WorldWideScience

Sample records for anatomy segmentation algorithm

  1. Image Segmentation Algorithms Overview

    OpenAIRE

    Yuheng, Song; Hao, Yan

    2017-01-01

    The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a predi...

  2. Exploitation of petiole, nodal segment, bulbil and tuber anatomy for ...

    African Journals Online (AJOL)

    All slides were examined under the light microscope at x100 and x400 magnifications and photos were taken using digital camera mounted on Zenith Ultra-500 A light microscope. Petiole and nodal segments anatomy showed six and nine vascular bundles, respectively in D. hirtiflora, whereas eight and eleven bundles ...

  3. An efficient algorithm for color image segmentation

    Directory of Open Access Journals (Sweden)

    Shikha Yadav

    2016-09-01

    Full Text Available In field of image processing, image segmentation plays an important role that focus on splitting the whole image into segments. Representation of an image so that it can be more easily analysed and involves more information is an important segmentation goal. The process of partitioning an image can be usually realized by Region based, Boundary based or edge based method. In this work a hybrid approach is followed that combines improved bee colony optimization and Tabu search for color image segmentation. The results produced from this hybrid approach are compared with non-sorted particle swarm optimization, non-sorted genetic algorithm and improved bee colony optimization. Results show that the Hybrid algorithm has better or somewhat similar performance as compared to other algorithms that are based on population. The algorithm is successfully implemented on MATLAB.

  4. A segmentation algorithm for noisy images

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Y.; Olman, V.; Uberbacher, E.C.

    1996-12-31

    This paper presents a 2-D image segmentation algorithm and addresses issues related to its performance on noisy images. The algorithm segments an image by first constructing a minimum spanning tree representation of the image and then partitioning the spanning tree into sub-trees representing different homogeneous regions. The spanning tree is partitioned in such a way that the sum of gray-level variations over all partitioned subtrees is minimized under the constraints that each subtree has at least a specified number of pixels and two adjacent subtrees have significantly different ``average`` gray-levels. Two types of noise, transmission errors and Gaussian additive noise. are considered and their effects on the segmentation algorithm are studied. Evaluation results have shown that the segmentation algorithm is robust in the presence of these two types of noise.

  5. Interactive segmentation techniques algorithms and performance evaluation

    CERN Document Server

    He, Jia; Kuo, C-C Jay

    2013-01-01

    This book focuses on interactive segmentation techniques, which have been extensively studied in recent decades. Interactive segmentation emphasizes clear extraction of objects of interest, whose locations are roughly indicated by human interactions based on high level perception. This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection. A comparative analysis of these methods will be provided

  6. Image segmentation algorithm based on improved PCNN

    Science.gov (United States)

    Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui

    2017-11-01

    A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

  7. Linac design algorithm with symmetric segments

    International Nuclear Information System (INIS)

    Takeda, Harunori; Young, L.M.; Nath, S.; Billen, J.H.; Stovall, J.E.

    1996-01-01

    The cell lengths in linacs of traditional design are typically graded as a function of particle velocity. By making groups of cells and individual cells symmetric in both the CCDTL AND CCL, the cavity design as well as mechanical design and fabrication is simplified without compromising the performance. We have implemented a design algorithm in the PARMILA code in which cells and multi-cavity segments are made symmetric, significantly reducing the number of unique components. Using the symmetric algorithm, a sample linac design was generated and its performance compared with a similar one of conventional design

  8. Segmentation of liver and spleen based on computational anatomy models.

    Science.gov (United States)

    Dong, Chunhua; Chen, Yen-Wei; Foruzan, Amir Hossein; Lin, Lanfen; Han, Xian-Hua; Tateyama, Tomoko; Wu, Xing; Xu, Gang; Jiang, Huiyan

    2015-12-01

    Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs (p<0.00001). Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. 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).

  10. 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.)

  11. An Objective Evaluation of Four SAR Image Segmentation Algorithms

    National Research Council Canada - National Science Library

    Gregga, Jason

    2001-01-01

    .... A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods...

  12. Adaptation of the Maracas algorithm for carotid artery segmentation and stenosis quantification on CT images

    International Nuclear Information System (INIS)

    Maria A Zuluaga; Maciej Orkisz; Edgar J F Delgado; Vincent Dore; Alfredo Morales Pinzon; Marcela Hernandez Hoyos

    2010-01-01

    This paper describes the adaptations of Maracas algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The maracas algorithm, which is based on an elastic model and on a multi-scale Eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT images and the bifurcations. The algorithms implemented in this new version are classified into two levels. 1. The low-level processing (filtering of noise and directional artifacts, enhancement and pre-segmentation) to improve the quality of the image and to pre-segment it. These techniques are based on a priori information about noise, artifacts and typical gray levels ranges of lumen, background and calcifications. 2. The high-level processing to extract the centerline of the artery, to segment the lumen and to quantify the stenosis. At this level, we apply a priori knowledge of shape and anatomy of vascular structures. The method was evaluated on 31 datasets from the carotid lumen segmentation and stenosis grading grand challenge 2009. The segmentation results obtained an average of 80:4% dice similarity score, compared to reference segmentation, and the mean stenosis quantification error was 14.4%.

  13. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    Science.gov (United States)

    A., Javadpour; A., Mohammadi

    2016-01-01

    Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

  14. 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.

  15. 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.

  16. Efficient Algorithms for Segmentation of Item-Set Time Series

    Science.gov (United States)

    Chundi, Parvathi; Rosenkrantz, Daniel J.

    We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

  17. Automated segment matching algorithm-theory, test, and evaluation

    Science.gov (United States)

    Kalcic, M. T. (Principal Investigator)

    1982-01-01

    Results to automate the U.S. Department of Agriculture's process of segment shifting and obtain results within one-half pixel accuracy are presented. Given an initial registration, the digitized segment is shifted until a more precise fit to the LANDSAT data is found. The algorithm automates the shifting process and performs certain tests for matching and accepting the computed shift numbers. Results indicate the algorithm can obtain results within one-half pixel accuracy.

  18. Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

    Directory of Open Access Journals (Sweden)

    Saadia Zahid

    2015-01-01

    Full Text Available Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs with artificial neural networks (ANNs. Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.

  19. Multilevel Image Segmentation Based on an Improved Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2016-01-01

    Full Text Available Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

  20. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    International Nuclear Information System (INIS)

    Chen, Xinjian; Bagci, Ulas

    2011-01-01

    Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al.[Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 deg. and 0.03, and over all foot bones are about 3.5709 mm, 0.35 deg. and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and

  1. 3D automatic anatomy segmentation based on iterative graph-cut-ASM.

    Science.gov (United States)

    Chen, Xinjian; Bagci, Ulas

    2011-08-01

    This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for

  2. 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.

  3. The speech signal segmentation algorithm using pitch synchronous analysis

    Directory of Open Access Journals (Sweden)

    Amirgaliyev Yedilkhan

    2017-03-01

    Full Text Available Parameterization of the speech signal using the algorithms of analysis synchronized with the pitch frequency is discussed. Speech parameterization is performed by the average number of zero transitions function and the signal energy function. Parameterization results are used to segment the speech signal and to isolate the segments with stable spectral characteristics. Segmentation results can be used to generate a digital voice pattern of a person or be applied in the automatic speech recognition. Stages needed for continuous speech segmentation are described.

  4. MAVEN: An Algorithm for Multi-Parametric Automated Segmentation of Brain Veins From Gradient Echo Acquisitions.

    Science.gov (United States)

    Monti, Serena; Cocozza, Sirio; Borrelli, Pasquale; Straub, Sina; Ladd, Mark E; Salvatore, Marco; Tedeschi, Enrico; Palma, Giuseppe

    2017-05-01

    Cerebral vein analysis provides a chance to study, from an unusual viewpoint, an entire class of brain diseases, including neurodegenerative disorders and traumatic brain injuries. Manual segmentation approaches can be used to assess vascular anatomy, but they are observer-dependent and time-consuming; therefore, automated approaches are desirable, as they also improve reproducibility. In this paper, a new, fully automated algorithm, based on structural, morphological, and relaxometric information, is proposed to segment the entire cerebral venous system from MR images. The algorithm for multi-parametric automated segmentation of brain VEiNs (MAVEN) is based on a combined investigation of multi-parametric information that allows for rejection of false positives and detection of thin vessels. The method is tested on gradient echo brain data sets acquired at 1.5, 3, and 7 T. It is compared to previous methods against manual segmentation, and its inter-scan reproducibility is assessed. The achieved accuracy and reproducibility are good, meaning that MAVEN outperforms previous methods on both quantitative and qualitative analyses. It is usable at all the field strengths explored, showing comparable accuracy scores, with no need for algorithm parameter adjustments, and thus, it is a promising candidate for the characterization of the venous tree topology.

  5. AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

    Directory of Open Access Journals (Sweden)

    V.G. Biju

    2015-11-01

    Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

  6. Performance evaluation of image segmentation algorithms on microscopic image data.

    Science.gov (United States)

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

    In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and 'the best' method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross-section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category - biological samples - is shown. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

  7. FCM Clustering Algorithms for Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    Yogita K. Dubey

    2016-01-01

    Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

  8. Particle segmentation algorithm for flexible single particle reconstruction.

    Science.gov (United States)

    Zhou, Qiang; Zhou, Niyun; Wang, Hong-Wei

    2017-01-01

    As single particle cryo-electron microscopy has evolved to a new era of atomic resolution, sample heterogeneity still imposes a major limit to the resolution of many macromolecular complexes, especially those with continuous conformational flexibility. Here, we describe a particle segmentation algorithm towards solving structures of molecules composed of several parts that are relatively flexible with each other. In this algorithm, the different parts of a target molecule are segmented from raw images according to their alignment information obtained from a preliminary 3D reconstruction and are subjected to single particle processing in an iterative manner. This algorithm was tested on both simulated and experimental data and showed improvement of 3D reconstruction resolution of each segmented part of the molecule than that of the entire molecule.

  9. Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology

    Directory of Open Access Journals (Sweden)

    Guanghua Zhang

    2016-09-01

    Full Text Available This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection.

  10. A novel algorithm for segmentation of brain MR images

    International Nuclear Information System (INIS)

    Sial, M.Y.; Yu, L.; Chowdhry, B.S.; Rajput, A.Q.K.; Bhatti, M.I.

    2006-01-01

    Accurate and fully automatic segmentation of brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of . attention lately. Many researchers have applied various techniques however a standard fuzzy c-means algorithm has produced better results compared to other methods. In this paper, we present a modified fuzzy c-means (FCM) based algorithm for segmentation of brain MR images. Our algorithm is formulated by modifying the objective function of the standard FCM and uses a special spread method to get a smooth and slow varying bias field This method has the advantage that it can be applied at an early stage in an automated data analysis before a tissue model is available. The results on MRI images show that this method provides better results compared to standard FCM algorithms. (author)

  11. 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

  12. Segmentation of kidney using C-V model and anatomy priors

    Science.gov (United States)

    Lu, Jinghua; Chen, Jie; Zhang, Juan; Yang, Wenjia

    2007-12-01

    This paper presents an approach for kidney segmentation on abdominal CT images as the first step of a virtual reality surgery system. Segmentation for medical images is often challenging because of the objects' complicated anatomical structures, various gray levels, and unclear edges. A coarse to fine approach has been applied in the kidney segmentation using Chan-Vese model (C-V model) and anatomy prior knowledge. In pre-processing stage, the candidate kidney regions are located. Then C-V model formulated by level set method is applied in these smaller ROI, which can reduce the calculation complexity to a certain extent. At last, after some mathematical morphology procedures, the specified kidney structures have been extracted interactively with prior knowledge. The satisfying results on abdominal CT series show that the proposed approach keeps all the advantages of C-V model and overcome its disadvantages.

  13. Video Segmentation Using Fast Marching and Region Growing Algorithms

    Directory of Open Access Journals (Sweden)

    Eftychis Sifakis

    2002-04-01

    Full Text Available The algorithm presented in this paper is comprised of three main stages: (1 classification of the image sequence and, in the case of a moving camera, parametric motion estimation, (2 change detection having as reference a fixed frame, an appropriately selected frame or a displaced frame, and (3 object localization using local colour features. The image sequence classification is based on statistical tests on the frame difference. The change detection module uses a two-label fast marching algorithm. Finally, the object localization uses a region growing algorithm based on the colour similarity. Video object segmentation results are shown using the COST 211 data set.

  14. 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.

  15. 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.

  16. Modified cuckoo search algorithm in microscopic image segmentation of hippocampus.

    Science.gov (United States)

    Chakraborty, Shouvik; Chatterjee, Sankhadeep; Dey, Nilanjan; Ashour, Amira S; Ashour, Ahmed S; Shi, Fuqian; Mali, Kalyani

    2017-10-01

    Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR. © 2017 Wiley Periodicals, Inc.

  17. Iris Segmentation and Normalization Algorithm Based on Zigzag Collarette

    Science.gov (United States)

    Rizky Faundra, M.; Ratna Sulistyaningrum, Dwi

    2017-01-01

    In this paper, we proposed iris segmentation and normalization algorithm based on the zigzag collarette. First of all, iris images are processed by using Canny Edge Detection to detect pupil edge, then finding the center and the radius of the pupil with the Hough Transform Circle. Next, isolate important part in iris based zigzag collarette area. Finally, Daugman Rubber Sheet Model applied to get the fixed dimensions or normalization iris by transforming cartesian into polar format and thresholding technique to remove eyelid and eyelash. This experiment will be conducted with a grayscale eye image data taken from a database of iris-Chinese Academy of Sciences Institute of Automation (CASIA). Data iris taken is the data reliable and widely used to study the iris biometrics. The result show that specific threshold level is 0.3 have better accuracy than other, so the present algorithm can be used to segmentation and normalization zigzag collarette with accuracy is 98.88%

  18. Fast Superpixel Segmentation Algorithm for PolSAR Images

    Directory of Open Access Journals (Sweden)

    Zhang Yue

    2017-10-01

    Full Text Available As a pre-processing technique, superpixel segmentation algorithms should be of high computational efficiency, accurate boundary adherence and regular shape in homogeneous regions. A fast superpixel segmentation algorithm based on Iterative Edge Refinement (IER has shown to be applicable on optical images. However, it is difficult to obtain the ideal results when IER is applied directly to PolSAR images due to the speckle noise and small or slim regions in PolSAR images. To address these problems, in this study, the unstable pixel set is initialized as all the pixels in the PolSAR image instead of the initial grid edge pixels. In the local relabeling of the unstable pixels, the fast revised Wishart distance is utilized instead of the Euclidean distance in CIELAB color space. Then, a post-processing procedure based on dissimilarity measure is empolyed to remove isolated small superpixels as well as to retain the strong point targets. Finally, extensive experiments based on a simulated image and a real-world PolSAR image from Airborne Synthetic Aperture Radar (AirSAR are conducted, showing that the proposed algorithm, compared with three state-of-the-art methods, performs better in terms of several commonly used evaluation criteria with high computational efficiency, accurate boundary adherence, and homogeneous regularity.

  19. The Galileo Ground Segment Integrity Algorithms: Design and Performance

    Directory of Open Access Journals (Sweden)

    Carlos Hernández Medel

    2008-01-01

    Full Text Available Galileo, the European Global Navigation Satellite System, will provide to its users highly accurate global positioning services and their associated integrity information. The element in charge of the computation of integrity messages within the Galileo Ground Mission Segment is the integrity processing facility (IPF, which is developed by GMV Aerospace and Defence. The main objective of this paper is twofold: to present the integrity algorithms implemented in the IPF and to show the achieved performance with the IPF software prototype, including aspects such as: implementation of the Galileo overbounding concept, impact of safety requirements on the algorithm design including the threat models for the so-called feared events, and finally the achieved performance with real GPS and simulated Galileo scenarios.

  20. Sensitivity field distributions for segmental bioelectrical impedance analysis based on real human anatomy

    Science.gov (United States)

    Danilov, A. A.; Kramarenko, V. K.; Nikolaev, D. V.; Rudnev, S. G.; Salamatova, V. Yu; Smirnov, A. V.; Vassilevski, Yu V.

    2013-04-01

    In this work, an adaptive unstructured tetrahedral mesh generation technology is applied for simulation of segmental bioimpedance measurements using high-resolution whole-body model of the Visible Human Project man. Sensitivity field distributions for a conventional tetrapolar, as well as eight- and ten-electrode measurement configurations are obtained. Based on the ten-electrode configuration, we suggest an algorithm for monitoring changes in the upper lung area.

  1. An Accurate liver segmentation method using parallel computing algorithm

    International Nuclear Information System (INIS)

    Elbasher, Eiman Mohammed Khalied

    2014-12-01

    Computed Tomography (CT or CAT scan) is a noninvasive diagnostic imaging procedure that uses a combination of X-rays and computer technology to produce horizontal, or axial, images (often called slices) of the body. A CT scan shows detailed images of any part of the body, including the bones muscles, fat and organs CT scans are more detailed than standard x-rays. CT scans may be done with or without c ontrast Contrast refers to a substance taken by mouth and/ or injected into an intravenous (IV) line that causes the particular organ or tissue under study to be seen more clearly. CT scan of the liver and biliary tract are used in the diagnosis of many diseases in the abdomen structures, particularly when another type of examination, such as X-rays, physical examination, and ultra sound is not conclusive. Unfortunately, the presence of noise and artifact in the edges and fine details in the CT images limit the contrast resolution and make diagnostic procedure more difficult. This experimental study was conducted at the College of Medical Radiological Science, Sudan University of Science and Technology and Fidel Specialist Hospital. The sample of study was included 50 patients. The main objective of this research was to study an accurate liver segmentation method using a parallel computing algorithm, and to segment liver and adjacent organs using image processing technique. The main technique of segmentation used in this study was watershed transform. The scope of image processing and analysis applied to medical application is to improve the quality of the acquired image and extract quantitative information from medical image data in an efficient and accurate way. The results of this technique agreed wit the results of Jarritt et al, (2010), Kratchwil et al, (2010), Jover et al, (2011), Yomamoto et al, (1996), Cai et al (1999), Saudha and Jayashree (2010) who used different segmentation filtering based on the methods of enhancing the computed tomography images. Anther

  2. Bladder segmentation in MR images with watershed segmentation and graph cut algorithm

    Science.gov (United States)

    Blaffert, Thomas; Renisch, Steffen; Schadewaldt, Nicole; Schulz, Heinrich; Wiemker, Rafael

    2014-03-01

    Prostate and cervix cancer diagnosis and treatment planning that is based on MR images benefit from superior soft tissue contrast compared to CT images. For these images an automatic delineation of the prostate or cervix and the organs at risk such as the bladder is highly desirable. This paper describes a method for bladder segmentation that is based on a watershed transform on high image gradient values and gray value valleys together with the classification of watershed regions into bladder contents and tissue by a graph cut algorithm. The obtained results are superior if compared to a simple region-after-region classification.

  3. The implement of Talmud property allocation algorithm based on graphic point-segment way

    Science.gov (United States)

    Cen, Haifeng

    2017-04-01

    Under the guidance of the Talmud allocation scheme's theory, the paper analyzes the algorithm implemented process via the perspective of graphic point-segment way, and designs the point-segment way's Talmud property allocation algorithm. Then it uses Java language to implement the core of allocation algorithm, by using Android programming to build a visual interface.

  4. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.

    Science.gov (United States)

    Sun, Kaiqiong; Udupa, Jayaram K; Odhner, Dewey; Tong, Yubing; Zhao, Liming; Torigian, Drew A

    2016-03-01

    In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed

  5. A wavelet relational fuzzy C-means algorithm for 2D gel image segmentation.

    Science.gov (United States)

    Rashwan, Shaheera; Faheem, Mohamed Talaat; Sarhan, Amany; Youssef, Bayumy A B

    2013-01-01

    One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

  6. A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation

    Directory of Open Access Journals (Sweden)

    Shaheera Rashwan

    2013-01-01

    Full Text Available One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM and the Wavelet Fuzzy C-Means (WFCM to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases the quality of the segmentation.

  7. Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm.

    Science.gov (United States)

    Song, Hong; Kang, Wei; Zhang, Qian; Wang, Shuliang

    2015-01-01

    Organ segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures. In this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods. Our method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification.

  8. A New Segment Building Algorithm for the Cathode Strip Chambers in the CMS Experiment

    Directory of Open Access Journals (Sweden)

    Golutvin I.

    2016-01-01

    Full Text Available A new segment building algorithm for the Cathode Strip Chambers in the CMS experiment is presented. A detailed description of the new algorithm is given along with a comparison with the algorithm used in the CMS software. The new segment builder was tested with different Monte-Carlo data samples. The new algorithm is meant to be robust and effective for hard muons and the higher luminosity that is expected in the future at the LHC.

  9. Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial

    International Nuclear Information System (INIS)

    Geraghty, John P; Grogan, Garry; Ebert, Martin A

    2013-01-01

    algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset adds a significant systematic bias to the resulting volumes and their use in the context of a multicentre trial should be carefully considered

  10. Categorizing segmentation quality using a quantitative quality assurance algorithm

    International Nuclear Information System (INIS)

    Rodrigues, George; Louie, Alexander; Best, Lara

    2012-01-01

    Obtaining high levels of contouring consistency is a major limiting step in optimizing the radiotherapeutic ratio. We describe a novel quantitative methodology for the quality assurance (QA) of contour compliance referenced against a community set of contouring experts. Two clinical tumour site scenarios (10 lung cases and one prostate case) were used with QA algorithm. For each case, multiple physicians (lung: n = 6, prostate: n = 25) segmented various target/organ at risk (OAR) structures to define a set of community reference contours. For each set of community contours, a consensus contour (Simultaneous Truth and Performance Level Estimation (STAPLE)) was created. Differences between each individual community contour versus the group consensus contour were quantified by consensus-based contouring penalty metric (PM) scores. New observers segmented these same cases to calculate individual PM scores (for each unique target/OAR) for each new observer–STAPLE pair for comparison against the community and consensus contours. Four physicians contoured the 10 lung cases for a total of 72 contours for quality assurance evaluation against the previously derived community consensus contours. A total of 16 outlier contours were identified by the QA system of which 11 outliers were due to over-contouring discrepancies, three were due to over-/under-contouring discrepancies, and two were due to missing/incorrect nodal contours. In the prostate scenario involving six physicians, the QA system detected a missing penile bulb contour, systematic inner-bladder contouring, and under-contouring of the upper/anterior rectum. A practical methodology for QA has been demonstrated with future clinical trial credentialing, medical education and auto-contouring assessment applications.

