errors because each voxel is classified independently in the segmentation process. This paper proposes a novel ensemble learning algorithm that can take into account global shape and location of organs. It estimates the shape and location of an organ from a given image by combining an intermediate segmentation result with a statistical shape model. Once an ensemble learning algorithm could not improve the segmentation performance in the iterative learning process, it estimates the shape and location by finding an optimal model parameter set with maximum degree of correspondence between a statistical shape model and the intermediate segmentation result. Novel weak classifiers are generated based on a signed distance from a boundary of the estimated shape and a distance from a barycenter of the intermediate segmentation result. Subsequently it continues the learning process with the novel weak classifiers. This paper presents experimental results where the proposed ensemble learning algorithm generates a segmentation process that can extract a spleen from a 3D CT image more precisely than a conventional one. (author)

  4. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

    Science.gov (United States)

    Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

    2017-06-12

    Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

  5. A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation

    Science.gov (United States)

    Xue, Yingying; Wang, Jianbiao; Zhou, Mei; Hou, Xiyue; Li, Qingli; Liu, Hongying; Wang, Yiting

    2017-07-01

    As an imaging technology used in remote sensing, hyperspectral imaging can provide more information than traditional optical imaging of blood cells. In this paper, an AOTF based microscopic hyperspectral imaging system is used to capture hyperspectral images of blood cells. In order to achieve the segmentation of red blood cells, Gaussian process using squared exponential kernel function is applied first after the data preprocessing to make the preliminary segmentation. The derivative spectrum with spectral angle mapping algorithm is then applied to the original image to segment the boundary of cells, and using the boundary to cut out cells obtained from the Gaussian process to separated adjacent cells. Then the morphological processing method including closing, erosion and dilation is applied so as to keep adjacent cells apart, and by applying median filtering to remove noise points and filling holes inside the cell, the final segmentation result can be obtained. The experimental results show that this method appears better segmentation effect on human red blood cells.

  6. Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

    Science.gov (United States)

    Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

    2016-01-01

    Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as

  7. The Research of Disease Spots Extraction Based on Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Kangshun Li

    2017-01-01

    Full Text Available According to the characteristics of maize disease spot performance in the image, this paper designs two-histogram segmentation method based on evolutionary algorithm, which combined with the analysis of image of maize diseases and insect pests, with full consideration of color and texture characteristic of the lesion of pests and diseases, the chroma and gray image, composed of two tuples to build a two-dimensional histogram, solves the problem of one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution and improved the traditional two-dimensional histogram application in pest damage lesion extraction. The chromosome coding suitable for the characteristics of lesion image is designed based on second segmentation of the genetic algorithm Otsu. Determining initial population with analysis results of lesion image, parallel selection, optimal preservation strategy, and adaptive mutation operator are used to improve the search efficiency. Finally, by setting the fluctuation threshold, we continue to search for the best threshold in the range of fluctuations for implementation of global search and local search.

  8. Managing Carious Lesions

    DEFF Research Database (Denmark)

    Schwendicke, F; Frencken, J E; Bjørndal, L

    2016-01-01

    should be prioritized, while in shallow or moderately deep lesions, restoration longevity becomes more important. For teeth with shallow or moderately deep cavitated lesions, carious tissue removal is performed according toselective removal to firm dentine.In deep cavitated lesions in primary......The International Caries Consensus Collaboration undertook a consensus process and here presents clinical recommendations for carious tissue removal and managing cavitated carious lesions, including restoration, based on texture of demineralized dentine. Dentists should manage the disease dental...

  9. Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

    Directory of Open Access Journals (Sweden)

    Shuo-Tsung Chen

    2015-01-01

    Full Text Available Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

  10. Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.

    Science.gov (United States)

    Deng, Minghui; Yu, Renping; Wang, Li; Shi, Feng; Yap, Pew-Thian; Shen, Dinggang

    2016-12-01

    Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio. In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation. The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values brain MR image segmentation. © 2016 American Association of Physicists in Medicine.

  11. Buildings and Terrain of Urban Area Point Cloud Segmentation based on PCL

    International Nuclear Information System (INIS)

    Liu, Ying; Zhong, Ruofei

    2014-01-01

    One current problem with laser radar point data classification is building and urban terrain segmentation, this paper proposes a point cloud segmentation method base on PCL libraries. PCL is a large cross-platform open source C++ programming library, which implements a large number of point cloud related efficient data structures and generic algorithms involving point cloud retrieval, filtering, segmentation, registration, feature extraction and curved surface reconstruction, visualization, etc. Due to laser radar point cloud characteristics with large amount of data, unsymmetrical distribution, this paper proposes using the data structure of kd-tree to organize data; then using Voxel Grid filter for point cloud resampling, namely to reduce the amount of point cloud data, and at the same time keep the point cloud shape characteristic; use PCL Segmentation Module, we use a Euclidean Cluster Extraction class with Europe clustering for buildings and ground three-dimensional point cloud segmentation. The experimental results show that this method avoids the multiple copy system existing data needs, saves the program storage space through the call of PCL library method and class, shortens the program compiled time and improves the running speed of the program