  11. A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

    Science.gov (United States)

    Liu, Likun

    2018-01-01

    In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

  12. A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights.

    Science.gov (United States)

    Akhondi-Asl, Alireza; Hoyte, Lennox; Lockhart, Mark E; Warfield, Simon K

    2014-10-01

    Pelvic floor dysfunction is common in women after childbirth and precise segmentation of magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment of patients. However, because of the complexity of its structures, manual segmentation of the pelvic floor is challenging and suffers from high inter and intra-rater variability of expert raters. Multiple template fusion algorithms are promising segmentation techniques for these types of applications, but they have been limited by imperfections in the alignment of templates to the target, and by template segmentation errors. A number of algorithms sought to improve segmentation performance by combining image intensities and template labels as two independent sources of information, carrying out fusion through local intensity weighted voting schemes. This class of approach is a form of linear opinion pooling, and achieves unsatisfactory performance for this application. We hypothesized that better decision fusion could be achieved by assessing the contribution of each template in comparison to a reference standard segmentation of the target image and developed a novel segmentation algorithm to enable automatic segmentation of MRI of the female pelvic floor. The algorithm achieves high performance by estimating and compensating for both imperfect registration of the templates to the target image and template segmentation inaccuracies. A local image similarity measure is used to infer a local reliability weight, which contributes to the fusion through a novel logarithmic opinion pooling. We evaluated our new algorithm in comparison to nine state-of-the-art segmentation methods and demonstrated our algorithm achieves the highest performance.

  13. A semi-supervised segmentation algorithm as applied to k-means ...

    African Journals Online (AJOL)

    In this paper a semi-supervised segmentation algorithm is presented, which is based on k-means clustering and which applies information value for the purpose of informing the segmentation process. Simulated data are used to identify a few key characteristics that may cause one segmentation technique to outperform ...

  14. Wound size measurement of lower extremity ulcers using segmentation algorithms

    Science.gov (United States)

    Dadkhah, Arash; Pang, Xing; Solis, Elizabeth; Fang, Ruogu; Godavarty, Anuradha

    2016-03-01

    Lower extremity ulcers are one of the most common complications that not only affect many people around the world but also have huge impact on economy since a large amount of resources are spent for treatment and prevention of the diseases. Clinical studies have shown that reduction in the wound size of 40% within 4 weeks is an acceptable progress in the healing process. Quantification of the wound size plays a crucial role in assessing the extent of healing and determining the treatment process. To date, wound healing is visually inspected and the wound size is measured from surface images. The extent of wound healing internally may vary from the surface. A near-infrared (NIR) optical imaging approach has been developed for non-contact imaging of wounds internally and differentiating healing from non-healing wounds. Herein, quantitative wound size measurements from NIR and white light images are estimated using a graph cuts and region growing image segmentation algorithms. The extent of the wound healing from NIR imaging of lower extremity ulcers in diabetic subjects are quantified and compared across NIR and white light images. NIR imaging and wound size measurements can play a significant role in potentially predicting the extent of internal healing, thus allowing better treatment plans when implemented for periodic imaging in future.

  15. Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach

    Science.gov (United States)

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

    2010-02-01

    Apparent Diffusion Coefficient (ADC) of lesions obtained from Diffusion Weighted Magnetic Resonance Imaging is an emerging biomarker for evaluating anti-cancer therapy response. To compute the lesion's ADC, accurate lesion segmentation must be performed. To quantitatively compare these lesion segmentation algorithms, standard methods are used currently. However, the end task from these images is accurate ADC estimation, and these standard methods don't evaluate the segmentation algorithms on this task-based measure. Moreover, standard methods rely on the highly unlikely scenario of there being perfectly manually segmented lesions. In this paper, we present two methods for quantitatively comparing segmentation algorithms on the above task-based measure; the first method compares them given good manual segmentations from a radiologist, the second compares them even in absence of good manual segmentations.

  16. Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism.

    Science.gov (United States)

    Wang, Li; Li, Gang; Adeli, Ehsan; Liu, Mingxia; Wu, Zhengwang; Meng, Yu; Lin, Weili; Shen, Dinggang

    2018-03-08

    Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness. © 2018 Wiley Periodicals, Inc.

  17. Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    R. V. V. Krishna

    2016-10-01

    Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.

  18. An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding

    OpenAIRE

    Wang, Fujun; Li, Junlan; Liu, Shiwei; Zhao, Xingyu; Zhang, Dawei; Tian, Yanling

    2014-01-01

    In order to improve the precision and efficiency of microelectronic bonding, this paper presents an improved adaptive genetic algorithm (IAGA) for the image segmentation and vision alignment of the solder joints in the microelectronic chips. The maximum between-cluster variance (OTSU) threshold segmentation method was adopted for the image segmentation of microchips, and the IAGA was introduced to the threshold segmentation considering the features of the images. The performance of the image ...

  19. Optimization of segment weight using simulated dynamics algorithm for beamlet-based IMRT

    International Nuclear Information System (INIS)

    Chen Bingzhou; Hou Qing

    2007-01-01

    With accurate calculation algorithms in inverse planning for beamlet-based intensity modulated radiotherapy (IMRT), it takes time to calculate the dose matrix, which represents the dose distribution of each beamlet element to each voxel for unit fluence. To reduce the calculation time, coarse or approximate algorithms are often a choice, but this results in a final dose distribution that cannot reflect the real value. In addition, it is necessary to test if a coarse algorithm is capable of calculating the dose matrix of beamlets. In this work, simulated dynamics optimization algorithm was applied to optimize the segment weight to minish the dose error from the dose matrix calculation. After calculating the dose matrix by ray-tracing algorithm which takes into account just the primary component of absorbed dose, the original beam profile intensity distribution was optimized by using the simulated dynamics algorithm. Before segmentation, the even-spaced algorithm and genetic algorithm were applied in clustering. The dose distribution of every segment was calculated accurately by using convolution-superposition algorithm, and the weight of any voxel was in inverse proportion to the voxel number of the PTV or OAR it belonged. The segment weight was optimized by using the simulated dynamics algorithm. By comparing the dose distributions before and after optimization of segment weight, one finds that the dose distribution is improved obviously., It was also found that some segments which contained fewer beamlets or lower weight value could be omitted because the decay of dose distribution could be improved by re-optimizing the segment weight, and that some segments could be omitted by re-optimizing the segment weight during the process of beamlet-based IMRT. (authors)

  20. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

    Science.gov (United States)

    Blessy, S A Praylin Selva; Sulochana, C Helen

    2015-01-01

    Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

  1. Extended-Maxima Transform Watershed Segmentation Algorithm for Touching Corn Kernels

    Directory of Open Access Journals (Sweden)

    Yibo Qin

    2013-01-01

    Full Text Available Touching corn kernels are usually oversegmented by the traditional watershed algorithm. This paper proposes a modified watershed segmentation algorithm based on the extended-maxima transform. Firstly, a distance-transformed image is processed by the extended-maxima transform in the range of the optimized threshold value. Secondly, the binary image obtained by the preceding process is run through the watershed segmentation algorithm, and watershed ridge lines are superimposed on the original image, so that touching corn kernels are separated into segments. Fifty images which all contain 400 corn kernels were tested. Experimental results showed that the effect of segmentation is satisfactory by the improved algorithm, and the accuracy of segmentation is as high as 99.87%.

  2. 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.

  3. A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

    International Nuclear Information System (INIS)

    Zhou Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J.

    2010-01-01

    Purpose: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. Methods: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. Results: The ACRASM segmentation algorithm was compared to the original active shape model (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to

  4. A Combined Coefficient Segmentation and Block Processing Algorithm for Low Power Implementation of FIR Digital Filters

    Directory of Open Access Journals (Sweden)

    A. T. Erdogan

    2002-01-01

    Full Text Available A combined coefficient segmentation and block processing algorithm for low power implementation of FIR digital filters is described in this paper. The algorithm processes data and coefficients in blocks of fixed sizes. During the manipulation of each block, coefficients are segmented into two primitive components. The accumulative effect of processing a sequence of blocks and segmentation results in up to 80% reduction in power consumption in the multiplier circuit compared to conventional filtering. The paper describes the implementation of the algorithm, its constituent components, and the power evaluation environment developed. Simulations are performed using eight practical digital filter examples with various filter orders and data/coefficient wordlengths. In addition, the algorithm is compared with conventional filtering implementations and those using block processing and coefficient segmentation algorithms alone.

  5. Efficient Active Contour and K-Means Algorithms in Image Segmentation

    Directory of Open Access Journals (Sweden)

    J.R. Rommelse

    2004-01-01

    Full Text Available In this paper we discuss a classic clustering algorithm that can be used to segment images and a recently developed active contour image segmentation model. We propose integrating aspects of the classic algorithm to improve the active contour model. For the resulting CVK and B-means segmentation algorithms we examine methods to decrease the size of the image domain. The CVK method has been implemented to run on parallel and distributed computers. By changing the order of updating the pixels, it was possible to replace synchronous communication with asynchronous communication and subsequently the parallel efficiency is improved.

  6. Flame Image Segmentation Based on the Bee Colony Algorithm with Characteristics of Levy Flights

    Directory of Open Access Journals (Sweden)

    Xiaolin Zhang

    2015-01-01

    Full Text Available The real-time processing of the image segmentation method with accuracy is very important in the application of the flame image detection system. This paper considers a novel method for flame image segmentation. It is the bee colony algorithm with characteristics enhancement of Levy flights against the problems of the algorithm during segmentation, including long calculation time and poor stability. By introducing the idea of Levy flights, this method designs a new local search strategy. By setting the current optimal value and based on the collaboration between the populations, it reinforces the overall convergence speed. By adopting the new fitness evaluation method and combining it with the two-dimensional entropy multithreshold segmentation principle, this paper develops a threshold segmentation test of the flame image. Test results show that this method has some advantages in terms of accuracy of threshold selection and calculation time. The robustness of the algorithm meets the actual demands in the engineering application.

  7. A Class Of Iterative Thresholding Algorithms For Real-Time Image Segmentation

    Science.gov (United States)

    Hassan, M. H.

    1989-03-01

    Thresholding algorithms are developed for segmenting gray-level images under nonuniform illumination. The algorithms are based on learning models generated from recursive digital filters which yield to continuously varying threshold tracking functions. A real-time region growing algorithm, which locates the objects in the image while thresholding, is developed and implemented. The algorithms work in a raster-scan format, thus making them attractive for real-time image segmentation in situations requiring fast data throughput such as robot vision and character recognition.

  8. Segmentation of Mushroom and Cap width Measurement using Modified K-Means Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Eser Sert

    2014-01-01

    Full Text Available Mushroom is one of the commonly consumed foods. Image processing is one of the effective way for examination of visual features and detecting the size of a mushroom. We developed software for segmentation of a mushroom in a picture and also to measure the cap width of the mushroom. K-Means clustering method is used for the process. K-Means is one of the most successful clustering methods. In our study we customized the algorithm to get the best result and tested the algorithm. In the system, at first mushroom picture is filtered, histograms are balanced and after that segmentation is performed. Results provided that customized algorithm performed better segmentation than classical K-Means algorithm. Tests performed on the designed software showed that segmentation on complex background pictures is performed with high accuracy, and 20 mushrooms caps are measured with 2.281 % relative error.

  9. 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.

  10. A Review of Algorithms for Retinal Vessel Segmentation

    Directory of Open Access Journals (Sweden)

    Monserrate Intriago Pazmiño

    2014-10-01

    Full Text Available This paper presents a review of algorithms for extracting blood vessels network from retinal images. Since retina is a complex and delicate ocular structure, a huge effort in computer vision is devoted to study blood vessels network for helping the diagnosis of pathologies like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma. To carry out this process many works for normal and abnormal images have been proposed recently. These methods include combinations of algorithms like Gaussian and Gabor filters, histogram equalization, clustering, binarization, motion contrast, matched filters, combined corner/edge detectors, multi-scale line operators, neural networks, ants, genetic algorithms, morphological operators. To apply these algorithms pre-processing tasks are needed. Most of these algorithms have been tested on publicly retinal databases. We have include a table summarizing algorithms and results of their assessment.

  11. PRESEE: an MDL/MML algorithm to time-series stream segmenting.

    Science.gov (United States)

    Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

    2013-01-01

    Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

  12. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    International Nuclear Information System (INIS)

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Vermandel, Maximilien; Baillet, Clio

    2015-01-01

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging.Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used.Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results.The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging. (paper)

  13. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    Science.gov (United States)

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

    2015-12-01

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

  14. Understanding Spatially Complex Segmental and Branch Anatomy Using 3D Printing: Liver, Lung, Prostate, Coronary Arteries, and Circle of Willis.

    Science.gov (United States)

    Javan, Ramin; Herrin, Douglas; Tangestanipoor, Ardalan

    2016-09-01

    Three-dimensional (3D) manufacturing is shaping personalized medicine, in which radiologists can play a significant role, be it as consultants to surgeons for surgical planning or by creating powerful visual aids for communicating with patients, physicians, and trainees. This report illustrates the steps in development of custom 3D models that enhance the understanding of complex anatomy. We graphically designed 3D meshes or modified imported data from cross-sectional imaging to develop physical models targeted specifically for teaching complex segmental and branch anatomy. The 3D printing itself is easily accessible through online commercial services, and the models are made of polyamide or gypsum. Anatomic models of the liver, lungs, prostate, coronary arteries, and the Circle of Willis were created. These models have advantages that include customizable detail, relative low cost, full control of design focusing on subsegments, color-coding potential, and the utilization of cross-sectional imaging combined with graphic design. Radiologists have an opportunity to serve as leaders in medical education and clinical care with 3D printed models that provide beneficial interaction with patients, clinicians, and trainees across all specialties by proactively taking on the educator's role. Complex models can be developed to show normal anatomy or common pathology for medical educational purposes. There is a need for randomized trials, which radiologists can design, to demonstrate the utility and effectiveness of 3D printed models for teaching simple and complex anatomy, simulating interventions, measuring patient satisfaction, and improving clinical care. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  15. PREPAID TELECOM CUSTOMERS SEGMENTATION USING THE K-MEAN ALGORITHM

    Directory of Open Access Journals (Sweden)

    Marar Liviu Ioan

    2012-07-01

    Full Text Available The scope of relationship marketing is to retain customers and win their loyalty. This can be achieved if the companies’ products and services are developed and sold considering customers’ demands. Fulfilling customers’ demands, taken as the starting point of relationship marketing, can be obtained by acknowledging that the customers’ needs and wishes are heterogeneous. The segmentation of the customers’ base allows operators to overcome this because it illustrates the whole heterogeneous market as the sum of smaller homogeneous markets. The concept of segmentation relies on the high probability of persons grouped into segments based on common demands and behaviours to have a similar response to marketing strategies. This article focuses on the segmentation of a telecom customer base according to specific and noticeable criteria of a certain service. Although the segmentation concept is widely approached in professional literature, articles on the segmentation of a telecom customer base are very scarce, due to the strategic nature of this information. Market segmentation is carried out based on how customers spent their money on credit recharging, on making calls, on sending SMS and on Internet navigation. The method used for customer segmentation is the K-mean cluster analysis. To assess the internal cohesion of the clusters we employed the average sum of squares error indicator, and to determine the differences among the clusters we used the ANOVA and the post-hoc Tukey tests. The analyses revealed seven customer segments with different features and behaviours. The results enable the telecom company to conceive marketing strategies and planning which lead to better understanding of its customers’ needs and ultimately to a more efficient relationship with the subscribers and enhanced customer satisfaction. At the same time, the results enable the description and characterization of expenditure patterns

  16. Myocardial segmentation based on coronary anatomy using coronary computed tomography angiography: Development and validation in a pig model

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Mi Sun [Chung-Ang University College of Medicine, Department of Radiology, Chung-Ang University Hospital, Seoul (Korea, Republic of); Yang, Dong Hyun; Seo, Joon Beom; Kang, Joon-Won; Lim, Tae-Hwan [Asan Medical Center, University of Ulsan College of Medicine, Department of Radiology and Research Institute of Radiology, Seoul (Korea, Republic of); Kim, Young-Hak; Kang, Soo-Jin; Jung, Joonho [Asan Medical Center, University of Ulsan College of Medicine, Heart Institute, Seoul (Korea, Republic of); Kim, Namkug [Asan Medical Center, University of Ulsan College of Medicine, Department of Convergence Medicine, Seoul (Korea, Republic of); Heo, Seung-Ho [Asan Medical Center, University of Ulsan College of Medicine, Asan institute for Life Science, Seoul (Korea, Republic of); Baek, Seunghee [Asan Medical Center, University of Ulsan College of Medicine, Department of Clinical Epidemiology and Biostatistics, Seoul (Korea, Republic of); Choi, Byoung Wook [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea, Republic of)

    2017-10-15

    To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA). Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns. CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011). CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA. (orig.)

  17. An Algorithm for Morphological Segmentation of Esperanto Words

    Directory of Open Access Journals (Sweden)

    Guinard Theresa

    2016-04-01

    Full Text Available Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of-speech information is a useful preprocessing step in many natural language processing applications, especially for synthetic languages. Compound words from the constructed language Esperanto are formed by straightforward agglutination, but for many words, there is more than one possible sequence of component morphemes. However, one segmentation is usually more semantically probable than the others. This paper presents a modified n-gram Markov model that finds the most probable segmentation of any Esperanto word, where the model’s states represent morpheme part-of-speech and semantic classes. The overall segmentation accuracy was over 98% for a set of presegmented dictionary words.

  18. Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

    Science.gov (United States)

    Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

    2011-10-01

    Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

  19. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

    Science.gov (United States)

    Polan, Daniel F; Brady, Samuel L; Kaufman, Robert A

    2016-09-07

    There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 (n) , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21

  20. HARDWARE REALIZATION OF CANNY EDGE DETECTION ALGORITHM FOR UNDERWATER IMAGE SEGMENTATION USING FIELD PROGRAMMABLE GATE ARRAYS

    Directory of Open Access Journals (Sweden)

    ALEX RAJ S. M.

    2017-09-01

    Full Text Available Underwater images raise new challenges in the field of digital image processing technology in recent years because of its widespread applications. There are many tangled matters to be considered in processing of images collected from water medium due to the adverse effects imposed by the environment itself. Image segmentation is preferred as basal stage of many digital image processing techniques which distinguish multiple segments in an image and reveal the hidden crucial information required for a peculiar application. There are so many general purpose algorithms and techniques that have been developed for image segmentation. Discontinuity based segmentation are most promising approach for image segmentation, in which Canny Edge detection based segmentation is more preferred for its high level of noise immunity and ability to tackle underwater environment. Since dealing with real time underwater image segmentation algorithm, which is computationally complex enough, an efficient hardware implementation is to be considered. The FPGA based realization of the referred segmentation algorithm is presented in this paper.

  1. An improved optimum-path forest clustering algorithm for remote sensing image segmentation

    Science.gov (United States)

    Chen, Siya; Sun, Tieli; Yang, Fengqin; Sun, Hongguang; Guan, Yu

    2018-03-01

    Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.

  2. Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm. Chapter 5

    Science.gov (United States)

    Tilton, James C.; Plaza, Antonio J. (Editor); Chang, Chein-I. (Editor)

    2008-01-01

    The hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.

  3. COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2016-06-01

    Full Text Available This paper presents a Synthetic Aperture Radar (SAR image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC' algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.

  4. An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2014-01-01

    Full Text Available A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA. An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.

  5. An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection.

    Science.gov (United States)

    Saleh, Marwan D; Eswaran, C; Mueen, Ahmed

    2011-08-01

    This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

  6. Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation

    Science.gov (United States)

    Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

    2015-01-01

    Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it. PMID:26221133

  7. Performance evaluation of image segmentation algorithms on microscopic image data

    Czech Academy of Sciences Publication Activity Database

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

    Roč. 275, č. 1 (2015), s. 65-85 ISSN 0022-2720 R&D Projects: GA ČR GAP103/12/2211 Institutional support: RVO:67985556 Keywords : image segmentation * performance evaluation * microscopic image s Subject RIV: JC - Computer Hardware ; Software Impact factor: 2.136, year: 2015 http://library.utia.cas.cz/separaty/2014/ZOI/zitova-0434809-DOI.pdf

  8. Retinal Vessels Segmentation Techniques and Algorithms: A Survey

    Directory of Open Access Journals (Sweden)

    Jasem Almotiri

    2018-01-01

    Full Text Available Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR, glaucoma, hypertension, and Age-related Macular Degeneration (AMD. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.

  9. Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

    Science.gov (United States)

    Phellan, Renzo; Forkert, Nils D

    2017-11-01

    Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM

  10. Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes

    Science.gov (United States)

    Spechler, Joshua A.; Hoppe, Daniel J.; Sigrist, Norbert; Shi, Fang; Seo, Byoung-Joon; Bikkannavar, Siddarayappa A.

    2013-01-01

    Segment mirror phasing, a critical step of segment mirror alignment, requires the ability to sense and correct the relative pistons between segments from up to a few hundred microns to a fraction of wavelength in order to bring the mirror system to its full diffraction capability. When sampling the aperture of a telescope, using auto-collimating flats (ACFs) is more economical. The performance of a telescope with a segmented primary mirror strongly depends on how well those primary mirror segments can be phased. One such process to phase primary mirror segments in the axial piston direction is dispersed fringe sensing (DFS). DFS technology can be used to co-phase the ACFs. DFS is essentially a signal fitting and processing operation. It is an elegant method of coarse phasing segmented mirrors. DFS performance accuracy is dependent upon careful calibration of the system as well as other factors such as internal optical alignment, system wavefront errors, and detector quality. Novel improvements to the algorithm have led to substantial enhancements in DFS performance. The Advanced Dispersed Fringe Sensing (ADFS) Algorithm is designed to reduce the sensitivity to calibration errors by determining the optimal fringe extraction line. Applying an angular extraction line dithering procedure and combining this dithering process with an error function while minimizing the phase term of the fitted signal, defines in essence the ADFS algorithm.

  11. Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm.

    Science.gov (United States)

    Maglietta, Rosalia; Amoroso, Nicola; Boccardi, Marina; Bruno, Stefania; Chincarini, Andrea; Frisoni, Giovanni B; Inglese, Paolo; Redolfi, Alberto; Tangaro, Sabina; Tateo, Andrea; Bellotti, Roberto

    The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice's index of [Formula: see text] ([Formula: see text]) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.

  12. Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

    Science.gov (United States)

    Janaki, Sathya D.; Geetha, K.

    2017-06-01

    Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.

  13. An improved Marching Cube algorithm for 3D data segmentation

    Science.gov (United States)

    Masala, G. L.; Golosio, B.; Oliva, P.