  12. Real-time biscuit tile image segmentation method based on edge detection.

    Science.gov (United States)

    Matić, Tomislav; Aleksi, Ivan; Hocenski, Željko; Kraus, Dieter

    2018-05-01

    In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Analysis Of Segmental Duplications In The Pig Genome Based On Next-Generation Sequencing

    DEFF Research Database (Denmark)

    Fadista, João; Bendixen, Christian

    Segmental duplications are >1kb segments of duplicated DNA present in a genome with high sequence identity (>90%). They are associated with genomic rearrangements and provide a significant source of gene and genome evolution within mammalian genomes. Although segmental duplications have been...... extensively studied in other organisms, its analysis in pig has been hampered by the lack of a complete pig genome assembly. By measuring the depth of coverage of Illumina whole-genome shotgun sequencing reads of the Tabasco animal aligned to the latest pig genome assembly (Sus scrofa 10 – based also...... and their associated copy number alterations, focusing on the global organization of these segments and their possible functional significance in porcine phenotypes. This work provides insights into mammalian genome evolution and generates a valuable resource for porcine genomics research...

  14. Generative adversarial networks for brain lesion detection

    Science.gov (United States)

    Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

    2017-02-01

    Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

  15. [Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

    Science.gov (United States)

    Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

    2018-01-01

    Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

  16. Left atrial appendage segmentation and quantitative assisted diagnosis of atrial fibrillation based on fusion of temporal-spatial information.

    Science.gov (United States)

    Jin, Cheng; Feng, Jianjiang; Wang, Lei; Yu, Heng; Liu, Jiang; Lu, Jiwen; Zhou, Jie

    2018-05-01

    In this paper, we present an approach for left atrial appendage (LAA) multi-phase fast segmentation and quantitative assisted diagnosis of atrial fibrillation (AF) based on 4D-CT data. We take full advantage of the temporal dimension information to segment the living, flailed LAA based on a parametric max-flow method and graph-cut approach to build 3-D model of each phase. To assist the diagnosis of AF, we calculate the volumes of 3-D models, and then generate a "volume-phase" curve to calculate the important dynamic metrics: ejection fraction, filling flux, and emptying flux of the LAA's blood by volume. This approach demonstrates more precise results than the conventional approaches that calculate metrics by area, and allows for the quick analysis of LAA-volume pattern changes of in a cardiac cycle. It may also provide insight into the individual differences in the lesions of the LAA. Furthermore, we apply support vector machines (SVMs) to achieve a quantitative auto-diagnosis of the AF by exploiting seven features from volume change ratios of the LAA, and perform multivariate logistic regression analysis for the risk of LAA thrombosis. The 100 cases utilized in this research were taken from the Philips 256-iCT. The experimental results demonstrate that our approach can construct the 3-D LAA geometries robustly compared to manual annotations, and reasonably infer that the LAA undergoes filling, emptying and re-filling, re-emptying in a cardiac cycle. This research provides a potential for exploring various physiological functions of the LAA and quantitatively estimating the risk of stroke in patients with AF. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. A Fully Automated Penumbra Segmentation Tool

    DEFF Research Database (Denmark)

    Nagenthiraja, Kartheeban; Ribe, Lars Riisgaard; Hougaard, Kristina Dupont

    2012-01-01

    Introduction: Perfusion- and diffusion weighted MRI (PWI/DWI) is widely used to select patients who are likely to benefit from recanalization therapy. The visual identification of PWI-DWI-mismatch tissue depends strongly on the observer, prompting a need for software, which estimates potentially...... salavageable tissue, quickly and accurately. We present a fully Automated Penumbra Segmentation (APS) algorithm using PWI and DWI images. We compare automatically generated PWI-DWI mismatch mask to mask outlined manually by experts, in 168 patients. Method: The algorithm initially identifies PWI lesions......) at 600∙10-6 mm2/sec. Due to the nature of thresholding, the ADC mask overestimates the DWI lesion volume and consequently we initialized level-set algorithm on DWI image with ADC mask as prior knowledge. Combining the PWI and inverted DWI mask then yield the PWI-DWI mismatch mask. Four expert raters...

  18. Transfer learning improves supervised image segmentation across imaging protocols.

    Science.gov (United States)

    van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

    2015-05-01

    The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

  19. Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

    Directory of Open Access Journals (Sweden)

    Erik Cuevas

    2015-01-01

    Full Text Available As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1 they frequently obtain suboptimal results (misclassifications as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2 the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS, is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness.

  20. Ultrasound Common Carotid Artery Segmentation Based on Active Shape Model

    Science.gov (United States)

    Yang, Xin; Jin, Jiaoying; Xu, Mengling; Wu, Huihui; He, Wanji; Yuchi, Ming; Ding, Mingyue

    2013-01-01

    Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression. PMID:23533535