    2013-03-01

    The marching cube algorithm is one of the most popular algorithms for isosurface triangulation. It is based on a division of the data volume into elementary cubes, followed by a standard triangulation inside each cube. In the original formulation, the marching cube algorithm is based on 15 basic triangulations and a total of 256 elementary triangulations are obtained from the basic ones by rotation, reflection, conjugation, and combinations of these operations. The original formulation of the algorithm suffers from well-known problems of connectivity among triangles of adjacent cubes, which has been solved in various ways. We developed a variant of the marching cube algorithm that makes use of 21 basic triangulations. Triangles of adjacent cubes are always well connected in this approach. The output of the code is a triangulated model of the isosurface in raw format or in VRML (Virtual Reality Modelling Language) format. Catalogue identifier: AENS_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENS_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 147558 No. of bytes in distributed program, including test data, etc.: 26084066 Distribution format: tar.gz Programming language: C. Computer: Pentium 4, CPU 3.2 GHz and 3.24 GB of RAM (2.77 GHz). Operating system: Tested on several Linux distribution, but generally works in all Linux-like platforms. RAM: Approximately 2 MB Classification: 6.5. Nature of problem: Given a scalar field μ(x,y,z) sampled on a 3D regular grid, build a discrete model of the isosurface associated to the isovalue μIso, which is defined as the set of points that satisfy the equation μ(x,y,z)=μIso. Solution method: The proposed solution is an improvement of the Marching Cube algorithm, which approximates the isosurface using a set of

  14. Performance evaluation of an algorithm for fast optimization of beam weights in anatomy-based intensity modulated radiotherapy

    International Nuclear Information System (INIS)

    Ranganathan, Vaitheeswaran; Sathiya Narayanan, V.K.; Bhangle, Janhavi R.; Gupta, Kamlesh K.; Basu, Sumit; Maiya, Vikram; Joseph, Jolly; Nirhali, Amit

    2010-01-01

    This study aims to evaluate the performance of a new algorithm for optimization of beam weights in anatomy-based intensity modulated radiotherapy (IMRT). The algorithm uses a numerical technique called Gaussian-Elimination that derives the optimum beam weights in an exact or non-iterative way. The distinct feature of the algorithm is that it takes only fraction of a second to optimize the beam weights, irrespective of the complexity of the given case. The algorithm has been implemented using MATLAB with a Graphical User Interface (GUI) option for convenient specification of dose constraints and penalties to different structures. We have tested the numerical and clinical capabilities of the proposed algorithm in several patient cases in comparison with KonRad inverse planning system. The comparative analysis shows that the algorithm can generate anatomy-based IMRT plans with about 50% reduction in number of MUs and 60% reduction in number of apertures, while producing dose distribution comparable to that of beamlet-based IMRT plans. Hence, it is clearly evident from the study that the proposed algorithm can be effectively used for clinical applications. (author)

  15. Performance evaluation of an algorithm for fast optimization of beam weights in anatomy-based intensity modulated radiotherapy

    Directory of Open Access Journals (Sweden)

    Ranganathan Vaitheeswaran

    2010-01-01

    Full Text Available This study aims to evaluate the performance of a new algorithm for optimization of beam weights in anatomy-based intensity modulated radiotherapy (IMRT. The algorithm uses a numerical technique called Gaussian-Elimination that derives the optimum beam weights in an exact or non-iterative way. The distinct feature of the algorithm is that it takes only fraction of a second to optimize the beam weights, irrespective of the complexity of the given case. The algorithm has been implemented using MATLAB with a Graphical User Interface (GUI option for convenient specification of dose constraints and penalties to different structures. We have tested the numerical and clinical capabilities of the proposed algorithm in several patient cases in comparison with KonRad® inverse planning system. The comparative analysis shows that the algorithm can generate anatomy-based IMRT plans with about 50% reduction in number of MUs and 60% reduction in number of apertures, while producing dose distribution comparable to that of beamlet-based IMRT plans. Hence, it is clearly evident from the study that the proposed algorithm can be effectively used for clinical applications.

  16. A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

    2016-03-01

    On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR

  17. A downstream algorithm based on extended gradient vector flow field for object segmentation.

    Science.gov (United States)

    Chuang, Cheng-Hung; Lie, Wen-Nung

    2004-10-01

    For object segmentation, traditional snake algorithms often require human interaction; region growing methods are considerably dependent on the selected homogeneity criterion and initial seeds; watershed algorithms, however, have the drawback of over segmentation. A new downstream algorithm based on a proposed extended gradient vector flow (E-GVF) field model is presented in this paper for multiobject segmentation. The proposed flow field, on one hand, diffuses and propagates gradients near object boundaries to provide an effective guiding force and, on the other hand, presents a higher resolution of direction than traditional GVF field. The downstream process starts with a set of seeds scored and selected by considering local gradient direction information around each pixel. This step is automatic and requires no human interaction, making our algorithm more suitable for practical applications. Experiments show that our algorithm is noise resistant and has the advantage of segmenting objects that are separated from the background, while ignoring the internal structures of them. We have tested the proposed algorithm with several realistic images (e.g., medical and complex background images) and gained good results.

  18. A novel white blood cells segmentation algorithm based on adaptive neutrosophic similarity score.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2018-12-01

    White blood cells (WBCs) play a crucial role in the diagnosis of many diseases according to their numbers or morphology. The recent digital pathology equipments investigate and analyze the blood smear images automatically. The previous automated segmentation algorithms worked on healthy and non-healthy WBCs separately. Also, such algorithms had employed certain color components which leak adaptively with different datasets. In this paper, a novel segmentation algorithm for WBCs in the blood smear images is proposed using multi-scale similarity measure based on the neutrosophic domain. We employ neutrosophic similarity score to measure the similarity between different color components of the blood smear image. Since we utilize different color components from different color spaces, we modify the neutrosphic score algorithm to be adaptive. Two different segmentation frameworks are proposed: one for the segmentation of nucleus, and the other for the cytoplasm of WBCs. Moreover, our proposed algorithm is applied to both healthy and non-healthy WBCs. in some cases, the single blood smear image gather between healthy and non-healthy WBCs which is considered in our proposed algorithm. Also, our segmentation algorithm is performed without any external morphological binary enhancement methods which may effect on the original shape of the WBC. Different public datasets with different resolutions were used in our experiments. We evaluate the system performance based on both qualitative and quantitative measurements. The quantitative results indicates high precision rates of the segmentation performance measurement A1 = 96.5% and A2 = 97.2% of the proposed method. The average segmentation performance results for different WBCs types reach to 97.6%. In this paper, a method based on adaptive neutrosphic sets similarity score is proposed in order to detect WBCs from a blood smear microscopic image and segment its components (nucleus and the cytoplasm). The proposed

  19. A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions

    Science.gov (United States)

    Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

    2016-01-01

    The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application. PMID:27924935

  20. Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2013-01-01

    Full Text Available A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

  1. Performance of an open-source heart sound segmentation algorithm on eight independent databases.

    Science.gov (United States)

    Liu, Chengyu; Springer, David; Clifford, Gari D

    2017-08-01

    Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected. The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for

  2. Nonlinear physical segmentation algorithm for determining the layer boundary from lidar signal.

    Science.gov (United States)

    Mao, Feiyue; Li, Jun; Li, Chen; Gong, Wei; Min, Qilong; Wang, Wei

    2015-11-30

    Layer boundary (base and top) detection is a basic problem in lidar data processing, the results of which are used as inputs of optical properties retrieval. However, traditional algorithms not only require manual intervention but also rely heavily on the signal-to-noise ratio. Therefore, we propose a robust and automatic algorithm for layer detection based on a novel algorithm for lidar signal segmentation and representation. Our algorithm is based on the lidar equation and avoids most of the limitations of the traditional algorithms. Testing of the simulated and real signals shows that the algorithm is able to position the base and top accurately even with a low signal to noise ratio. Furthermore, the results of the classification are accurate and satisfactory. The experimental results confirm that our algorithm can be used for automatic detection, retrieval, and analysis of lidar data sets.

  3. 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.

  4. An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes

    Science.gov (United States)

    2002-01-01

    encoded in in the standard GB-scheme. Franz Kafka’s The Castle in the original German comprised the final text. For comparison purposes we selected the...Orwell corpus, and 10% of the Kafka corpus, so it is not surprising that the algorithm performs worst on the Chinese corpus and best on the Kafka ...64 .34 .37 .53 .10 Chinese .57 .42 .07 .13 .57 .30 Table 2. Results of running Voting-Experts on Franz Kafka’s The Castle, Orwell’s 1984, a subset of

  5. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm

    DEFF Research Database (Denmark)

    Letteboer, Marloes M J; Olsen, Ole F; Dam, Erik B

    2004-01-01

    RATIONALE AND OBJECTIVE: This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures. MATERIALS AND METHODS: The watershed method is compared...... with manual delineation with respect to accuracy, repeatability, and efficiency. RESULTS: In the 20 patients included in this study, the measured volume of the tumors ranged from 2.7 to 81.9 cm(3). A comparison of the tumor volumes measured with watershed segmentation to the volumes measured with manual...

  6. Detection of constituent layers of histological oral sub-mucous fibrosis: images using the hybrid segmentation algorithm.

    Science.gov (United States)

    Ray, Tathagata; Reddy, D Shivashanker; Mukherjee, Anirban; Chatterjee, Jyotirmoy; Paul, Ranjan R; Dutta, Pranab K

    2008-12-01

    This paper concentrates on the segmentation of histological images of oral sub-mucous fibrosis (OSF) into its constituent layers. In this regard hybrid segmentation algorithm shows very interesting results. The segmentation results depict the superiority of hybrid segmentation algorithm (HSA) in comparison to region growing algorithm (RGA). In clinical sense, the presented method provides an automatic means for segmenting histological layers (reference class map provided by the expert). The method shows potential in mimicking clinical acumen to act as a support system to oncologist.

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

    Science.gov (United States)

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

    2012-07-07

    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.

  8. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

    Science.gov (United States)

    Hatipoglu, Nuh; Bilgin, Gokhan

    2017-10-01

    In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

  9. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  10. A fully automated algorithm of baseline correction based on wavelet feature points and segment interpolation

    Science.gov (United States)

    Qian, Fang; Wu, Yihui; Hao, Peng

    2017-11-01

    Baseline correction is a very important part of pre-processing. Baseline in the spectrum signal can induce uneven amplitude shifts across different wavenumbers and lead to bad results. Therefore, these amplitude shifts should be compensated before further analysis. Many algorithms are used to remove baseline, however fully automated baseline correction is convenient in practical application. A fully automated algorithm based on wavelet feature points and segment interpolation (AWFPSI) is proposed. This algorithm finds feature points through continuous wavelet transformation and estimates baseline through segment interpolation. AWFPSI is compared with three commonly introduced fully automated and semi-automated algorithms, using simulated spectrum signal, visible spectrum signal and Raman spectrum signal. The results show that AWFPSI gives better accuracy and has the advantage of easy use.

  11. Generalized rough fuzzy c-means algorithm for brain MR image segmentation.

    Science.gov (United States)

    Ji, Zexuan; Sun, Quansen; Xia, Yong; Chen, Qiang; Xia, Deshen; Feng, Dagan

    2012-11-01

    Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  12. A hybrid algorithm for instant optimization of beam weights in anatomy-based intensity modulated radiotherapy: a performance evaluation study

    International Nuclear Information System (INIS)

    Vaitheeswaran, Ranganathan; Sathiya Narayanan, V.K.; Bhangle, Janhavi R.; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram

    2011-01-01

    The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) (∼ 2% - 5% improvement) and Homogeneity Index (HI) (∼ 4% - 10% improvement) as compared to GEM and FSA algorithms (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are

  13. An evolutionary algorithm for the segmentation of muscles and bones of the lower limb.

    Science.gov (United States)

    Lpez, Marco A.; Braidot, A.; Sattler, Anbal; Schira, Claudia; Uriburu, E.

    2016-04-01

    In the field of medical image segmentation, muscles segmentation is a problem that has not been fully resolved yet. This is due to the fact that the basic assumption of image segmentation, which asserts that a visual distinction should ex- ist between the different structures to be identified, is infringed. As the tissue composition of two different muscles is the same, it becomes extremely difficult to distinguish one another if they are near. We have developed an evolutionary algorithm which selects the set and the sequence of morphological operators that better segments muscles and bones from an MRI image. The achieved results shows that the developed algorithm presents average sensitivity values close to 75% in the segmentation of the different processed muscles and bones. It also presents average specificity values close to 93% for the same structures. Furthermore, the algorithm can identify muscles that are closely located through the path from their origin point to their insertions, with very low error values (below 7%) .

  14. Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI.

    Science.gov (United States)

    Kurzendorfer, Tanja; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas; Brost, Alexander

    2017-07-01

    The current challenge for electrophysiology procedures, targeting the left ventricle, is the localization and qualification of myocardial scar. Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the current gold standard to visualize regions of myocardial infarction. Commonly, a stack of 2-D images is acquired of the left ventricle in short-axis orientation. Recently, 3-D LGE-MRI methods were proposed that continuously cover the whole heart with a high resolution within a single acquisition. The acquisition promises an accurate quantification of the myocardium to the extent of myocardial scarring. The major challenge arises in the analysis of the resulting images, as the accurate segmentation of the myocardium is a requirement for a precise scar tissue quantification. In this work, we propose a novel approach for fully automatic left ventricle segmentation in 3-D whole-heart LGE-MRI, to address this limitation. First, a two-step registration is performed to initialize the left ventricle. In the next step, the principal components are computed and a pseudo short axis view of the left ventricle is estimated. The refinement of the endocardium and epicardium is performed in polar space. Prior knowledge for shape and inter-slice smoothness is used during segmentation. The proposed method was evaluated on 30 clinical 3-D LGE-MRI datasets from individual patients obtained at two different clinical sites and were compared to gold standard segmentations of two clinical experts. This comparison resulted in a Dice coefficient of 0.83 for the endocardium and 0.80 for the epicardium. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. A combined learning algorithm for prostate segmentation on 3D CT images.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

    2017-11-01

    Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is

  16. 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.

  17. Research on Segmentation Monitoring Control of IA-RWA Algorithm with Probe Flow

    Science.gov (United States)

    Ren, Danping; Guo, Kun; Yao, Qiuyan; Zhao, Jijun

    2018-04-01

    The impairment-aware routing and wavelength assignment algorithm with probe flow (P-IA-RWA) can make an accurate estimation for the transmission quality of the link when the connection request comes. But it also causes some problems. The probe flow data introduced in the P-IA-RWA algorithm can result in the competition for wavelength resources. In order to reduce the competition and the blocking probability of the network, a new P-IA-RWA algorithm with segmentation monitoring-control mechanism (SMC-P-IA-RWA) is proposed. The algorithm would reduce the holding time of network resources for the probe flow. It segments the candidate path suitably for the data transmitting. And the transmission quality of the probe flow sent by the source node will be monitored in the endpoint of each segment. The transmission quality of data can also be monitored, so as to make the appropriate treatment to avoid the unnecessary probe flow. The simulation results show that the proposed SMC-P-IA-RWA algorithm can effectively reduce the blocking probability. It brings a better solution to the competition for resources between the probe flow and the main data to be transferred. And it is more suitable for scheduling control in the large-scale network.

  18. Hopfield-K-Means clustering algorithm: A proposal for the segmentation of electricity customers

    International Nuclear Information System (INIS)

    Lopez, Jose J.; Aguado, Jose A.; Martin, F.; Munoz, F.; Rodriguez, A.; Ruiz, Jose E.

    2011-01-01

    Customer classification aims at providing electric utilities with a volume of information to enable them to establish different types of tariffs. Several methods have been used to segment electricity customers, including, among others, the hierarchical clustering, Modified Follow the Leader and K-Means methods. These, however, entail problems with the pre-allocation of the number of clusters (Follow the Leader), randomness of the solution (K-Means) and improvement of the solution obtained (hierarchical algorithm). Another segmentation method used is Hopfield's autonomous recurrent neural network, although the solution obtained only guarantees that it is a local minimum. In this paper, we present the Hopfield-K-Means algorithm in order to overcome these limitations. This approach eliminates the randomness of the initial solution provided by K-Means based algorithms and it moves closer to the global optimun. The proposed algorithm is also compared against other customer segmentation and characterization techniques, on the basis of relative validation indexes. Finally, the results obtained by this algorithm with a set of 230 electricity customers (residential, industrial and administrative) are presented. (author)

  19. Research of fiber optical faceplate defects segmentation based on improved watershed algorithm

    Science.gov (United States)

    Yang, Bingqian; Wang, Mingquan; Zhang, Junsheng; Gao, Jinkai

    2017-08-01

    In this paper, an improved adaptive watershed segmentation method is proposed based on the characteristics of the optical fiber faceplate. Firstly, median filtering and morphological contrast enhancement are performed on the defect images, and then the gradient of the image is obtained by multi-scale morphology. In the improved watershed algorithm, the local minimum is first removed which the depth is lower than H. Then, the local minimum of the depth larger than H as the seed point are extended. Finally, the gradient image is modified by the forced minimum method. The modified gradient image is used to make the watershed Segmentation to get the final segmentation result .The experimental results show that the method can effectively suppress the over-segmentation, and the defects can be extracted well.

  20. 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...

  1. Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms

    Directory of Open Access Journals (Sweden)

    Didier Auroux

    2011-01-01

    Full Text Available We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides accurate and connected contours. We present then two numerical applications, to image inpainting and segmentation, of this hybrid algorithm.

  2. 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.

  3. A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

    Science.gov (United States)

    Long, Zourong; Wei, Biao; Feng, Peng; Yu, Pengwei; Liu, Yuanyuan

    2018-01-01

    This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation. Different from the classical convolutional neural networks (CNN), FCN uses convolution layers instead of the fully connected layers. So it can accept image of arbitrary size. In this paper, we combine the convolutional neural network and scale invariant feature matching to solve the problem of visual positioning under different scenarios. All high-resolution images are captured with our calibrated binocular imaging system and several groups of test data are collected to verify this method. The experimental results show that the binocular images are effectively segmented without over-segmentation. With these segmented images, feature matching via SURF method is implemented to obtain regional information for further image processing. The final positioning procedure shows that the results are acceptable in the range of 1.4 1.6 m, the distance error is less than 10mm.

  4. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics.

    Science.gov (United States)

    Moccia, Sara; De Momi, Elena; El Hadji, Sara; Mattos, Leonardo S

    2018-05-01

    Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic

  5. Lack of protein-tyrosine sulfation disrupts photoreceptor outer segment morphogenesis, retinal function and retinal anatomy.

    Science.gov (United States)

    Sherry, David M; Murray, Anne R; Kanan, Yogita; Arbogast, Kelsey L; Hamilton, Robert A; Fliesler, Steven J; Burns, Marie E; Moore, Kevin L; Al-Ubaidi, Muayyad R

    2010-11-01

    To investigate the role(s) of protein-tyrosine sulfation in the retina, we examined retinal function and structure in mice lacking tyrosylprotein sulfotransferases (TPST) 1 and 2. Tpst double knockout (DKO; Tpst1(-/-) /Tpst2 (-/-) ) retinas had drastically reduced electroretinographic responses, although their photoreceptors exhibited normal responses in single cell recordings. These retinas appeared normal histologically; however, the rod photoreceptors had ultrastructurally abnormal outer segments, with membrane evulsions into the extracellular space, irregular disc membrane spacing and expanded intradiscal space. Photoreceptor synaptic terminals were disorganized in Tpst DKO retinas, but established ultrastructurally normal synapses, as did bipolar and amacrine cells; however, the morphology and organization of neuronal processes in the inner retina were abnormal. These results indicate that protein-tyrosine sulfation is essential for proper outer segment morphogenesis and synaptic function, but is not critical for overall retinal structure or synapse formation, and may serve broader functions in neuronal development and maintenance. © 2010 The Authors. European Journal of Neuroscience © 2010 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  6. A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Barari, Amin

    2011-01-01

    Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...... for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over-segmentation...

  7. The Neutrosophic Set and Quantum-behaved Particle Swarm Optimization Algorithm of Side Scan Sonar Image Segmentation

    Directory of Open Access Journals (Sweden)

    ZHAO Jianhu

    2016-08-01

    Full Text Available Due to the problem of the existing image segmentation methods applied in side scan sonar (SSS image often suffered from low efficiency or low accuracy, this paper proposed a novel SSS image thresholding segmentation method based on neutrosophic set (NS and quantum-behaved particle swarm optimization (QPSO algorithm. Firstly, the image gray co-occurrence matrix is constructed in NS domain, the fine texture of SSS image is expressed, and this can improve the accuracy of SSS image segmentation. Then, based on the two-dimensional maximum entropy theory, the optimal two-dimensional segmentation threshold vector is quickly and accurately obtained by QPSO algorithm, and this can improve the efficiency and accuracy of SSS image segmentation. Finally, the accurate and high efficient target segmentation of SSS image with high noises is realized. The effectiveness of the algorithm is verified by segmenting SSS image containing different targets.

  8. 2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm

    Directory of Open Access Journals (Sweden)

    Zhiwei Ye

    2018-03-01

    Full Text Available Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA, particle swarm optimization (PSO, ant colony optimization algorithm (ACO and differential evolution algorithm (DE are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA. The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.

  9. A novel segmentation algorithm for volumetric analysis of macular hole boundaries identified with optical coherence tomography.

    Science.gov (United States)

    Xu, David; Yuan, Alex; Kaiser, Peter K; Srivastava, Sunil K; Singh, Rishi P; Sears, Jonathan E; Martin, Daniel F; Ehlers, Justis P

    2013-01-07

    To demonstrate a novel algorithm for macular hole (MH) segmentation and volumetric analysis. A computer algorithm was developed for automated MH segmentation in spectral-domain optical coherence tomography (SD-OCT). Algorithm validation was performed by trained graders with performance characterized by absolute accuracy and intraclass correlation coefficient. A retrospective case series of 56 eyes of 55 patients with idiopathic MHs analyzed using the custom algorithm to measure MH volume, base area/diameter, top area/diameter, minimum diameter, and height-to-base diameter ratio. Five eyes were excluded due to poor signal quality (1), motion artifact (1), and failure of surgical closure (3) for a final cohort of 51 eyes. Preoperative MH measurements were correlated with clinical MH stage, baseline, and 6-month postoperative best-corrected Snellen visual acuity (BCVA). The algorithm achieved 96% absolute accuracy and an intraclass correlation of 0.994 compared to trained graders. In univariate analysis, MH volume, base area, base diameter, top area, top diameter, minimum diameter, and MH height were significantly correlated to baseline BCVA (P value from 0.0003-0.011). Volume, base area, base diameter, and height-to-base diameter ratio were significantly correlated to 6-month postoperative BCVA (P value from volumetric analysis of MH geometry and correlates with baseline and postoperative visual function. Further research is needed to better understand the algorithm's role in prognostication and clinical management.

  10. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).

    Science.gov (United States)

    Feng, Xiang; Deistung, Andreas; Dwyer, Michael G; Hagemeier, Jesper; Polak, Paul; Lebenberg, Jessica; Frouin, Frédérique; Zivadinov, Robert; Reichenbach, Jürgen R; Schweser, Ferdinand

    2017-06-01

    Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T 1 -weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. SEGMENTATION OF HYPERSPECTRAL IMAGE USING JSEG BASED ON UNSUPERVISED CLUSTERING ALGORITHMS

    Directory of Open Access Journals (Sweden)

    V. Saravana Kumar

    2016-11-01

    Full Text Available Hyperspectral image analysis is a complicated and challenging task due to the inherent nature of the image. The main aim of this work is to segment the object in hyperspectral scene using image processing technique. This paper address a novel approach entitled as Segmentation of hyperspectral image using JSEG based on unsupervised cluster methods. In the preprocessing part, single band is picked out from the hyperspectral image and then converts into false color image. The JSEG algorithm is segregate the false color image properly without manual parameter adjustment. The segmentation has carried in two major stages. To begin with, colors in the image are quantized to represent several classes which can be used to differentiate regions in the image. Besides, hit rate regions with cognate color regions merging algorithm is used. In region merging part, K-means, Fuzzy C-Means (FCM and Fast K-Means weighted option (FWKM algorithm are used to segregate the image in accordance with the color for each cluster and its neighborhoods. Experiment results of above clustering method could be analyzed in terms of mean, standard deviation, number of cluster, number of pixels, time taken, number of objects occur in the resultant image. FWKM algorithm results yields good performance than its counterparts.

  12. Reliability and validity of an algorithm for fuzzy tissue segmentation of MRI.

    Science.gov (United States)

    Reiss, A L; Hennessey, J G; Rubin, M; Beach, L; Abrams, M T; Warsofsky, I S; Liu, A M; Links, J M

    1998-01-01

    A new multistep, volumetric-based tissue segmentation algorithm that results in fuzzy (or probabilistic) voxel description is described. This algorithm is designed to accurately segment gray matter, white matter, and CSF and can be applied to both single channel high resolution and multispectral (multiecho) MR images. The reliability and validity of this method are evaluated by assessing (a) the stability of the algorithm across time, rater, and pulse sequence; (b) the accuracy of the method when applied to both real and synthetic image datasets; and (c) differences in specific tissue volumes between individuals with a specific genetic condition (fragile X syndrome) and normal control subjects. The algorithm was found to have high reliability, accuracy, and validity. The finding of increased caudate gray matter volume associated with the fragile X syndrome is replicated in this sample. Since this segmentation approach incorporates "fuzzy" or probabilistic methods, it has the potential to more accurately address partial volume effects, anatomical variation within "pure" tissue compartments, and more subtle changes in tissue volumes as a result of disease and treatment. The method is a component of software that is available in the public domain and has been implemented on an inexpensive personal computer thus offering an attractive and promising method for determining the status and progression of both normal development and pathology of the CNS.

  13. Analyzing the medical image by using clustering algorithms through segmentation process

    Science.gov (United States)

    Kumar, Papendra; Kumar, Suresh

    2012-01-01

    Basic aim of our study is to analyze the medical image. In computer vision, segmentationRefers to the process of partitioning a digital image into multiple regions. The goal ofSegmentation is to simplify and/or change the representation of an image into something thatIs more meaningful and easier to analyze. Image segmentation is typically used to locateObjects and boundaries (lines, curves, etc.) in images.There is a lot of scope of the analysis that we have done in our project; our analysis couldBe used for the purpose of monitoring the medical image. Medical imaging refers to theTechniques and processes used to create images of the human body (or parts thereof) forClinical purposes (medical procedures seeking to reveal, diagnose or examine disease) orMedical science (including the study of normal anatomy and function).As a discipline and in its widest sense, it is part of biological imaging and incorporatesRadiology (in the wider sense), radiological sciences, endoscopy, (medical) thermography, Medical photography and microscopy (e.g. for human pathological investigations).Measurement and recording techniques which are not primarily designed to produce images.

  14. Balancing the data term of graph-cuts algorithm to improve segmentation of hepatic vascular structures.

    Science.gov (United States)

    Sangsefidi, Neda; Foruzan, Amir Hossein; Dolati, Ardeshir

    2018-02-01

    The accurate delineation of hepatic vessels is important to diagnosis and treatment planning. To improve the segmentation of these vessels and extract small structures, we adaptively calculate the data term in conventional graph-cuts algorithm. To assign higher costs to the data term in small vessel regions, we estimate the statistical parameters of the vessel adaptively. After preprocessing an input CT image, we model the liver and its vessels by two Gaussian distributions. The Maximum Intensity Projection (MIP) of the image is employed in the Expectation-Maximization algorithm to estimate the parameters of the model. These parameters are used together with a medial-axes enhancement algorithm to find the axes of the vessels. The skeleton of these vessels is considered to be the image voxels that are most similar to the hepatic vascular structures. To calculate the cost function of the graph-cuts algorithm, those axes that are nearby are employed to estimate the vessel parameters. The conventional minimum-cut/maximum-flow energy minimization framework finds the global minimum of the cost function and labels vessel voxels. We evaluated our method using synthetic data and clinical images. We compared our algorithm with state-of-the-art vessel segmentation methods. The mean Dice measure of our results was 95.51% (0.9% lower than the first rank method). Quantitatively, our method segmented small hepatic vessels that were not extracted by traditional techniques including conventional graph-cuts. The proposed method improved the segmentation of small vessels in the presence of noise. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Application of Micro-segmentation Algorithms to the Healthcare Market:A Case Study

    Energy Technology Data Exchange (ETDEWEB)

    Sukumar, Sreenivas R [ORNL; Aline, Frank [ORNL

    2013-01-01

    We draw inspiration from the recent success of loyalty programs and targeted personalized market campaigns of retail companies such as Kroger, Netflix, etc. to understand beneficiary behaviors in the healthcare system. Our posit is that we can emulate the financial success the companies have achieved by better understanding and predicting customer behaviors and translating such success to healthcare operations. Towards that goal, we survey current practices in market micro-segmentation research and analyze health insurance claims data using those algorithms. We present results and insights from micro-segmentation of the beneficiaries using different techniques and discuss how the interpretation can assist with matching the cost-effective insurance payment models to the beneficiary micro-segments.

  16. Co-phasing experiment of a segmented mirror using a combined broadband and two-wavelength algorithm.

    Science.gov (United States)

    Li, Bin; Yu, Wen-Hao; Chen, Mo; Tang, Jin-Long; Xian, Hao

    2017-11-10

    In this paper, a broadband phasing algorithm is combined with a two-wavelength phasing algorithm to detect the piston error of a segmented mirror with the advantages of long range, high precision, and fast detection. Moreover, an active optics co-phasing experimental system of the segmented mirror is built to verify the algorithm's effectiveness. The segmented mirror consists of four hexagonal segments, with flat-to-flat lengths of 100 mm and radii of curvature of 2000 mm. First, a Shack-Hartmann sensor and piezoelectric actuators are used to finely co-focus the segmented mirror. Then, the broadband phasing algorithm is used to reduce the piston error of the segmented mirror to several micrometers. Finally, the two-wavelength phasing algorithm is used to reduce the piston error of the segmented mirror to zero. The experimental results show that the measurement accuracy is better than 26 nm, and the adjustment accuracy is approximately 55 nm, which demonstrates that the combined algorithm is valuable for segmented-mirror co-phasing measurement and adjustment.

  17. Simulating Deformations of MR Brain Images for Validation of Atlas-based Segmentation and Registration Algorithms

    OpenAIRE

    Xue, Zhong; Shen, Dinggang; Karacali, Bilge; Stern, Joshua; Rottenberg, David; Davatzikos, Christos

    2006-01-01

    Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and in...

  18. 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.

  19. An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues

    Science.gov (United States)

    Ribeiro, Angela; Ranz, Juan; Burgos-Artizzu, Xavier P.; Pajares, Gonzalo; Sanchez del Arco, Maria J.; Navarrete, Luis

    2011-01-01

    Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest. The RGB images used come from a sample of images in which sections of terrain were photographed with a conventional camera positioned in zenith orientation atop a tripod. The images were taken outdoors under uncontrolled lighting conditions. Up to 92% similarity was achieved between the images obtained by the segmentation process proposed in this paper and the templates made by an elaborate manual tracing process. In addition to the proposed segmentation procedure and the fine tuning procedure that was developed, a global quantification of the soil coverage by residues for the sampled area was achieved that differed by only 0.85% from the quantification obtained using template images. Moreover, the proposed method does not depend on the type of residue present in the image. The study was conducted at the experimental farm “El Encín” in Alcalá de Henares (Madrid, Spain). PMID:22163966

  20. Structure and development of the subesophageal zone of the Drosophila brain. I. Segmental architecture, compartmentalization, and lineage anatomy.

    Science.gov (United States)

    Hartenstein, Volker; Omoto, Jaison J; Ngo, Kathy T; Wong, Darren; Kuert, Philipp A; Reichert, Heinrich; Lovick, Jennifer K; Younossi-Hartenstein, Amelia

    2018-01-01

    The subesophageal zone (SEZ) of the Drosophila brain houses the circuitry underlying feeding behavior and is involved in many other aspects of sensory processing and locomotor control. Formed by the merging of four neuromeres, the internal architecture of the SEZ can be best understood by identifying segmentally reiterated landmarks emerging in the embryo and larva, and following the gradual changes by which these landmarks become integrated into the mature SEZ during metamorphosis. In previous works, the system of longitudinal fibers (connectives) and transverse axons (commissures) has been used as a scaffold that provides internal landmarks for the neuromeres of the larval ventral nerve cord. We have extended the analysis of this scaffold to the SEZ and, in addition, reconstructed the tracts formed by lineages and nerves in relationship to the connectives and commissures. As a result, we establish reliable criteria that define boundaries between the four neuromeres (tritocerebrum, mandibular neuromere, maxillary neuromere, labial neuromere) of the SEZ at all stages of development. Fascicles and lineage tracts also demarcate seven columnar neuropil domains (ventromedial, ventro-lateral, centromedial, central, centrolateral, dorsomedial, dorsolateral) identifiable throughout development. These anatomical subdivisions, presented in the form of an atlas including confocal sections and 3D digital models for the larval, pupal and adult stage, allowed us to describe the morphogenetic changes shaping the adult SEZ. Finally, we mapped MARCM-labeled clones of all secondary lineages of the SEZ to the newly established neuropil subdivisions. Our work will facilitate future studies of function and comparative anatomy of the SEZ. © 2017 Wiley Periodicals, Inc.

  1. A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy

    International Nuclear Information System (INIS)

    Lahanas, M; Baltas, D; Zamboglou, N

    2003-01-01

    Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives

  2. Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm.

    Science.gov (United States)

    Saberkari, Hamidreza; Bahrami, Sheyda; Shamsi, Mousa; Amoshahy, Mohammad Javad; Ghavifekr, Habib Badri; Sedaaghi, Mohammad Hossein

    2015-01-01

    DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component analysis and the spatial fuzzy c-means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

  3. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    Directory of Open Access Journals (Sweden)

    Yehu Shen

    2014-01-01

    Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

  4. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    Science.gov (United States)

    Peng, Zhenyun; Zhang, Yaohui

    2014-01-01

    Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying. PMID:24592182

  5. Segmentation algorithm of colon based on multi-slice CT colonography

    Science.gov (United States)

    Hu, Yizhong; Ahamed, Mohammed Shabbir; Takahashi, Eiji; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Suzuki, Masahiro; Iinuma, Gen; Moriyama, Noriyuki

    2012-02-01

    CT colonography is a radiology test that looks at people's large intestines(colon). CT colonography can screen many options of colon cancer. This test is used to detect polyps or cancers of the colon. CT colonography is safe and reliable. It can be used if people are too sick to undergo other forms of colon cancer screening. In our research, we proposed a method for automatic segmentation of the colon from abdominal computed Tomography (CT) images. Our multistage detection method extracted colon and spited colon into different parts according to the colon anatomy information. We found that among the five segmented parts of the colon, sigmoid (20%) and rectum (50%) are more sensitive toward polyps and masses than the other three parts. Our research focused on detecting the colon by the individual diagnosis of sigmoid and rectum. We think it would make the rapid and easy diagnosis of colon in its earlier stage and help doctors for analysis of correct position of each part and detect the colon rectal cancer much easier.

  6. Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT

    Energy Technology Data Exchange (ETDEWEB)

    Bae, JangPyo [Interdisciplinary Program, Bioengineering Major, Graduate School, Seoul National University, Seoul 110-744, South Korea and Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Kim, Namkug, E-mail: namkugkim@gmail.com; Lee, Sang Min; Seo, Joon Beom [Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Kim, Hee Chan [Department of Biomedical Engineering, College of Medicine and Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 110-744 (Korea, Republic of)

    2014-04-15

    Purpose: To develop and validate a semiautomatic segmentation method for thoracic cavity volumetry and mediastinum fat quantification of patients with chronic obstructive pulmonary disease. Methods: The thoracic cavity region was separated by segmenting multiorgans, namely, the rib, lung, heart, and diaphragm. To encompass various lung disease-induced variations, the inner thoracic wall and diaphragm were modeled by using a three-dimensional surface-fitting method. To improve the accuracy of the diaphragm surface model, the heart and its surrounding tissue were segmented by a two-stage level set method using a shape prior. To assess the accuracy of the proposed algorithm, the algorithm results of 50 patients were compared to the manual segmentation results of two experts with more than 5 years of experience (these manual results were confirmed by an expert thoracic radiologist). The proposed method was also compared to three state-of-the-art segmentation methods. The metrics used to evaluate segmentation accuracy were volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), false negative ratio on VOR (FNRV), average symmetric absolute surface distance (ASASD), average symmetric squared surface distance (ASSSD), and maximum symmetric surface distance (MSSD). Results: In terms of thoracic cavity volumetry, the mean ± SD VOR, FPRV, and FNRV of the proposed method were (98.17 ± 0.84)%, (0.49 ± 0.23)%, and (1.34 ± 0.83)%, respectively. The ASASD, ASSSD, and MSSD for the thoracic wall were 0.28 ± 0.12, 1.28 ± 0.53, and 23.91 ± 7.64 mm, respectively. The ASASD, ASSSD, and MSSD for the diaphragm surface were 1.73 ± 0.91, 3.92 ± 1.68, and 27.80 ± 10.63 mm, respectively. The proposed method performed significantly better than the other three methods in terms of VOR, ASASD, and ASSSD. Conclusions: The proposed semiautomatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart

  7. a Fast Segmentation Algorithm for C-V Model Based on Exponential Image Sequence Generation

    Science.gov (United States)

    Hu, J.; Lu, L.; Xu, J.; Zhang, J.

    2017-09-01

    For the island coastline segmentation, a fast segmentation algorithm for C-V model method based on exponential image sequence generation is proposed in this paper. The exponential multi-scale C-V model with level set inheritance and boundary inheritance is developed. The main research contributions are as follows: 1) the problems of the "holes" and "gaps" are solved when extraction coastline through the small scale shrinkage, low-pass filtering and area sorting of region. 2) the initial value of SDF (Signal Distance Function) and the level set are given by Otsu segmentation based on the difference of reflection SAR on land and sea, which are finely close to the coastline. 3) the computational complexity of continuous transition are successfully reduced between the different scales by the SDF and of level set inheritance. Experiment results show that the method accelerates the acquisition of initial level set formation, shortens the time of the extraction of coastline, at the same time, removes the non-coastline body part and improves the identification precision of the main body coastline, which automates the process of coastline segmentation.

  8. A FAST SEGMENTATION ALGORITHM FOR C-V MODEL BASED ON EXPONENTIAL IMAGE SEQUENCE GENERATION

    Directory of Open Access Journals (Sweden)

    J. Hu

    2017-09-01

    Full Text Available For the island coastline segmentation, a fast segmentation algorithm for C-V model method based on exponential image sequence generation is proposed in this paper. The exponential multi-scale C-V model with level set inheritance and boundary inheritance is developed. The main research contributions are as follows: 1 the problems of the "holes" and "gaps" are solved when extraction coastline through the small scale shrinkage, low-pass filtering and area sorting of region. 2 the initial value of SDF (Signal Distance Function and the level set are given by Otsu segmentation based on the difference of reflection SAR on land and sea, which are finely close to the coastline. 3 the computational complexity of continuous transition are successfully reduced between the different scales by the SDF and of level set inheritance. Experiment results show that the method accelerates the acquisition of initial level set formation, shortens the time of the extraction of coastline, at the same time, removes the non-coastline body part and improves the identification precision of the main body coastline, which automates the process of coastline segmentation.

  9. Line Segmentation in Handwritten Assamese and Meetei Mayek Script Using Seam Carving Based Algorithm

    Science.gov (United States)

    Kumar, Chandan Jyoti; Kalita, Sanjib Kr.

    Line segmentation is a key stage in an Optical Character Recognition system. This paper primarily concerns the problem of text line extraction on color and grayscale manuscript pages of two major North-east Indian regional Scripts, Assamese and Meetei Mayek. Line segmentation of handwritten text in Assamese and Meetei Mayek scripts is an uphill task primarily because of the structural features of both the scripts and varied writing styles. Line segmentation of a document image is been achieved by using the Seam carving technique, in this paper. Researchers from various regions used this approach for content aware resizing of an image. However currently many researchers are implementing Seam Carving for line segmentation phase of OCR. Although it is a language independent technique, mostly experiments are done over Arabic, Greek, German and Chinese scripts. Two types of seams are generated, medial seams approximate the orientation of each text line, and separating seams separated one line of text from another. Experiments are performed extensively over various types of documents and detailed analysis of the evaluations reflects that the algorithm performs well for even documents with multiple scripts. In this paper, we present a comparative study of accuracy of this method over different types of data.

  10. A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Barari, Amin

    2011-01-01

    for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over...

  11. DIGITAL TERRAIN FROM A TWO-STEP SEGMENTATION AND OUTLIER-BASED ALGORITHM

    Directory of Open Access Journals (Sweden)

    K. Hingee

    2016-06-01

    Full Text Available We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs for a 9600km2 region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km. The DSMs are captured annually at 0.2m resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho’s Match-T algorithm.

  12. Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers.

    Science.gov (United States)

    Milankovic, Ivan L; Mijailovic, Nikola V; Filipovic, Nenad D; Peulic, Aleksandar S

    2017-01-01

    Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

  13. New second-order difference algorithm for image segmentation based on cellular neural networks (CNNs)

    Science.gov (United States)

    Meng, Shukai; Mo, Yu L.

    2001-09-01

    Image segmentation is one of the most important operations in many image analysis problems, which is the process that subdivides an image into its constituents and extracts those parts of interest. In this paper, we present a new second order difference gray-scale image segmentation algorithm based on cellular neural networks. A 3x3 CNN cloning template is applied, which can make smooth processing and has a good ability to deal with the conflict between the capability of noise resistance and the edge detection of complex shapes. We use second order difference operator to calculate the coefficients of the control template, which are not constant but rather depend on the input gray-scale values. It is similar to Contour Extraction CNN in construction, but there are some different in algorithm. The result of experiment shows that the second order difference CNN has a good capability in edge detection. It is better than Contour Extraction CNN in detail detection and more effective than the Laplacian of Gauss (LOG) algorithm.

  14. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images.

    Science.gov (United States)

    Arslan, Salim; Ozyurek, Emel; Gunduz-Demir, Cigdem

    2014-06-01

    Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. © 2014 International Society for Advancement of Cytometry.

  15. Theoretically exact backprojection filtration algorithm for multi-segment linear trajectory

    Science.gov (United States)

    Wu, Weiwen; Yu, Hengyong; Cong, Wenxiang; Liu, Fenglin

    2018-01-01

    A theoretically exact backprojection filtration algorithm is proved and implemented for image reconstruction from a multi-segment linear trajectory assuming fan-beam geometry. The reconstruction formula is based on a concept of linear PI-line (L-PI) proposed in our previous work. The proof is completed in two consecutive steps. In the first step, it is proved that theoretically exact image reconstruction can be obtained on an arbitrary L-PI line from an infinite straight-line trajectory. In the second step, it is shown that accurate image reconstruction can be achieved from a multi-segment line trajectory by introducing a weight function to deal with the data redundancy. Numerical implementation and simulation results validate the correctness of our theoretical results.

  16. Outdoor Illegal Construction Identification Algorithm Based on 3D Point Cloud Segmentation

    Science.gov (United States)

    An, Lu; Guo, Baolong

    2018-03-01

    Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).

  17. Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

    Directory of Open Access Journals (Sweden)

    Vecchio Pietro

    2011-01-01

    Full Text Available Abstract The Bio-inspired (Bi-i Cellular Vision System is a computing platform consisting of sensing, array sensing-processing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy.

  18. Optimized adaptation algorithm for HEVC/H.265 dynamic adaptive streaming over HTTP using variable segment duration

    Science.gov (United States)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

    Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next

  19. 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.

  20. An Algorithm for Obtaining the Distribution of 1-Meter Lightning Channel Segment Altitudes for Application in Lightning NOx Production Estimation

    Science.gov (United States)

    Peterson, Harold; Koshak, William J.

    2009-01-01

    An algorithm has been developed to estimate the altitude distribution of one-meter lightning channel segments. The algorithm is required as part of a broader objective that involves improving the lightning NOx emission inventories of both regional air quality and global chemistry/climate models. The algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand; VHF source amplitude thresholding and smoothing were applied to optimize results. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. One-meter channel segment altitude distributions were also obtained for the different seasons.

  1. The Cascaded Enhanced k-Means and Fuzzy c-Means Clustering Algorithms for Automated Segmentation of Malaria Parasites

    Directory of Open Access Journals (Sweden)

    Abdul Nasir Aimi Salihah

    2018-01-01

    Full Text Available Malaria continues to be one of the leading causes of death in the world, despite the massive efforts put forth by World Health Organization (WHO in eradicating it, worldwide. Efficient control and proper treatment of this disease requires early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes a malaria parasite segmentation approach via cascaded clustering algorithms to automate the malaria diagnosis process. The comparisons among the cascaded clustering algorithms have been made by considering the accuracy, sensitivity and specificity of the segmented malaria images. Based on the qualitative and quantitative findings, the results show that by using the final centres that have been generated by enhanced k-means (EKM clustering as the initial centres for fuzzy c-means (FCM clustering, has led to the production of good segmented malaria image. The proposed cascaded EKM and FCM clustering has successfully segmented 100 malaria images of Plasmodium Vivax species with average segmentation accuracy, sensitivity and specificity values of 99.22%, 88.84% and 99.56%, respectively. Therefore, the EKM algorithm has given the best performance compared to k-means (KM and moving k-means (MKM algorithms when all the three clustering algorithms are cascaded with FCM algorithm.

  2. Local Pixel Value Collection Algorithm for Spot Segmentation in Two-Dimensional Gel Electrophoresis Research

    Directory of Open Access Journals (Sweden)

    Peter Peer

    2007-09-01

    Full Text Available Two-dimensional gel-electrophoresis (2-DE images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis database. A comparison of results is done with a method called pixel value collection (PVC. Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC. The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC.

  3. Usage of Clustering Algorithm to Segment Image into Simply Connected Domains

    Directory of Open Access Journals (Sweden)

    S. V. Belim

    2015-01-01

    Full Text Available The article suggests a method of image segmentation into simply connected domains based on color. Pixels from an original image are represented as points in five-dimensional space which includes three color and two spatial coordinates. The points are normalized in order to eliminate distinguished characteristics. The set of points is compared with a weighted complete graph. The points of five-dimensional space are vertexes in the graph. Euclidian distance between the points is used as weights of the edges in the graph. To solve the task of clustering, a minimum spanning tree of the graph is built. For clustering, the tree is separated into sub-trees by removing some edges. Each sub-tree is a simply connected domain on the original image. In order to improve algorithm speed and reduce memory usage a greedy algorithm is used to build this minimum spanning tree for the graph. Edges to be removed are searched on the graph representing the length of an added edge versus a sequence number of its adding to the tree in the greedy algorithm. The desired edges are detected as maximums on the graphic. This search is based on assumption that transition to an adjacent cluster leads to connection of longer edge in comparison with edges within a cluster. Segmentation into clusters is iterative. At each step the bigger clusters are divided into smaller ones. It means that hierarchy of clusters can be built. A computer experiment was carried out using different images.The suggested method has no disadvantages of the most common method of k-means and allows dividing domains with different colors but the same intensity. Therewith there is no need to specify a number of clusters. Instead, it is necessary to choose a segmentation depth then a number of clusters will be automatically defined. The suggested method has no disadvantages of detection of image edges either. It is sufficient to find one point of image edge to separate two domains.A distinctive feature of

  4. Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm.

    Science.gov (United States)

    Kim, Jung Jin; Nam, Jimin; Jang, In Gwun

    2018-02-01

    Automated segmentation with high accuracy and speed is a prerequisite for FEA-based quantitative assessment with a large population. However, hip joint segmentation has remained challenging due to a narrow articular cartilage and thin cortical bone with a marked interindividual variance. To overcome this challenge, this paper proposes a fully automated segmentation method for a hip joint that uses the complementary characteristics between the thresholding technique and the watershed algorithm. Using the golden section method and load path algorithm, the proposed method first determines the patient-specific optimal threshold value that enables reliably separating a femur from a pelvis while removing cortical and trabecular bone in the femur at the minimum. This provides regional information on the femur. The watershed algorithm is then used to obtain boundary information on the femur. The proximal femur can be extracted by merging the complementary information on a target image. For eight CT images, compared with the manual segmentation and other segmentation methods, the proposed method offers a high accuracy in terms of the dice overlap coefficient (97.24 ± 0.44%) and average surface distance (0.36 ± 0.07 mm) within a fast timeframe in terms of processing time per slice (1.25 ± 0.27 s). The proposed method also delivers structural behavior which is close to that of the manual segmentation with a small mean of average relative errors of the risk factor (4.99%). The segmentation results show that, without the aid of a prerequisite dataset and users' manual intervention, the proposed method can segment a hip joint as fast as the simplified Kang (SK)-based automated segmentation, while maintaining the segmentation accuracy at a similar level of the snake-based semi-automated segmentation. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Multiobjective anatomy-based dose optimization for HDR-brachytherapy with constraint free deterministic algorithms

    International Nuclear Information System (INIS)

    Milickovic, N.; Lahanas, M.; Papagiannopoulou, M.; Zamboglou, N.; Baltas, D.

    2002-01-01

    In high dose rate (HDR) brachytherapy, conventional dose optimization algorithms consider multiple objectives in the form of an aggregate function that transforms the multiobjective problem into a single-objective problem. As a result, there is a loss of information on the available alternative possible solutions. This method assumes that the treatment planner exactly understands the correlation between competing objectives and knows the physical constraints. This knowledge is provided by the Pareto trade-off set obtained by single-objective optimization algorithms with a repeated optimization with different importance vectors. A mapping technique avoids non-feasible solutions with negative dwell weights and allows the use of constraint free gradient-based deterministic algorithms. We compare various such algorithms and methods which could improve their performance. This finally allows us to generate a large number of solutions in a few minutes. We use objectives expressed in terms of dose variances obtained from a few hundred sampling points in the planning target volume (PTV) and in organs at risk (OAR). We compare two- to four-dimensional Pareto fronts obtained with the deterministic algorithms and with a fast-simulated annealing algorithm. For PTV-based objectives, due to the convex objective functions, the obtained solutions are global optimal. If OARs are included, then the solutions found are also global optimal, although local minima may be present as suggested. (author)

  6. Practical Constraint K-Segment Principal Curve Algorithms for Generating Railway GPS Digital Map

    Directory of Open Access Journals (Sweden)

    Dewang Chen

    2013-01-01

    Full Text Available In order to obtain a decent trade-off between the low-cost, low-accuracy Global Positioning System (GPS receivers and the requirements of high-precision digital maps for modern railways, using the concept of constraint K-segment principal curves (CKPCS and the expert knowledge on railways, we propose three practical CKPCS generation algorithms with reduced computational complexity, and thereafter more suitable for engineering applications. The three algorithms are named ALLopt, MPMopt, and DCopt, in which ALLopt exploits global optimization and MPMopt and DCopt apply local optimization with different initial solutions. We compare the three practical algorithms according to their performance on average projection error, stability, and the fitness for simple and complex simulated trajectories with noise data. It is found that ALLopt only works well for simple curves and small data sets. The other two algorithms can work better for complex curves and large data sets. Moreover, MPMopt runs faster than DCopt, but DCopt can work better for some curves with cross points. The three algorithms are also applied in generating GPS digital maps for two railway GPS data sets measured in Qinghai-Tibet Railway (QTR. Similar results like the ones in synthetic data are obtained. Because the trajectory of a railway is relatively simple and straight, we conclude that MPMopt works best according to the comprehensive considerations on the speed of computation and the quality of generated CKPCS. MPMopt can be used to obtain some key points to represent a large amount of GPS data. Hence, it can greatly reduce the data storage requirements and increase the positioning speed for real-time digital map applications.

  7. Automatic segmentation of thermal images of diabetic-at-risk feet using the snakes algorithm

    Science.gov (United States)

    Etehadtavakol, Mahnaz; Ng, E. Y. K.; Kaabouch, Naima

    2017-11-01

    Diabetes is a disease with multi-systemic problems. It is a leading cause of death, medical costs, and loss of productivity. Foot ulcers are one generally known problem of uncontrolled diabetes that can lead to amputation signs of foot ulcers are not always obvious. Sometimes, symptoms won't even show up until ulcer is infected. Hence, identification of pre-ulceration of the plantar surface of the foot in diabetics is beneficial. Thermography has the potential to identify regions of the plantar with no evidence of ulcer but yet risk. Thermography is a technique that is safe, easy, non-invasive, with no contact, and repeatable. In this study, 59 thermographic images of the plantar foot of patients with diabetic neuropathy are implemented using the snakes algorithm to separate two feet from background automatically and separating the right foot from the left on each image. The snakes algorithm both separates the right and left foot into segmented different clusters according to their temperatures. The hottest regions will have the highest risk of ulceration for each foot. This algorithm also worked perfectly for all the current images.

  8. A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering.

    Science.gov (United States)

    Ma, Li; Li, Yang; Fan, Suohai; Fan, Runzhu

    2015-01-01

    Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

  9. A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans.

    Science.gov (United States)

    Girish, G N; Anima, V A; Kothari, Abhishek R; Sudeep, P V; Roychowdhury, Sohini; Rajan, Jeny

    2018-01-01

    Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes. In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes. This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Hepatic Arterial Configuration in Relation to the Segmental Anatomy of the Liver; Observations on MDCT and DSA Relevant to Radioembolization Treatment

    Energy Technology Data Exchange (ETDEWEB)

    Hoven, Andor F. van den, E-mail: a.f.vandenhoven@umcutrecht.nl; Leeuwen, Maarten S. van, E-mail: m.s.vanleeuwen@umcutrecht.nl; Lam, Marnix G. E. H., E-mail: m.lam@umcutrecht.nl; Bosch, Maurice A. A. J. van den, E-mail: mbosch@umcutrecht.nl [University Medical Center Utrecht, Department of Radiology and Nuclear Medicine (Netherlands)

    2015-02-15

    PurposeCurrent anatomical classifications do not include all variants relevant for radioembolization (RE). The purpose of this study was to assess the individual hepatic arterial configuration and segmental vascularization pattern and to develop an individualized RE treatment strategy based on an extended classification.MethodsThe hepatic vascular anatomy was assessed on MDCT and DSA in patients who received a workup for RE between February 2009 and November 2012. Reconstructed MDCT studies were assessed to determine the hepatic arterial configuration (origin of every hepatic arterial branch, branching pattern and anatomical course) and the hepatic segmental vascularization territory of all branches. Aberrant hepatic arteries were defined as hepatic arterial branches that did not originate from the celiac axis/CHA/PHA. Early branching patterns were defined as hepatic arterial branches originating from the celiac axis/CHA.ResultsThe hepatic arterial configuration and segmental vascularization pattern could be assessed in 110 of 133 patients. In 59 patients (54 %), no aberrant hepatic arteries or early branching was observed. Fourteen patients without aberrant hepatic arteries (13 %) had an early branching pattern. In the 37 patients (34 %) with aberrant hepatic arteries, five also had an early branching pattern. Sixteen different hepatic arterial segmental vascularization patterns were identified and described, differing by the presence of aberrant hepatic arteries, their respective vascular territory, and origin of the artery vascularizing segment four.ConclusionsThe hepatic arterial configuration and segmental vascularization pattern show marked individual variability beyond well-known classifications of anatomical variants. We developed an individualized RE treatment strategy based on an extended anatomical classification.

  11. SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

    Energy Technology Data Exchange (ETDEWEB)

    Polan, D [University of Michigan, Ann Arbor, MI (United States); Brady, S; Kaufman, R [St. Jude Children’s Research Hospital, Memphis, TN (United States)

    2016-06-15

    Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT

  12. A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

    In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...... segmentation could be avoided. Indeed, the bilateral filtering, as a preprocessing step, eliminates the unnecessary details of the image and results in a few numbers of pixons, faster performance and more robustness against unwanted environmental noises. Then, the obtained pixonal image is segmented using...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques....

  13. Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

    Directory of Open Access Journals (Sweden)

    Edgar Chavolla

    2018-01-01

    Full Text Available Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

  14. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    Directory of Open Access Journals (Sweden)

    Margarita Gamarra

    2017-08-01

    Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

  15. Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms.

    Science.gov (United States)

    Xue, Zhong; Shen, Dinggang; Karacali, Bilge; Stern, Joshua; Rottenberg, David; Davatzikos, Christos

    2006-11-15

    Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.

  16. Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Chengquan Zhou

    2018-02-01

    Full Text Available To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram–Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT, and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.

  17. Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening.

    Science.gov (United States)

    Panda, Rashmi; Puhan, N B; Panda, Ganapati

    2018-02-01

    Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

  18. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

    Directory of Open Access Journals (Sweden)

    Elizabeth M Sweeney

    Full Text Available 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.

  19. 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

  20. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

    Science.gov (United States)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-07

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  1. SU-E-J-142: Performance Study of Automatic Image-Segmentation Algorithms in Motion Tracking Via MR-IGRT

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Y; Olsen, J.; Parikh, P.; Noel, C; Wooten, H; Du, D; Mutic, S; Hu, Y [Washington University, St. Louis, MO (United States); Kawrakow, I; Dempsey, J [Washington University, St. Louis, MO (United States); ViewRay Co., Oakwood Village, OH (United States)

    2014-06-01

    Purpose: Evaluate commonly used segmentation algorithms on a commercially available real-time MR image guided radiotherapy (MR-IGRT) system (ViewRay), compare the strengths and weaknesses of each method, with the purpose of improving motion tracking for more accurate radiotherapy. Methods: MR motion images of bladder, kidney, duodenum, and liver tumor were acquired for three patients using a commercial on-board MR imaging system and an imaging protocol used during MR-IGRT. A series of 40 frames were selected for each case to cover at least 3 respiratory cycles. Thresholding, Canny edge detection, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE), along with the ViewRay treatment planning and delivery system (TPDS) were included in the comparisons. To evaluate the segmentation results, an expert manual contouring of the organs or tumor from a physician was used as a ground-truth. Metrics value of sensitivity, specificity, Jaccard similarity, and Dice coefficient were computed for comparison. Results: In the segmentation of single image frame, all methods successfully segmented the bladder and kidney, but only FKM, KHM and TPDS were able to segment the liver tumor and the duodenum. For segmenting motion image series, the TPDS method had the highest sensitivity, Jarccard, and Dice coefficients in segmenting bladder and kidney, while FKM and KHM had a slightly higher specificity. A similar pattern was observed when segmenting the liver tumor and the duodenum. The Canny method is not suitable for consistently segmenting motion frames in an automated process, while thresholding and RD-LSE cannot consistently segment a liver tumor and the duodenum. Conclusion: The study compared six different segmentation methods and showed the effectiveness of the ViewRay TPDS algorithm in segmenting motion images during MR-IGRT. Future studies include a selection of conformal segmentation methods based on image/organ-specific information

  2. Impact of Reconstruction Algorithms and Gender-Associated Anatomy on Coronary Calcium Scoring with CT: An Anthropomorphic Phantom Study.

    Science.gov (United States)

    Li, Qin; Liu, Songtao; Myers, Kyle J; Gavrielides, Marios A; Zeng, Rongping; Sahiner, Berkman; Petrick, Nicholas

    2016-12-01

    Different computed tomography imaging protocols and patient characteristics can impact the accuracy and precision of the calcium score and may lead to inconsistent patient treatment recommendations. The aim of this work was to determine the impact of reconstruction algorithm and gender characteristics on coronary artery calcium scoring based on a phantom study using computed tomography. Four synthetic heart vessels with vessel diameters corresponding to female and male left main and left circumflex arteries containing calcification-mimicking materials (200-1000 HU) were inserted into a thorax phantom and were scanned with and without female breast plates (male and female phantoms, respectively). Ten scans were acquired and were reconstructed at 3-mm slices using filtered-back projection (FBP) and iterative reconstruction with medium and strong denoising (IR3 and IR5) algorithms. Agatston and calcium volume scores were estimated for each vessel. Calcium scores for each vessel and the total calcium score (summation of all four vessels) were compared between the two phantoms to quantify the impact of the breast plates and reconstruction parameters. Calcium scores were also compared among vessels of different diameters to investigate the impact of the vessel size. The calcium scores were significantly larger for FBP reconstruction (FBP > IR3>IR5). Agatston scores (calcium volume score) for vessels in the male phantom scans were on average 4.8% (2.9%), 8.2% (7.1%), and 10.5% (9.4%) higher compared to those in the female phantom with FBP, IR3, and IR5, respectively, when exposure was conserved across phantoms. The total calcium scores from the male phantom were significantly larger than those from the female phantom (P phantom. Calcium scores significantly decreased with iterative reconstruction and tended to be underestimated for female anatomy (smaller vessels and presence of breast plates). Published by Elsevier Inc.

  3. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study

    NARCIS (Netherlands)

    Rudyanto, R.D.; Kerkstra, S.; Rikxoort, E.M. van; Fetita, C.; Brillet, P.-Y.; Lefevre, C.; Xue, W.; Zhu, X; Liang, J.; Öksüz, I.; Ünay, D.; Kadipasaoglu, K.; Estépar, R.S.J.; Ross, J.C.; Washko, G.R.; Prieto, J.-C.; Hoyos, M.H.a.; Orkisz, M.; Meine, H.; Hüllebrand, M.; Stöcker, C.; Mir, F.L.; Naranjo, V.; Villanueva, E.; Staring, M.; Xiao, C.; Stoel, B.C.; Fabijanska, A.; Smistad, E.; Elster, A.C.; Lindseth, F.; Foruzan, A.H.; Kiros, R.; Popuri, K.; Cobzas, D.; Jimenez-Carretero, D.; Santos, A.; Ledesma-Carbayo, M.J.; Helmberger, M.; Urschler, M.; Pienn, M.; Bosboom, D.G.H.; Campo, A.; Prokop, M.; Jong, P.A. de; Solorzano, C.O. de; Muñoz-Barrutia, A.; Ginneken, B. van

    2014-01-01

    The {VESSEL12} ({VES}sel {SE}mentation in the {L}ung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography ({CT}) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities.

  4. Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images.

    Science.gov (United States)

    Meena Prakash, R; Shantha Selva Kumari, R

    2017-01-01

    The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.

  5. Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.

    Science.gov (United States)

    Conversano, Francesco; Franchini, Roberto; Demitri, Christian; Massoptier, Laurent; Montagna, Francesco; Maffezzoli, Alfonso; Malvasi, Antonio; Casciaro, Sergio

    2011-04-01

    The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features. The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively. A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

  6. A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image.

    Science.gov (United States)

    Ji, Ze-Xuan; Sun, Quan-Sen; Xia, De-Shen

    2011-07-01

    A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The segmentation target therefore is driven by two forces to smooth the derived optimal bias field and improve the accuracy of the segmentation task. The proposed method has been successfully applied to 3 T, 7 T, synthetic and real MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. Moreover, the proposed algorithm is robust to initialization, thereby allowing fully automatic applications. Copyright © 2010 Elsevier Ltd. All rights reserved.

  7. Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm

    Directory of Open Access Journals (Sweden)

    Fernando Cervantes-Sanchez

    2016-01-01

    Full Text Available This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA in X-ray angiograms. Since the single-scale Gabor filters (SSG are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az=0.9502 over a training set of 40 images and Az=0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.

  8. Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm.

    Science.gov (United States)

    Stefanoiu, Anca; Weinmann, Andreas; Storath, Martin; Navab, Nassir; Baust, Maximilian

    2016-05-11

    This paper presents a method for the simultaneous segmentation and regularization of a series of shapes from a corresponding sequence of images. Such series arise as time series of 2D images when considering video data, or as stacks of 2D images obtained by slicewise tomographic reconstruction. We first derive a model where the regularization of the shape signal is achieved by a total variation prior on the shape manifold. The method employs a modified Kendall shape space to facilitate explicit computations together with the concept of Sobolev gradients. For the proposed model, we derive an efficient and computationally accessible splitting scheme. Using a generalized forward-backward approach, our algorithm treats the total variation atoms of the splitting via proximal mappings, whereas the data terms are dealt with by gradient descent. The potential of the proposed method is demonstrated on various application examples dealing with 3D data. We explain how to extend the proposed combined approach to shape fields which, for instance, arise in the context of 3D+t imaging modalities, and show an application in this setup as well.

  9. Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

    Directory of Open Access Journals (Sweden)

    Viswanath Satish

    2012-02-01

    of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis.

  10. A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering

    Directory of Open Access Journals (Sweden)

    Li Ma

    2015-01-01

    Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA. The proposed algorithm combines artificial fish swarm algorithm (AFSA with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM.

  11. An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model.

    Science.gov (United States)

    Jahromi, Mahdi Kazemian; Kafieh, Raheleh; Rabbani, Hossein; Dehnavi, Alireza Mehri; Peyman, Alireza; Hajizadeh, Fedra; Ommani, Mohammadreza

    2014-07-01

    Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter-observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively.

  12. Interleaved segment correction achieves higher improvement factors in using genetic algorithm to optimize light focusing through scattering media

    Science.gov (United States)

    Li, Runze; Peng, Tong; Liang, Yansheng; Yang, Yanlong; Yao, Baoli; Yu, Xianghua; Min, Junwei; Lei, Ming; Yan, Shaohui; Zhang, Chunmin; Ye, Tong

    2017-10-01

    Focusing and imaging through scattering media has been proved possible with high resolution wavefront shaping. A completely scrambled scattering field can be corrected by applying a correction phase mask on a phase only spatial light modulator (SLM) and thereby the focusing quality can be improved. The correction phase is often found by global searching algorithms, among which Genetic Algorithm (GA) stands out for its parallel optimization process and high performance in noisy environment. However, the convergence of GA slows down gradually with the progression of optimization, causing the improvement factor of optimization to reach a plateau eventually. In this report, we propose an interleaved segment correction (ISC) method that can significantly boost the improvement factor with the same number of iterations comparing with the conventional all segment correction method. In the ISC method, all the phase segments are divided into a number of interleaved groups; GA optimization procedures are performed individually and sequentially among each group of segments. The final correction phase mask is formed by applying correction phases of all interleaved groups together on the SLM. The ISC method has been proved significantly useful in practice because of its ability to achieve better improvement factors when noise is present in the system. We have also demonstrated that the imaging quality is improved as better correction phases are found and applied on the SLM. Additionally, the ISC method lowers the demand of dynamic ranges of detection devices. The proposed method holds potential in applications, such as high-resolution imaging in deep tissue.

  13. A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance.

    Science.gov (United States)

    Zhang, Yue; Zou, Huanxin; Luo, Tiancheng; Qin, Xianxiang; Zhou, Shilin; Ji, Kefeng

    2016-10-13

    The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR) and Airborne Synthetic Aperture Radar (AirSAR) data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods.

  14. A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

    Directory of Open Access Journals (Sweden)

    Yue Zhang

    2016-10-01

    Full Text Available The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR and Airborne Synthetic Aperture Radar (AirSAR data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods.

  15. Real-time implementations of image segmentation algorithms on shared memory multicore architecture: a survey (Conference Presentation)

    Science.gov (United States)

    Akil, Mohamed

    2017-05-01

    The real-time processing is getting more and more important in many image processing applications. Image segmentation is one of the most fundamental tasks image analysis. As a consequence, many different approaches for image segmentation have been proposed. The watershed transform is a well-known image segmentation tool. The watershed transform is a very data intensive task. To achieve acceleration and obtain real-time processing of watershed algorithms, parallel architectures and programming models for multicore computing have been developed. This paper focuses on the survey of the approaches for parallel implementation of sequential watershed algorithms on multicore general purpose CPUs: homogeneous multicore processor with shared memory. To achieve an efficient parallel implementation, it's necessary to explore different strategies (parallelization/distribution/distributed scheduling) combined with different acceleration and optimization techniques to enhance parallelism. In this paper, we give a comparison of various parallelization of sequential watershed algorithms on shared memory multicore architecture. We analyze the performance measurements of each parallel implementation and the impact of the different sources of overhead on the performance of the parallel implementations. In this comparison study, we also discuss the advantages and disadvantages of the parallel programming models. Thus, we compare the OpenMP (an application programming interface for multi-Processing) with Ptheads (POSIX Threads) to illustrate the impact of each parallel programming model on the performance of the parallel implementations.

  16. Validation of Point Clouds Segmentation Algorithms Through Their Application to Several Case Studies for Indoor Building Modelling

    Science.gov (United States)

    Macher, H.; Landes, T.; Grussenmeyer, P.

    2016-06-01

    Laser scanners are widely used for the modelling of existing buildings and particularly in the creation process of as-built BIM (Building Information Modelling). However, the generation of as-built BIM from point clouds involves mainly manual steps and it is consequently time consuming and error-prone. Along the path to automation, a three steps segmentation approach has been developed. This approach is composed of two phases: a segmentation into sub-spaces namely floors and rooms and a plane segmentation combined with the identification of building elements. In order to assess and validate the developed approach, different case studies are considered. Indeed, it is essential to apply algorithms to several datasets and not to develop algorithms with a unique dataset which could influence the development with its particularities. Indoor point clouds of different types of buildings will be used as input for the developed algorithms, going from an individual house of almost one hundred square meters to larger buildings of several thousand square meters. Datasets provide various space configurations and present numerous different occluding objects as for example desks, computer equipments, home furnishings and even wine barrels. For each dataset, the results will be illustrated. The analysis of the results will provide an insight into the transferability of the developed approach for the indoor modelling of several types of buildings.

  17. Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.

    Science.gov (United States)

    Wiesmann, Veit; Bergler, Matthias; Palmisano, Ralf; Prinzen, Martin; Franz, Daniela; Wittenberg, Thomas

    2017-03-18

    Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

  18. Quantification of the myocardial area at risk using coronary CT angiography and Voronoi algorithm-based myocardial segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Kurata, Akira; Kono, Atsushi; Coenen, Adriaan; Saru-Chelu, Raluca G.; Krestin, Gabriel P. [Erasmus University Medical Center, Department of Radiology, Rotterdam (Netherlands); Sakamoto, Tsuyoshi [AZE inc, Development Division, Chiyoda, Tokyo (Japan); Kido, Teruhito; Mochizuki, Teruhito [Ehime University Graduate School of Medicine, Department of Radiology, Toon, Ehime (Japan); Higashino, Hiroshi [Yotsuba Circulation Clinic, Department of Radiology, Matsuyama, Ehime (Japan); Abe, Mitsunori [Yotsuba Circulation Clinic, Department of Cardiology, Matsuyama, Ehime (Japan); Feyter, Pim J. de; Nieman, Koen [Erasmus University Medical Center, Department of Radiology, Rotterdam (Netherlands); Erasmus University Medical Center, Department of Cardiology, Rotterdam (Netherlands)

    2015-01-15

    The purpose of this study was to estimate the myocardial area at risk (MAAR) using coronary computed tomography angiography (CTA) and Voronoi algorithm-based myocardial segmentation in comparison with single-photon emission computed tomography (SPECT). Thirty-four patients with coronary artery disease underwent 128-slice coronary CTA, stress/rest thallium-201 SPECT, and coronary angiography (CAG). CTA-based MAAR was defined as the sum of all CAG stenosis (>50 %) related territories (the ratio of the left ventricular volume). Using automated quantification software (17-segment model, 5-point scale), SPECT-based MAAR was defined as the number of segments with a score above zero as compared to the total 17 segments by summed stress score (SSS), difference (SDS) score map, and comprehensive SPECT interpretation with either SSS or SDS best correlating CAG findings (SSS/SDS). Results were compared using Pearson's correlation coefficient. Forty-nine stenoses were observed in 102 major coronary territories. Mean value of CTA-based MAAR was 28.3 ± 14.0 %. SSS-based, SDS-based, and SSS/SDS-based MAAR was 30.1 ± 6.1 %, 20.1 ± 15.8 %, and 26.8 ± 15.7 %, respectively. CTA-based MAAR was significantly related to SPECT-based MAAR (r = 0.531 for SSS; r = 0.494 for SDS; r = 0.814 for SSS/SDS; P < 0.05 in each). CTA-based Voronoi algorithm myocardial segmentation reliably quantifies SPECT-based MAAR. (orig.)

  19. A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

    Directory of Open Access Journals (Sweden)

    José García

    2017-01-01

    Full Text Available Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

  20. A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

    OpenAIRE

    José García; Christopher Pope; Francisco Altimiras

    2017-01-01

    Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application...

  1. A Spectral-Spatial Classification of Hyperspectral Images Based on the Algebraic Multigrid Method and Hierarchical Segmentation Algorithm

    Directory of Open Access Journals (Sweden)

    Haiwei Song

    2016-03-01

    Full Text Available The algebraic multigrid (AMG method is used to solve linear systems of equations on a series of progressively coarser grids and has recently attracted significant attention for image segmentation due to its high efficiency and robustness. In this paper, a novel spectral-spatial classification method for hyperspectral images based on the AMG method and hierarchical segmentation (HSEG algorithm is proposed. Our method consists of the following steps. First, the AMG method is applied to hyperspectral imagery to construct a multigrid structure of fine-to-coarse grids based on the anisotropic diffusion partial differential equation (PDE. The vertices in the multigrid structure are then considered as the initial seeds (markers for growing regions and are clustered to obtain a sequence of segmentation results. In the next step, a maximum vote decision rule is employed to combine the pixel-wise classification map and the segmentation maps. Finally, a final classification map is produced by choosing the optimal grid level to extract representative spectra. Experiments based on three different types of real hyperspectral datasets with different resolutions and contexts demonstrate that our method can obtain 3.84%–13.81% higher overall accuracies than the SVM classifier. The performance of our method was further compared to several marker-based spectral-spatial classification methods using objective quantitative measures and a visual qualitative evaluation.

  2. NOTE: Implementation of biologically conformal radiation therapy (BCRT) in an algorithmic segmentation-based inverse planning approach

    Science.gov (United States)

    Vanderstraeten, Barbara; DeGersem, Werner; Duthoy, Wim; DeNeve, Wilfried; Thierens, Hubert

    2006-08-01

    The development of new biological imaging technologies offers the opportunity to further individualize radiotherapy. Biologically conformal radiation therapy (BCRT) implies the use of the spatial distribution of one or more radiobiological parameters to guide the IMRT dose prescription. Our aim was to implement BCRT in an algorithmic segmentation-based planning approach. A biology-based segmentation tool was developed to generate initial beam segments that reflect the biological signal intensity pattern. The weights and shapes of the initial segments are optimized by means of an objective function that minimizes the root mean square deviation between the actual and intended dose values within the PTV. As proof of principle, [18F]FDG-PET-guided BCRT plans for two different levels of dose escalation were created for an oropharyngeal cancer patient. Both plans proved to be dosimetrically feasible without violating the planning constraints for the expanded spinal cord and the contralateral parotid gland as organs at risk. The obtained biological conformity was better for the first (2.5 Gy per fraction) than for the second (3 Gy per fraction) dose escalation level.

  3. A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data

    Energy Technology Data Exchange (ETDEWEB)

    Spiegel, M; Hornegger, J [Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen (Germany); Redel, T [Siemens AG Healthcare Sector, Forchheim (Germany); Struffert, T; Doerfler, A, E-mail: martin.spiegel@informatik.uni-erlangen.de [Department of Neuroradiology, University Erlangen-Nuremberg, Erlangen (Germany)

    2011-10-07

    Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.

  4. A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data

    International Nuclear Information System (INIS)

    Spiegel, M; Hornegger, J; Redel, T; Struffert, T; Doerfler, A

    2011-01-01

    Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.

  5. Fast track segment finding in the Monitored Drift Tubes (MDT) of the ATLAS Muon Spectrometer using a Legendre transform algorithm

    CERN Document Server

    Ntekas, Konstantinos; The ATLAS collaboration

    2018-01-01

    Many of the physics goals of ATLAS in the High Luminosity LHC era, including precision studies of the Higgs boson, require an unprescaled single muon trigger with a 20 GeV threshold. The selectivity of the current ATLAS first-level muon trigger is limited by the moderate spatial resolution of the muon trigger chambers. By incorporating the precise tracking of the MDT, the muon transverse momentum can be measured with an accuracy close to that of the offline reconstruction at the trigger level, sharpening the trigger turn-on curves and reducing the single muon trigger rate. A novel algorithm is proposed which reconstructs segments from MDT hits in an FPGA and find tracks within the tight latency constraints of the ATLAS first-level muon trigger. The algorithm represents MDT drift circles as curves in the Legendre space and returns one or more segment lines tangent to the maximum possible number of drift circles.  This algorithm is implemented without the need of resource and time consuming hit position calcul...

  6. Pharynx Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Pharynx Anatomy Add to My Pictures View /Download : Small: 720x576 ... View Download Large: 3000x2400 View Download Title: Pharynx Anatomy Description: Anatomy of the pharynx; drawing shows the ...

  7. Larynx Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Larynx Anatomy Add to My Pictures View /Download : Small: 648x576 ... View Download Large: 2700x2400 View Download Title: Larynx Anatomy Description: Anatomy of the larynx; drawing shows the ...

  8. Vulva Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Vulva Anatomy Add to My Pictures View /Download : Small: 720x634 ... View Download Large: 3000x2640 View Download Title: Vulva Anatomy Description: Anatomy of the vulva; drawing shows the ...

  9. The anatomy of anatomy

    OpenAIRE

    John Paul Judson

    2012-01-01

    The relationship between anatomy and surgeryhas been historic and epic, spanning many centuries,complementing each other in medical education andbeing independent as well as interdependent in manyways. However, curricular changes that have happenedglobally in recent years with the introduction of severalcontemporary styles of medical teaching have subtlydownplayed the importance of anatomy in medicine,allowing young doctors with poor knowledge of anatomyto become surgeons. With a whimsical in...

  10. Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

    OpenAIRE

    Le, Trong-Ngoc; Bao, Pham The; Huynh, Hieu Trung

    2016-01-01

    Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are co...

  11. Robust iris segmentation through parameterization of the Chan-Vese algorithm

    CSIR Research Space (South Africa)

    Mabuza-Hocquet, G

    2015-06-01

    Full Text Available that the pupil-iris boundaries are perfect circles sharing the same center, hence only use circle fitting methods for segmentation. The side effect posed by the traditional rubber sheet model used for normalization is; one cannot work backwards to place...

  12. Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images

    NARCIS (Netherlands)

    Hertem, van T.; Alchanatis, V.; Antler, A.; Maltz, E.; Halachmi, I.; Schlageter Tello, A.A.; Lokhorst, C.; Viazzi, S.; Romanini, C.E.B.; Pluk, A.; Bahr, C.; Berckmans, D.

    2013-01-01

    Computer vision techniques are a means to extract individual animal information such as weight, activity and calving time in intensive farming. Automatic detection requires adequate image pre-processing such as segmentation to precisely distinguish the animal from its background. For some analyses

  13. Layer stacking: A novel algorithm for individual forest tree segmentation from LiDAR point clouds

    Science.gov (United States)

    Elias Ayrey; Shawn Fraver; John A. Kershaw; Laura S. Kenefic; Daniel Hayes; Aaron R. Weiskittel; Brian E. Roth

    2017-01-01

    As light detection and ranging (LiDAR) technology advances, it has become common for datasets to be acquired at a point density high enough to capture structural information from individual trees. To process these data, an automatic method of isolating individual trees from a LiDAR point cloud is required. Traditional methods for segmenting trees attempt to isolate...

  14. The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer.

    Science.gov (United States)

    Bashir, Usman; Azad, Gurdip; Siddique, Muhammad Musib; Dhillon, Saana; Patel, Nikheel; Bassett, Paul; Landau, David; Goh, Vicky; Cook, Gary

    2017-12-01

    Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18 F-FDG PET/CT images. Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18 F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC). 40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85-0.92) compared with FLAB (median ICC 0.83; IQR 0.77-0.86) and FH (median ICC 0.77; IQR 0.7-0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs. Compared with both

  15. A Semiautomatic Segmentation Algorithm for Extracting the Complete Structure of Acini from Synchrotron Micro-CT Images

    Directory of Open Access Journals (Sweden)

    Luosha Xiao

    2013-01-01

    Full Text Available Pulmonary acinus is the largest airway unit provided with alveoli where blood/gas exchange takes place. Understanding the complete structure of acinus is necessary to measure the pathway of gas exchange and to simulate various mechanical phenomena in the lungs. The usual manual segmentation of a complete acinus structure from their experimentally obtained images is difficult and extremely time-consuming, which hampers the statistical analysis. In this study, we develop a semiautomatic segmentation algorithm for extracting the complete structure of acinus from synchrotron micro-CT images of the closed chest of mouse lungs. The algorithm uses a combination of conventional binary image processing techniques based on the multiscale and hierarchical nature of lung structures. Specifically, larger structures are removed, while smaller structures are isolated from the image by repeatedly applying erosion and dilation operators in order, adjusting the parameter referencing to previously obtained morphometric data. A cluster of isolated acini belonging to the same terminal bronchiole is obtained without floating voxels. The extracted acinar models above 98% agree well with those extracted manually. The run time is drastically shortened compared with manual methods. These findings suggest that our method may be useful for taking samples used in the statistical analysis of acinus.

  16. Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm.

    Science.gov (United States)

    Abdullah, Muhammad; Fraz, Muhammad Moazam; Barman, Sarah A

    2016-01-01

    Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

  17. Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

    Directory of Open Access Journals (Sweden)

    E.A. Zanaty

    2012-03-01

    Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

  18. Retinal Vessel Segmentation Based on Primal-Dual Asynchronous Particle Swarm Optimisation (pdAPSO Algorithm

    Directory of Open Access Journals (Sweden)

    E. G. Dada

    2017-04-01

    Full Text Available Acute damage to the retina vessel has been identified to be main reason for blindness and impaired vision all over the world. A timely detection and control of these illnesses can greatly decrease the number of loss of sight cases. Developing a high performance unsupervised retinal vessel segmentation technique poses an uphill task. This paper presents study on the Primal-Dual Asynchronous Particle Swarm Optimisation (pdAPSO method for the segmentation of retinal vessels. A maximum average accuracy rate 0.9243 with an average specificity of sensitivity rate of 0.9834 and average sensitivity rate of 0.5721 were achieved on DRIVE database. The proposed method produces higher mean sensitivity and accuracy rates in the same range of very good specificity.

  19. A sport scene images segmentation method based on edge detection algorithm

    Science.gov (United States)

    Chen, Biqing

    2011-12-01

    This paper proposes a simple, fast sports scene image segmentation method; a lot of work so far has been looking for a way to reduce the different shades of emotions in smooth area. A novel method of pretreatment, proposed the elimination of different shades feelings. Internal filling mechanism is used to change the pixels enclosed by the interest as interest pixels. For some test has achieved harvest sports scene images has been confirmed.

  20. Study of the vocal signal in the amplitude-time representation. Speech segmentation and recognition algorithms

    International Nuclear Information System (INIS)

    Baudry, Marc

    1978-01-01

    This dissertation exposes an acoustical and phonetical study of vocal signal. The complex pattern of the signal is segmented into simple sub-patterns and each one of these sub-patterns may be segmented again into another more simplest patterns with lower level. Application of pattern recognition techniques facilitates on one hand this segmentation and on the other hand the definition of the structural relations between the sub-patterns. Particularly, we have developed syntactic techniques in which the rewriting rules, context-sensitive, are controlled by predicates using parameters evaluated on the sub-patterns themselves. This allow to generalize a pure syntactic analysis by adding a semantic information. The system we expose, realizes pre-classification and a partial identification of the phonemes as also the accurate detection of each pitch period. The voice signal is analysed directly using the amplitude-time representation. This system has been implemented on a mini-computer and it works in the real time. (author) [fr

  1. Local Pixel Value Collection Algorithm for Spot Segmentation in Two-Dimensional Gel Electrophoresis Research

    OpenAIRE

    Peer, Peter; Corzo, Luis Galo

    2007-01-01

    Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as...

  2. Comparative assessment of segmentation algorithms for tumor delineation on a test-retest [(11)C]choline dataset.

    Science.gov (United States)

    Tomasi, Giampaolo; Shepherd, Tony; Turkheimer, Federico; Visvikis, Dimitris; Aboagye, Eric

    2012-12-01

    Many methods have been proposed for tumor segmentation from positron emission tomography images. Because of the increasingly important role that [(11)C]choline is playing in oncology and because no study has compared segmentation methods on this tracer, the authors assessed several segmentation algorithms on a [(11)C]choline test-retest dataset. Fixed and adaptive threshold-based methods, fuzzy C-means (FCM), Canny's edge detection method, the watershed transform, and the fuzzy locally adaptive Bayesian algorithm (FLAB) were used. Test-retest [(11)C]choline scans of nine patients with breast cancer were considered and the percent test-retest variability %VAR(TEST-RETEST) of tumor volume (TV) was employed to assess the results. The same methods were then applied to two denoised datasets generated by applying either a Gaussian filter or the wavelet transform. The (semi)automated methods FCM, FLAB, and Canny emerged as the best ones in terms of TV reproducibility. For these methods, the %root mean square error %RMSE of %VAR(TEST-RETEST), defined as %RMSE= variance+mean(2), was in the range 10%-21.2%, depending on the dataset and algorithm. Threshold-based methods gave TV estimates which were extremely variable, particularly on the unsmoothed data; their performance improved on the denoised datasets, whereas smoothing did not have a remarkable impact on the (semi)automated methods. TV variability was comparable to that of SUV(MAX) and SUV(MEAN) (range 14.7%-21.9% for %RMSE of %VAR(TEST-RETEST), after the exclusion of one outlier, 40%-43% when the outlier was included). The TV variability obtained with the best methods was similar to the one reported for TV in previous [(18)F]FDG and [(18)F]FLT studies and to the one of SUV(MAX)∕SUV(MEAN) on the authors' [(11)C]choline dataset. The good reproducibility of [(11)C]choline TV warrants further studies to test whether TV could predict early response to treatment and survival, as for [(18)F]FDG, to complement

  3. Influence of different contributions of scatter and attenuation on the threshold values in contrast-based algorithms for volume segmentation.

    Science.gov (United States)

    Matheoud, Roberta; Della Monica, Patrizia; Secco, Chiara; Loi, Gianfranco; Krengli, Marco; Inglese, Eugenio; Brambilla, Marco

    2011-01-01

    The aim of this work is to evaluate the role of different amount of attenuation and scatter on FDG-PET image volume segmentation using a contrast-oriented method based on the target-to-background (TB) ratio and target dimensions. A phantom study was designed employing 3 phantom sets, which provided a clinical range of attenuation and scatter conditions, equipped with 6 spheres of different volumes (0.5-26.5 ml). The phantoms were: (1) the Hoffman 3-dimensional brain phantom, (2) a modified International Electro technical Commission (IEC) phantom with an annular ring of water bags of 3 cm thickness fit over the IEC phantom, and (3) a modified IEC phantom with an annular ring of water bags of 9 cm. The phantoms cavities were filled with a solution of FDG at 5.4 kBq/ml activity concentration, and the spheres with activity concentration ratios of about 16, 8, and 4 times the background activity concentration. Images were acquired with a Biograph 16 HI-REZ PET/CT scanner. Thresholds (TS) were determined as a percentage of the maximum intensity in the cross section area of the spheres. To reduce statistical fluctuations a nominal maximum value is calculated as the mean from all voxel > 95%. To find the TS value that yielded an area A best matching the true value, the cross section were auto-contoured in the attenuation corrected slices varying TS in step of 1%, until the area so determined differed by less than 10 mm² versus its known physical value. Multiple regression methods were used to derive an adaptive thresholding algorithm and to test its dependence on different conditions of attenuation and scatter. The errors of scatter and attenuation correction increased with increasing amount of attenuation and scatter in the phantoms. Despite these increasing inaccuracies, PET threshold segmentation algorithms resulted not influenced by the different condition of attenuation and scatter. The test of the hypothesis of coincident regression lines for the three phantoms used

  4. Implementation of a cellular neural network-based segmentation algorithm on the bio-inspired vision system

    Science.gov (United States)

    Karabiber, Fethullah; Grassi, Giuseppe; Vecchio, Pietro; Arik, Sabri; Yalcin, M. Erhan

    2011-01-01

    Based on the cellular neural network (CNN) paradigm, the bio-inspired (bi-i) cellular vision system is a computing platform consisting of state-of-the-art sensing, cellular sensing-processing and digital signal processing. This paper presents the implementation of a novel CNN-based segmentation algorithm onto the bi-i system. The experimental results, carried out for different benchmark video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frame/sec. Comparisons with existing CNN-based methods show that, even though these methods are from two to six times faster than the proposed one, the conceived approach is more accurate and, consequently, represents a satisfying trade-off between real-time requirements and accuracy.

  5. Study of system for segmentation of images and elaboration of algorithms for three dimensional scene reconstruction

    International Nuclear Information System (INIS)

    Bufacchi, A.; Tripi, A.

    1995-09-01

    The aim of this paper is the presentation of a series of methodologies to recognize and to obtain a three-dimensional reconstruction of an inner architectural scene, using a gray level image obtained using a TV camera. In the first part of the work, a series of methods used to find the edges in an effective way are critically compared, obtaining a binary image, and then the application of the Hough transform to such binary image to find the straight lines in the original image are discussed. In the second part, an algorithm is shown in order to find the vanishing points in such image

  6. Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification

    Science.gov (United States)

    Lavanya, M; Kannan, P Muthu

    2017-12-29

    Lung cancer is a frequently lethal disease often causing death of human beings at an early age because of uncontrolled cell growth in the lung tissues. The diagnostic methods available are less than effective for detection of cancer. Therefore an automatic lesion segmentation method with computed tomography (CT) scans has been developed. However it is very difficult to perform automatic identification and segmentation of lung tumours with good accuracy because of the existence of variation in lesions. This paper describes the application of a robust lesion detection and segmentation technique to segment every individual cell from pathological images to extract the essential features. The proposed technique based on the FLICM (Fuzzy Local Information Cluster Means) algorithm used for segmentation, with reduced false positives in detecting lung cancers. The back propagation network used to classify cancer cells is based on computer aided diagnosis (CAD). Creative Commons Attribution License

  7. Hand Anatomy

    Science.gov (United States)

    ... Home Anatomy Bones Joints Muscles Nerves Vessels Tendons Anatomy The upper extremity is a term used to define the upper limb. This includes the shoulder, arm, forearm, wrist and hand. The hand is a very ...

  8. Tooth anatomy

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/article/002214.htm Tooth anatomy To use the sharing features on this page, ... upper jawbone is called the maxilla. Images Tooth anatomy References Chan S, Alessandrini EA. Dental injuries. In: Selbst ...

  9. Paraganglioma Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Paraganglioma Anatomy Add to My Pictures View /Download : Small: 648x576 ... View Download Large: 2700x2400 View Download Title: Paraganglioma Anatomy Description: Paraganglioma of the head and neck; drawing ...

  10. Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

    Directory of Open Access Journals (Sweden)

    Trong-Ngoc Le

    2016-01-01

    Full Text Available Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN, which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

  11. Evaluation of segmentation algorithms for x-ray-based microtomographic imaging of multi- phase flow in porous media

    Science.gov (United States)

    Wildenschild, D.; Porter, M. L.

    2007-12-01

    Significant strides have been made in recent years in imaging fluid flow in porous media using x-ray computerized microtomography (CMT) with 1-20 micron resolution; however, one of the remaining hurdles is precise quantification of the results. Tomographic imaging was for many years focused on volume rendering and the more qualitative analyses necessary for rapid assessment of the state of a patient's health. In recent years, many highly quantitative CMT-based studies of fluid flow processes in porous media have been reported; however, many of these analyses are made difficult by the complexities in processing the resulting grey-scale data into reliable applicable information such as pore network structures, phase saturations, interfacial areas, and curvatures. Yet, relatively few rigorous tests of these analysis tools have been reported so far. The work presented here was designed to evaluate segmentation and surface generation algorithms as they were applied to CMT images of gas-fluid configurations in a number of glass capillary tubes. Interfacial areas calculated with these algorithms were compared to actual interfacial geometries and we found very good agreement between actual and measured surface and interfacial areas. (The test images used are available for download at the website listed below)

  12. SURGICAL ANATOMY

    African Journals Online (AJOL)

    SURGICAL ANATOMY. Rare high origin of the radial artery: a bilateral, symmetrical ease. I. O. ()koro and B. C. J iburum. Department of Anatomy, College of Medicine, lrno State University, Owerri, Nigeria. Reprint requests to: Dr I. O. 0k0r0, Department of Anatomy, [mo State University, P. M. B. 2000. Owerri, Nigeria.

  13. Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer

    Energy Technology Data Exchange (ETDEWEB)

    Hoang Duc, Albert K., E-mail: albert.hoangduc.ucl@gmail.com; McClelland, Jamie; Modat, Marc; Cardoso, M. Jorge; Mendelson, Alex F. [Center for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom); Eminowicz, Gemma; Mendes, Ruheena; Wong, Swee-Ling; D’Souza, Derek [Radiotherapy Department, University College London Hospitals, 235 Euston Road, London NW1 2BU (United Kingdom); Veiga, Catarina [Department of Medical Physics and Bioengineering, University College London, London WC1E 6BT (United Kingdom); Kadir, Timor [Mirada Medical UK, Oxford Center for Innovation, New Road, Oxford OX1 1BY (United Kingdom); Ourselin, Sebastien [Centre for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom)

    2015-09-15

    Purpose: The aim of this study was to assess whether clinically acceptable segmentations of organs at risk (OARs) in head and neck cancer can be obtained automatically and efficiently using the novel “similarity and truth estimation for propagated segmentations” (STEPS) compared to the traditional “simultaneous truth and performance level estimation” (STAPLE) algorithm. Methods: First, 6 OARs were contoured by 2 radiation oncologists in a dataset of 100 patients with head and neck cancer on planning computed tomography images. Each image in the dataset was then automatically segmented with STAPLE and STEPS using those manual contours. Dice similarity coefficient (DSC) was then used to compare the accuracy of these automatic methods. Second, in a blind experiment, three separate and distinct trained physicians graded manual and automatic segmentations into one of the following three grades: clinically acceptable as determined by universal delineation guidelines (grade A), reasonably acceptable for clinical practice upon manual editing (grade B), and not acceptable (grade C). Finally, STEPS segmentations graded B were selected and one of the physicians manually edited them to grade A. Editing time was recorded. Results: Significant improvements in DSC can be seen when using the STEPS algorithm on large structures such as the brainstem, spinal canal, and left/right parotid compared to the STAPLE algorithm (all p < 0.001). In addition, across all three trained physicians, manual and STEPS segmentation grades were not significantly different for the brainstem, spinal canal, parotid (right/left), and optic chiasm (all p > 0.100). In contrast, STEPS segmentation grades were lower for the eyes (p < 0.001). Across all OARs and all physicians, STEPS produced segmentations graded as well as manual contouring at a rate of 83%, giving a lower bound on this rate of 80% with 95% confidence. Reduction in manual interaction time was on average 61% and 93% when automatic

  14. Development and Evaluation of an Algorithm for the Computer-Assisted Segmentation of the Human Hypothalamus on 7-Tesla Magnetic Resonance Images

    Science.gov (United States)

    Schmidt, Laura; Anwander, Alfred; Strauß, Maria; Trampel, Robert; Bazin, Pierre-Louis; Möller, Harald E.; Hegerl, Ulrich; Turner, Robert; Geyer, Stefan

    2013-01-01

    Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures. PMID:23935821

  15. 3D visualization and finite element mesh formation from wood anatomy samples, Part II – Algorithm approach

    Directory of Open Access Journals (Sweden)

    Petr Koňas

    2009-01-01

    Full Text Available Paper presents new original application WOOD3D in form of program code assembling. The work extends the previous article “Part I – Theoretical approach” in detail description of implemented C++ classes of utilized projects Visualization Toolkit (VTK, Insight Toolkit (ITK and MIMX. Code is written in CMake style and it is available as multiplatform application. Currently GNU Linux (32/64b and MS Windows (32/64b platforms were released. Article discusses various filter classes for image filtering. Mainly Otsu and Binary threshold filters are classified for anatomy wood samples thresholding. Registration of images series is emphasized for difference of colour spaces compensation is included. Resulted work flow of image analysis is new methodological approach for images processing through the composition, visualization, filtering, registration and finite element mesh formation. Application generates script in ANSYS parametric design language (APDL which is fully compatible with ANSYS finite element solver and designer environment. The script includes the whole definition of unstructured finite element mesh formed by individual elements and nodes. Due to simple notation, the same script can be used for generation of geometrical entities in element positions. Such formed volumetric entities are prepared for further geometry approximation (e.g. by boolean or more advanced methods. Hexahedral and tetrahedral types of mesh elements are formed on user request with specified mesh options. Hexahedral meshes are formed both with uniform element size and with anisotropic character. Modified octree method for hexahedral mesh with anisotropic character was declared in application. Multicore CPUs in the application are supported for fast image analysis realization. Visualization of image series and consequent 3D image are realized in VTK format sufficiently known and public format, visualized in GPL application Paraview. Future work based on mesh

  16. Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels

    International Nuclear Information System (INIS)

    Bolte, H.; Jahnke, T.; Schaefer, F.K.W.; Wenke, R.; Hoffmann, B.; Freitag-Wolf, S.; Dicken, V.; Kuhnigk, J.M.; Lohmann, J.; Voss, S.; Knoess, N.

    2007-01-01

    Objective: The aim of this study was to investigate the interobserver variability of CT based diameter and volumetric measurements of artificial pulmonary nodules. A special interest was the consideration of different measurement methods, observer experience and training levels. Materials and methods: For this purpose 46 artificial small solid nodules were examined in a dedicated ex-vivo chest phantom with multislice-spiral CT (20 mAs, 120 kV, collimation 16 mm x 0.75 mm, table feed 15 mm, reconstructed slice thickness 1 mm, reconstruction increment 0.7 mm, intermediate reconstruction kernel). Two observer groups of different radiologic experience (0 and more than 5 years of training, 3 observers each) analysed all lesions with digital callipers and 2 volumetry software packages (click-point depending and robust volumetry) in a semi-automatic and manually corrected mode. For data analysis the variation coefficient (VC) was calculated in per cent for each group and a Wilcoxon test was used for analytic statistics. Results: Click-point robust volumetry showed with a VC of <0.01% in both groups the smallest interobserver variability. Between experienced and un-experienced observers interobserver variability was significantly different for diameter measurements (p = 0.023) but not for semi-automatic and manual corrected volumetry. A significant training effect was revealed for diameter measurements (p = 0.003) and semi-automatic measurements of click-point depending volumetry (p = 0.007) in the un-experienced observer group. Conclusions: Compared to diameter measurements volumetry achieves a significantly smaller interobserver variance and advanced volumetry algorithms are independent of observer experience

  17. Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging

    Directory of Open Access Journals (Sweden)

    Jane Tufvesson

    2015-01-01

    Full Text Available Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n=40, test set n=50. Manual delineation was reference standard and second observer analysis was performed in a subset (n=25. The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.

  18. Identification of linear features at geothermal field based on Segment Tracing Algorithm (STA) of the ALOS PALSAR data

    Science.gov (United States)

    Haeruddin; Saepuloh, A.; Heriawan, M. N.; Kubo, T.

    2016-09-01

    Indonesia has about 40% of geothermal energy resources in the world. An area with the potential geothermal energy in Indonesia is Wayang Windu located at West Java Province. The comprehensive understanding about the geothermal system in this area is indispensable for continuing the development. A geothermal system generally associated with joints or fractures and served as the paths for the geothermal fluid migrating to the surface. The fluid paths are identified by the existence of surface manifestations such as fumaroles, solfatara and the presence of alteration minerals. Therefore the analyses of the liner features to geological structures are crucial for identifying geothermal potential. Fractures or joints in the form of geological structures are associated with the linear features in the satellite images. The Segment Tracing Algorithm (STA) was used for the basis to determine the linear features. In this study, we used satellite images of ALOS PALSAR in Ascending and Descending orbit modes. The linear features obtained by satellite images could be validated by field observations. Based on the application of STA to the ALOS PALSAR data, the general direction of extracted linear features were detected in WNW-ESE, NNE-SSW and NNW-SSE. The directions are consistent with the general direction of faults system in the field. The linear features extracted from ALOS PALSAR data based on STA were very useful to identify the fractured zones at geothermal field.

  19. Algorithms

    Indian Academy of Sciences (India)

    have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming language Is called a program. From activities 1-3, we can observe that: • Each activity is a command.

  20. Integer anatomy

    Energy Technology Data Exchange (ETDEWEB)

    Doolittle, R. [ONR, Arlington, VA (United States)

    1994-11-15

    The title integer anatomy is intended to convey the idea of a systematic method for displaying the prime decomposition of the integers. Just as the biological study of anatomy does not teach us all things about behavior of species neither would we expect to learn everything about the number theory from a study of its anatomy. But, some number-theoretic theorems are illustrated by inspection of integer anatomy, which tend to validate the underlying structure and the form as developed and displayed in this treatise. The first statement to be made in this development is: the way structure of the natural numbers is displayed depends upon the allowed operations.

  1. Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation

    Science.gov (United States)

    Carles, Montserrat; Fechter, Tobias; Nemer, Ursula; Nanko, Norbert; Mix, Michael; Nestle, Ursula; Schaefer, Andrea

    2015-12-01

    PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements (Δ φ =0.3+/- 1.6 mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC  =  0.66+/- 0.04 ), Positive Predictive Value (PPV  =  0.81+/- 0.06 ) and Sensitivity (Sen.  =  0.49+/- 0.05 ). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol)  =  40+/- 30 , DSC  =  0.71+/- 0.07 and PPV  =  0.90+/- 0.13 ). High accuracy in target tracking position (Δ ME) was obtained for experimental and clinical data (Δ ME{{}\\text{exp}}=0+/- 3 mm; Δ ME{{}\\text{clin}}=0.3+/- 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume

  2. Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm.

    Science.gov (United States)

    Ghane, Narjes; Vard, Alireza; Talebi, Ardeshir; Nematollahy, Pardis

    2017-01-01

    Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, k-means clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell's image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.

  3. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

    Science.gov (United States)

    Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

    2017-11-01

    Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV

  4. SU-C-BRA-01: Interactive Auto-Segmentation for Bowel in Online Adaptive MRI-Guided Radiation Therapy by Using a Multi-Region Labeling Algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Y; Chen, I; Kashani, R; Wan, H; Maughan, N; Muccigrosso, D; Parikh, P [Washington University School of Medicine, Saint Louis, MO (United States)

    2016-06-15

    Purpose: In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. Methods: 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graph cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician’s manual contouring by using metrics of Dice coefficient and Hausdorff distance. Results: Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. Conclusion: The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.

  5. Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm

    Directory of Open Access Journals (Sweden)

    Yachun Pang

    2012-01-01

    Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.

  6. Algorithms

    Indian Academy of Sciences (India)

    algorithms such as synthetic (polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language ... ·1 x:=sln(theta) x : = sm(theta) 1. ~. Idl d.t Read A.B,C. ~ lei ~ Print x.y.z. L;;;J. Figure 2 Symbols used In flowchart language to rep- resent Assignment, Read.

  7. Algorithms

    Indian Academy of Sciences (India)

    In the previous articles, we have discussed various common data-structures such as arrays, lists, queues and trees and illustrated the widely used algorithm design paradigm referred to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted ...

  8. Computer-assisted segmentation of CT images by statistical region merging for the production of voxel models of anatomy for CT dosimetry

    Czech Academy of Sciences Publication Activity Database

    Caon, M.; Sedlář, Jiří; Bajger, M.; Lee, G.

    2014-01-01

    Roč. 37, č. 2 (2014), s. 393-403 ISSN 0158-9938 Institutional support: RVO:67985556 Keywords : Voxel model * Image segmentation * Statistical region merging * CT dosimetry Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.882, year: 2014 http://library.utia.cas.cz/separaty/2014/ZOI/sedlar-0428537.pdf

  9. A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation.

    Science.gov (United States)

    da Silva Senra Filho, Antonio Carlos

    2017-11-18

    Multiple sclerosis (MS) is a neurodegenerative disease with increasing importance in recent years, in which the T2 weighted with fluid attenuation inversion recovery (FLAIR) MRI imaging technique has been addressed for the hyperintense MS lesion assessment. Many automatic lesion segmentation approaches have been proposed in the literature in order to assist health professionals. In this study, a new hybrid lesion segmentation approach based on logistic classification (LC) and the iterative contrast enhancement (ICE) method is proposed (LC+ICE). T1 and FLAIR MRI images from 32 secondary progressive MS (SPMS) patients were used in the LC+ICE method, in which manual segmentation was used as the ground truth lesion segmentation. The DICE, Sensitivity, Specificity, Area under the ROC curve (AUC), and Volume Similarity measures showed that the LC+ICE method is able to provide a precise and robust lesion segmentation estimate, which was compared with two recent FLAIR lesion segmentation approaches. In addition, the proposed method also showed a stable segmentation among lesion loads, showing a wide applicability to different disease stages. The LC+ICE procedure is a suitable alternative to assist the manual FLAIR hyperintense MS lesion segmentation task.

  10. White matter lesion segmentation using machine learning and weakly labeled MR images

    Science.gov (United States)

    Xie, Yuchen; Tao, Xiaodong

    2011-03-01

    We propose a fast, learning-based algorithm for segmenting white matter (WM) lesions for magnetic resonance (MR) brain images. The inputs to the algorithm are T1, T2, and FLAIR images. Unlike most of the previously reported learning-based algorithms, which treat expert labeled lesion map as ground truth in the training step, the proposed algorithm only requires the user to provide a few regions of interest (ROI's) containing lesions. An unsupervised clustering algorithm is applied to segment these ROI's into areas. Based on the assumption that lesion voxels have higher intensity on FLAIR image, areas corresponding to lesions are identified and their probability distributions in T1, T2, and FLAIR images are computed. The lesion segmentation in 3D is done by using the probability distributions to generate a confidence map of lesion and applying a graph based segmentation algorithm to label lesion voxels. The initial lesion label is used to further refine the probability distribution estimation for the final lesion segmentation. The advantages of the proposed algorithm are: 1. By using the weak labels, we reduced the dependency of the segmentation performance on the expert discrimination of lesion voxels in the training samples; 2. The training can be done using labels generated by users with only general knowledge of brain anatomy and image characteristics of WM lesion, instead of these carefully labeled by experienced radiologists; 3. The algorithm is fast enough to make interactive segmentation possible. We test the algorithm on nine ACCORD-MIND MRI datasets. Experimental results show that our algorithm agrees well with expert labels and outperforms a support vector machine based WM lesion segmentation algorithm.

  11. An Improved Algorithm Based on Minimum Spanning Tree for Multi-scale Segmentation of Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    LI Hui

    2015-07-01

    Full Text Available As the basis of object-oriented information extraction from remote sensing imagery,image segmentation using multiple image features,exploiting spatial context information, and by a multi-scale approach are currently the research focuses. Using an optimization approach of the graph theory, an improved multi-scale image segmentation method is proposed. In this method, the image is applied with a coherent enhancement anisotropic diffusion filter followed by a minimum spanning tree segmentation approach, and the resulting segments are merged with reference to a minimum heterogeneity criterion.The heterogeneity criterion is defined as a function of the spectral characteristics and shape parameters of segments. The purpose of the merging step is to realize the multi-scale image segmentation. Tested on two images, the proposed method was visually and quantitatively compared with the segmentation method employed in the eCognition software. The results show that the proposed method is effective and outperforms the latter on areas with subtle spectral differences.

  12. Algorithms

    Indian Academy of Sciences (India)

    In the program shown in Figure 1, we have repeated the algorithm. M times and we can make the following observations. Each block is essentially a different instance of "code"; that is, the objects differ by the value to which N is initialized before the execution of the. "code" block. Thus, we can now avoid the repetition of the ...

  13. Algorithms

    Indian Academy of Sciences (India)

    algorithms built into the computer corresponding to the logic- circuit rules that are used to .... For the purpose of carrying ou t ari thmetic or logical operations the memory is organized in terms .... In fixed point representation, one essentially uses integer arithmetic operators assuming the binary point to be at some point other ...

  14. Effect of a novel motion correction algorithm (SSF) on the image quality of coronary CTA with intermediate heart rates: Segment-based and vessel-based analyses

    Energy Technology Data Exchange (ETDEWEB)

    Li, Qianwen, E-mail: qianwen18@126.com; Li, Pengyu, E-mail: lipyu818@gmail.com; Su, Zhuangzhi, E-mail: suzhuangzhi@xwh.ccmu.edu.cn; Yao, Xinyu, E-mail: 314985151@qq.com; Wang, Yan, E-mail: wy19851121@126.com; Wang, Chen, E-mail: fskwangchen@gmail.com; Du, Xiangying, E-mail: duxying_xw@163.com; Li, Kuncheng, E-mail: kuncheng.li@gmail.com

    2014-11-15

    analysis, IQS was improved in most segments (9/14). Conclusion: The SSF algorithm can provide acceptable diagnostic image quality in coronary CTA for patients with intermediate HR.

  15. Regulatory Anatomy

    DEFF Research Database (Denmark)

    Hoeyer, Klaus

    2015-01-01

    , legal documents, technological devices, organizational structures, and work practices aimed at minimizing risk. I use this term to reorient the analytical attention with respect to safety regulation. Instead of evaluating whether safety is achieved, the point is to explore the types of “safety” produced...... they arise. In short, I expose the regulatory anatomy of the policy landscape....

  16. Automatic segmentation of the optic nerves and chiasm in CT and MR using the atlas-navigated optimal medial axis and deformable-model algorithm

    Science.gov (United States)

    Noble, Jack H.; Dawant, Benoit M.

    2009-02-01

    In recent years, radiation therapy has become the preferred treatment for many types of head and neck tumors. To minimize side effects, radiation beams are planned pre-operatively to avoid over-radiation of vital structures, such as the optic nerves and chiasm, which are essential to the visual process. To plan the procedure, these structures must be identified using CT/MR imagery. Currently, a radiation oncologist must manually segment the structures, which is both inefficient and ineffective. Clearly an automated approach could be beneficial to the planning process. The problem is difficult due to the shape variability and low image contrast of the structures, and several attempts at automatic localization have been reported with marginal results. In this work we present a novel method for localizing the optic nerves and chiasm in CT/MR volumes using the atlas-navigated optimal medial axis and deformable-model algorithm (NOMAD). NOMAD uses a statistical model and image registration to provide a priori local intensity and shape information to both a medial axis extraction procedure and a deformable-model, which deforms the medial axis and completes the segmentation process. This approach achieves mean dice coefficients greater than 0.8 for both the optic nerves and the chiasm when compared to manual segmentations over ten test cases. By comparing quantitative results with existing techniques it can be seen that this method produces more accurate results.

  17. The Anatomy of Learning Anatomy

    Science.gov (United States)

    Wilhelmsson, Niklas; Dahlgren, Lars Owe; Hult, Hakan; Scheja, Max; Lonka, Kirsti; Josephson, Anna

    2010-01-01

    The experience of clinical teachers as well as research results about senior medical students' understanding of basic science concepts has much been debated. To gain a better understanding about how this knowledge-transformation is managed by medical students, this work aims at investigating their ways of setting about learning anatomy.…

  18. Validation tools for image segmentation

    Science.gov (United States)

    Padfield, Dirk; Ross, James

    2009-02-01

    A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.

  19. Anatomy Transfer

    OpenAIRE

    Dicko, Ali Hamadi; Liu, Tiantian; Gilles, Benjamin; Kavan, Ladislav; Faure, François; Palombi, Olivier; Cani, Marie-Paule

    2013-01-01

    International audience; Characters with precise internal anatomy are important in film and visual effects, as well as in medical applications. We propose the first semi-automatic method for creating anatomical structures, such as bones, muscles, viscera and fat tissues. This is done by transferring a reference anatomical model from an input template to an arbitrary target character, only defined by its boundary representation (skin). The fat distribution of the target character needs to be sp...

  20. Image segmentation, evaluation, and applications

    OpenAIRE

    McGuinness, Kevin

    2010-01-01

    This thesis aims to advance research in image segmentation by developing robust techniques for evaluating image segmentation algorithms. The key contributions of this work are as follows. First, we investigate the characteristics of existing measures for supervised evaluation of automatic image segmentation algorithms. We show which of these measures is most effective at distinguishing perceptually accurate image segmentation from inaccurate segmentation. We then apply these measures to evalu...

  1. Quantification of Right and Left Ventricular Function in Cardiac MR Imaging: Comparison of Semiautomatic and Manual Segmentation Algorithms

    Science.gov (United States)

    Souto, Miguel; Masip, Lambert Raul; Couto, Miguel; Suárez-Cuenca, Jorge Juan; Martínez, Amparo; Tahoces, Pablo G.; Carreira, Jose Martin; Croisille, Pierre

    2013-01-01

    The purpose of this study was to evaluate the performance of a semiautomatic segmentation method for the anatomical and functional assessment of both ventricles from cardiac cine magnetic resonance (MR) examinations, reducing user interaction to a “mouse-click”. Fifty-two patients with cardiovascular diseases were examined using a 1.5-T MR imaging unit. Several parameters of both ventricles, such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF), were quantified by an experienced operator using the conventional method based on manually-defined contours, as the standard of reference; and a novel semiautomatic segmentation method based on edge detection, iterative thresholding and region growing techniques, for evaluation purposes. No statistically significant differences were found between the two measurement values obtained for each parameter (p > 0.05). Correlation to estimate right ventricular function was good (r > 0.8) and turned out to be excellent (r > 0.9) for the left ventricle (LV). Bland-Altman plots revealed acceptable limits of agreement between the two methods (95%). Our study findings indicate that the proposed technique allows a fast and accurate assessment of both ventricles. However, further improvements are needed to equal results achieved for the right ventricle (RV) using the conventional methodology. PMID:26835680

  2. A Comparison of Amplitude-Based and Phase-Based Positron Emission Tomography Gating Algorithms for Segmentation of Internal Target Volumes of Tumors Subject to Respiratory Motion

    Energy Technology Data Exchange (ETDEWEB)

    Jani, Shyam S., E-mail: sjani@mednet.ucla.edu [Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California (United States); Robinson, Clifford G. [Department of Radiation Oncology, Siteman Cancer Center, Washington University in St Louis, St Louis, Missouri (United States); Dahlbom, Magnus [Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, California (United States); White, Benjamin M.; Thomas, David H.; Gaudio, Sergio; Low, Daniel A.; Lamb, James M. [Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California (United States)

    2013-11-01

    Purpose: To quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET). Methods and Materials: List-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated. Results: Tumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P<.0323). A1 produced the largest and most accurate ITVs for spheres with diameters of ≥2 cm (P=.002). No significant difference was shown between algorithms in the 1-cm sphere data set. For phantom spheres, amplitude-based methods recovered an average of 9.5% more motion displacement than temporal-based methods under regular breathing conditions and an average of 45.7% more in the presence of baseline drift (P<.001). Conclusions: Target volumes in images generated

  3. A Comparison of Amplitude-Based and Phase-Based Positron Emission Tomography Gating Algorithms for Segmentation of Internal Target Volumes of Tumors Subject to Respiratory Motion

    International Nuclear Information System (INIS)

    Jani, Shyam S.; Robinson, Clifford G.; Dahlbom, Magnus; White, Benjamin M.; Thomas, David H.; Gaudio, Sergio; Low, Daniel A.; Lamb, James M.

    2013-01-01

    Purpose: To quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET). Methods and Materials: List-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated. Results: Tumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P<.0323). A1 produced the largest and most accurate ITVs for spheres with diameters of ≥2 cm (P=.002). No significant difference was shown between algorithms in the 1-cm sphere data set. For phantom spheres, amplitude-based methods recovered an average of 9.5% more motion displacement than temporal-based methods under regular breathing conditions and an average of 45.7% more in the presence of baseline drift (P<.001). Conclusions: Target volumes in images generated

  4. Unraveling Pancreatic Segmentation.

    Science.gov (United States)

    Renard, Yohann; de Mestier, Louis; Perez, Manuela; Avisse, Claude; Lévy, Philippe; Kianmanesh, Reza

    2018-04-01

    Limited pancreatic resections are increasingly performed, but the rate of postoperative fistula is higher than after classical resections. Pancreatic segmentation, anatomically and radiologically identifiable, may theoretically help the surgeon removing selected anatomical portions with their own segmental pancreatic duct and thus might decrease the postoperative fistula rate. We aimed at systematically and comprehensively reviewing the previously proposed pancreatic segmentations and discuss their relevance and limitations. PubMed database was searched for articles investigating pancreatic segmentation, including human or animal anatomy, and cadaveric or surgical studies. Overall, 47/99 articles were selected and grouped into 4 main hypotheses of pancreatic segmentation methodology: anatomic, vascular, embryologic and lymphatic. The head, body and tail segments are gross description without distinct borders. The arterial territories defined vascular segments and isolate an isthmic paucivascular area. The embryological theory relied on the fusion plans of the embryological buds. The lymphatic drainage pathways defined the lymphatic segmentation. These theories had differences, but converged toward separating the head and body/tail parts, and the anterior from posterior and inferior parts of the pancreatic head. The rate of postoperative fistula was not decreased when surgical resection was performed following any of these segmentation theories; hence, none of them appeared relevant enough to guide pancreatic transections. Current pancreatic segmentation theories do not enable defining anatomical-surgical pancreatic segments. Other approaches should be explored, in particular focusing on pancreatic ducts, through pancreatic ducts reconstructions and embryologic 3D modelization.

  5. Thymus Gland Anatomy

    Science.gov (United States)

    ... hyphen, e.g. -historical Searches are case-insensitive Thymus Gland, Adult, Anatomy Add to My Pictures View / ... 1500x1200 View Download Large: 3000x2400 View Download Title: Thymus Gland, Adult, Anatomy Description: Anatomy of the thymus ...

  6. Normal Pancreas Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Pancreas Anatomy Add to My Pictures View /Download : Small: 761x736 ... View Download Large: 3172x3068 View Download Title: Pancreas Anatomy Description: Anatomy of the pancreas; drawing shows the ...

  7. Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.

    Science.gov (United States)

    Rezaee, Kh; Haddadnia, J

    2013-09-01

    Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters' number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

  8. Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Rezaee Kh

    2013-09-01

    Full Text Available Background: Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. Objective: This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. Method: In the frst step, after designing a flter, the discrete wavelet transform is applied to the input images and the approximate coeffcients of scaling components are constructed. Then, the different parts of image are classifed in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters’ number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. Results: We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM. The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coeffcient was approximately 0.85, which proved the suitable reliability of the system performance. Conclusion: The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

  9. Optimization of automated segmentation of monkeypox virus-induced lung lesions from normal lung CT images using hard C-means algorithm

    Science.gov (United States)

    Castro, Marcelo A.; Thomasson, David; Avila, Nilo A.; Hufton, Jennifer; Senseney, Justin; Johnson, Reed F.; Dyall, Julie

    2013-03-01

    Monkeypox virus is an emerging zoonotic pathogen that results in up to 10% mortality in humans. Knowledge of clinical manifestations and temporal progression of monkeypox disease is limited to data collected from rare outbreaks in remote regions of Central and West Africa. Clinical observations show that monkeypox infection resembles variola infection. Given the limited capability to study monkeypox disease in humans, characterization of the disease in animal models is required. A previous work focused on the identification of inflammatory patterns using PET/CT image modality in two non-human primates previously inoculated with the virus. In this work we extended techniques used in computer-aided detection of lung tumors to identify inflammatory lesions from monkeypox virus infection and their progression using CT images. Accurate estimation of partial volumes of lung lesions via segmentation is difficult because of poor discrimination between blood vessels, diseased regions, and outer structures. We used hard C-means algorithm in conjunction with landmark based registration to estimate the extent of monkeypox virus induced disease before inoculation and after disease progression. Automated estimation is in close agreement with manual segmentation.

  10. Quick Dissection of the Segmental Bronchi

    Science.gov (United States)

    Nakajima, Yuji

    2010-01-01

    Knowledge of the three-dimensional anatomy of the bronchopulmonary segments is essential for respiratory medicine. This report describes a quick guide for dissecting the segmental bronchi in formaldehyde-fixed human material. All segmental bronchi are easy to dissect, and thus, this exercise will help medical students to better understand the…

  11. Ensemble segmentation using efficient integer linear programming.

    Science.gov (United States)

    Alush, Amir; Goldberger, Jacob

    2012-10-01

    We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the "space of segmentations" which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.

  12. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu Jianwu; Hori, Masatoshi [Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)

    2010-05-15

    Purpose: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. Methods: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as ''gold standard.''Results: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F=0.77; p(F{<=}f)=0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less

  13. 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

  14. 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.

  15. Analysis of Intergrade Variables In The Fuzzy C-Means And Improved Algorithm Cat Swarm Optimization(FCM-ISO) In Search Segmentation

    Science.gov (United States)

    Saragih, Jepronel; Salim Sitompul, Opim; Situmorang, Zakaria

    2017-12-01

    One of the techniques known in Data Mining namely clustering. Image segmentation process does not always represent the actual image which is caused by a combination of algorithms as long as it has not been able to obtain optimal cluster centers. In this research will search for the smallest error with the counting result of a Fuzzy C Means process optimized with Cat swam Algorithm Optimization that has been developed by adding the weight of the energy in the process of Tracing Mode.So with the parameter can be determined the most optimal cluster centers and most closely with the data will be made the cluster. Weigh inertia in this research, namely: (0.1), (0.2), (0.3), (0.4), (0.5), (0.6), (0.7), (0.8) and (0.9). Then compare the results of each variable values inersia (W) which is different and taken the smallest results. Of this weighting analysis process can acquire the right produce inertia variable cost function the smallest.

  16. Segmentation of the ovine lung in 3D CT Images

    Science.gov (United States)

    Shi, Lijun; Hoffman, Eric A.; Reinhardt, Joseph M.

    2004-04-01

    Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.

  17. 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...

  18. The velocity of a radioactive bolus in the oesophagus evaluated by means of an image segmentation algorithm

    International Nuclear Information System (INIS)

    Miquelin, Charlie A; Dantas, Roberto O; Oliveira, Ricardo B; Braga, Francisco Jos H. N

    2002-01-01

    Classical scintigraphic evaluation of a radioactive bolus through the oesophagus is based on regions of interest and time/activity curves, which only gives information about the total time required for it to cross the organ. Instantaneous parameters can be obtained if the exact position (centroid) of the bolus is known. For that, one needs to know the co-ordinates of the centre of mass of the bolus radioactivity distribution. From this, one can obtain velocity at each time. Obtaining such a new parameter would be important, to try to determine whether the anatomical differences among the 3 thirds of the oesophagus have a functional correspondence or not. We have studied 5 normal volunteers (4 males, 1 female, 33-68 yo). Each volunteer swallowed (unique swallowing) 40 MBq of 99mTc-phytate in 10 ml water. Eighty frames (0.3 sec) were acquired in a scintillation camera. External marks were used to separate the pharynx from the oesophagus. Images were transformed into bitmap by means of a Sophy Medical processing module and analysed by means of the algorithm, which determines the co-ordinates of the centroid (horizontal and vertical) for each frame and instant velocities through the organ. Different velocities were found in typical evaluations. Curves representing the different positions of the bolus C and the correspondent different Vs were obtained. Different velocities of the bolus were detected during the pharyngeal phase, and proximal, mid and distal parts of the oesophagus. Larger studies are necessary, but it seems that the velocity of a radioactive bolus changes in the different parts of the oesophagus. It is reasonable to say that there is a functional correspondence to the anatomical differences in the organ (Au)

  19. Object Recognition and Segmentation of Wounds

    OpenAIRE

    Wåsjø, Robin

    2015-01-01

    Object recognition and segmentation of objects is a complex task. Our goal is to develop an algorithm that can recognize and segment wound objects in images. We attempt to solve the object recognition and segmentation problem by using a hypothesis optimization framework. This method optimizes the object segmentation by assigning objective function values to the object segmentation hypotheses. The optimization algorithm is a genetic algorithm. The objective function relies on textural and shap...

  20. Head segmentation in vertebrates

    OpenAIRE

    Kuratani, Shigeru; Schilling, Thomas

    2008-01-01

    Classic theories of vertebrate head segmentation clearly exemplify the idealistic nature of comparative embryology prior to the 20th century. Comparative embryology aimed at recognizing the basic, primary structure that is shared by all vertebrates, either as an archetype or an ancestral developmental pattern. Modern evolutionary developmental (Evo-Devo) studies are also based on comparison, and therefore have a tendency to reduce complex embryonic anatomy into overly simplified patterns. Her...

  1. Image Information Mining Utilizing Hierarchical Segmentation

    Science.gov (United States)

    Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

    2002-01-01

    The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

  2. Robust optic nerve segmentation on clinically acquired CT

    Science.gov (United States)

    Panda, Swetasudha; Asman, Andrew J.; DeLisi, Michael P.; Mawn, Louise A.; Galloway, Robert L.; Landman, Bennett A.

    2014-03-01

    The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image fieldof- view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.

  3. Normal Female Reproductive Anatomy

    Science.gov (United States)

    ... an inner lining called the endometrium. Normal female reproductive system anatomy. Topics/Categories: Anatomy -- Gynecologic Type: Color, Medical Illustration Source: National Cancer Institute Creator: Terese Winslow (Illustrator) AV Number: CDR609921 Date Created: November 17, 2014 Date Added: ...

  4. Segmentation of radiographic images under topological constraints: application to the femur

    International Nuclear Information System (INIS)

    Gamage, Pavan; Xie, Sheng Quan; Delmas, Patrice; Xu, Wei Liang

    2010-01-01

    A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions. (orig.)

  5. Segmentation of radiographic images under topological constraints: application to the femur

    Energy Technology Data Exchange (ETDEWEB)

    Gamage, Pavan; Xie, Sheng Quan [University of Auckland, Department of Mechanical Engineering (Mechatronics), Auckland (New Zealand); Delmas, Patrice [University of Auckland, Department of Computer Science, Auckland (New Zealand); Xu, Wei Liang [Massey University, School of Engineering and Advanced Technology, Auckland (New Zealand)

    2010-09-15

    A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions. (orig.)

  6. IRIS: Intelligent Roadway Image Segmentation

    OpenAIRE

    Brown, Ryan Charles

    2014-01-01

    The problem of roadway navigation and obstacle avoidance for unmanned ground vehicles has typically needed very expensive sensing to operate properly. To reduce the cost of sensing, it is proposed that an algorithm be developed that uses a single visual camera to image the roadway, determine where the lane of travel is in the image, and segment that lane. The algorithm would need to be as accurate as current lane finding algorithms as well as faster than a standard k- means segmentation acros...

  7. A Heuristic Image Search Algorithm for Active Shape Model Segmentation of the Caudate Nucleus and Hippocampus in Brain MR Images of Children with FASD

    Directory of Open Access Journals (Sweden)

    A A Eicher

    2012-09-01

    Full Text Available Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of Fetal Alcohol Spectrum Disorder (FASD - the most common form of preventable mental retardation worldwide. One approach aims to detect brain abnormalities through an assessment of volume and shape of two sub-cortical structures, the caudate nucleus and hippocampus. We present a method for automatically segmenting these structures from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model, which is used to learn shape variation from manually segmented training data. A modified discrete Geometrically Deformable Model is used to generate point correspondence between training models. An ASM is then created from the landmark points. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the hippocampus and caudate nucleus. Various popular image search techniques were tested, including an edge detection method and a method based on grey profile Mahalanobis distance measurement. A novel heuristic image search method was also developed and tested. This heuristic method improves image segmentation by taking advantage of characteristics specific to the target data, such as a relatively homogeneous tissue colour in target structures. Results show that ASMs that use the heuristic image search technique produce the most accurate segmentations. An ASM constructed using this technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study.

  8. Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3d segmentation algorithms

    Directory of Open Access Journals (Sweden)

    Yee Kwo

    2011-06-01

    Full Text Available Abstract Background Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss/.

  9. Parallel fuzzy connected image segmentation on GPU

    OpenAIRE

    Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

    2011-01-01

    Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm impleme...

  10. Clinical anatomy of the subserous layer: An amalgamation of gross and clinical anatomy.

    Science.gov (United States)

    Yabuki, Yoshihiko

    2016-05-01

    The 1998 edition of Terminologia Anatomica introduced some currently used clinical anatomical terms for the pelvic connective tissue or subserous layer. These innovations persuaded the present author to consider a format in which the clinical anatomical terms could be reconciled with those of gross anatomy and incorporated into a single anatomical glossary without contradiction or ambiguity. Specific studies on the subserous layer were undertaken on 79 Japanese women who had undergone surgery for uterine cervical cancer, and on 26 female cadavers that were dissected, 17 being formalin-fixed and 9 fresh. The results were as follows: (a) the subserous layer could be segmentalized by surgical dissection in the perpendicular, horizontal and sagittal planes; (b) the segmentalized subserous layer corresponded to 12 cubes, or ligaments, of minimal dimension that enabled the pelvic organs to be extirpated; (c) each ligament had a three-dimensional (3D) structure comprising craniocaudal, mediolateral, and dorsoventral directions vis-á-vis the pelvic axis; (d) these 3D-structured ligaments were encoded morphologically in order of decreasing length; and (e) using these codes, all the surgical procedures for 19th century to present-day radical hysterectomy could be expressed symbolically. The establishment of clinical anatomical terms, represented symbolically through coding as demonstrated in this article, could provide common ground for amalgamating clinical anatomy with gross anatomy. Consequently, terms in clinical anatomy and gross anatomy could be reconciled and compiled into a single anatomical glossary. © 2015 Wiley Periodicals, Inc.

  11. Polarimetric Segmentation Using Wishart Test Statistic

    DEFF Research Database (Denmark)

    Skriver, Henning; Schou, Jesper; Nielsen, Allan Aasbjerg

    2002-01-01

    ) approach, which is a merging algorithm for single channel SAR images. The polarimetric version described in this paper uses the above-mentioned test statistic for merging. The segmentation algorithm has been applied to polarimetric SAR data from the Danish dual-frequency, airborne polarimetric SAR, EMISAR......A newly developed test statistic for equality of two complex covariance matrices following the complex Wishart distribution and an associated asymptotic probability for the test statistic has been used in a segmentation algorithm. The segmentation algorithm is based on the MUM (merge using moments....... The results show clearly an improved segmentation performance for the full polarimetric algorithm compared to single channel approaches....

  12. Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms.

    Science.gov (United States)

    Yusung Kim; Patwardhan, Kaustubh Anil; Beichel, Reinhard R; Smith, Brian J; Mart, Christopher; Plichta, Kristin A; Chang, Tangel; Sonka, Milan; Graham, Michael M; Magnotta, Vince; Casavant, Thomas; Junyi Xia; Buatti, John M

    2017-07-01

    RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p segmentation method regardless of STAPLE design (p segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.

  13. An interactive segmentation method based on superpixel

    DEFF Research Database (Denmark)

    Yang, Shu; Zhu, Yaping; Wu, Xiaoyu

    2015-01-01

    This paper proposes an interactive image-segmentation method which is based on superpixel. To achieve fast segmentation, the method is used to establish a Graphcut model using superpixels as nodes, and a new energy function is proposed. Experimental results demonstrate that the authors' method has...... excellent performance in terms of segmentation accuracy and computation efficiency compared with other segmentation algorithm based on pixels....

  14. Modeling of Craniofacial Anatomy, Variation, and Growth

    DEFF Research Database (Denmark)

    Thorup, Signe Strann

    the two images. To elaborate further: a computational atlas of the average anatomy was constructed. Using non-rigid registration, image data from a subject is automatically transformed into the coordinate space of the atlas. In this process, all knowledge built into the atlas is transferred to the subject......-subject variation etc. Besides image registration, a volumetric segmentation method using graph cuts was developed and applied for intracranial volume estimation. Graph cut is a fast method for segmentation utilizing a suitable graph. Three different craniofacial anomalies were examined in this thesis: Cleft lip...

  15. Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures.

    Science.gov (United States)

    Garg, Amanmeet; Wong, Darren; Popuri, Karteek; Poskitt, Kenneth J; Fitzpatrick, Kevin; Bjornson, Bruce; Grunau, Ruth E; Beg, Mirza Faisal

    2014-10-01

    Manual segmentation of anatomy in brain MRI data taken to be the closest to the "gold standard" in quality is often used in automated registration-based segmentation paradigms for transfer of template labels onto the unlabeled MRI images. This study presents a library of template data with 16 subcortical structures in the central brain area which were manually labeled for MRI data from 22 children (8 male, [Formula: see text]). The lateral ventricle, thalamus, caudate, putamen, hippocampus, cerebellum, third vevntricle, fourth ventricle, brainstem, and corpuscallosum were segmented by two expert raters. Cross-validation experiments with randomized template subset selection were conducted to test for their ability to accurately segment MRI data under an automated segmentation pipeline. A high value of the dice similarity coefficient ([Formula: see text], [Formula: see text], [Formula: see text]) and small Hausdorff distance ([Formula: see text], [Formula: see text], [Formula: see text]) of the automated segmentation against the manual labels was obtained on this template library data. Additionally, comparison with segmentation obtained from adult templates showed significant improvement in accuracy with the use of an age-matched library in this cohort. A manually delineated pediatric template library such as the one described here could provide a useful benchmark for testing segmentation algorithms.

  16. Metrics for image segmentation

    Science.gov (United States)

    Rees, Gareth; Greenway, Phil; Morray, Denise

    1998-07-01

    An important challenge in mapping image-processing techniques onto applications is the lack of quantitative performance measures. From a systems engineering perspective these are essential if system level requirements are to be decomposed into sub-system requirements which can be understood in terms of algorithm selection and performance optimization. Nowhere in computer vision is this more evident than in the area of image segmentation. This is a vigorous and innovative research activity, but even after nearly two decades of progress, it remains almost impossible to answer the question 'what would the performance of this segmentation algorithm be under these new conditions?' To begin to address this shortcoming, we have devised a well-principled metric for assessing the relative performance of two segmentation algorithms. This allows meaningful objective comparisons to be made between their outputs. It also estimates the absolute performance of an algorithm given ground truth. Our approach is an information theoretic one. In this paper, we describe the theory and motivation of our method, and present practical results obtained from a range of state of the art segmentation methods. We demonstrate that it is possible to measure the objective performance of these algorithms, and to use the information so gained to provide clues about how their performance might be improved.

  17. Text segmentation for MRC document compression.

    Science.gov (United States)

    Haneda, Eri; Bouman, Charles A

    2011-06-01

    The mixed raster content (MRC) standard (ITU-T T.44) specifies a framework for document compression which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC compression is the separation of the document into foreground and background layers, represented as a binary mask. Therefore, the resulting quality and compression ratio of a MRC document encoder is highly dependent upon the segmentation algorithm used to compute the binary mask. In this paper, we propose a novel multiscale segmentation scheme for MRC document encoding based upon the sequential application of two algorithms. The first algorithm, cost optimized segmentation (COS), is a blockwise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, connected component classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using an Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multiscale framework in order to improve the segmentation accuracy of text with varying size. In comparisons to state-of-the-art commercial MRC products and selected segmentation algorithms in the literature, we show that the new algorithm achieves greater accuracy of text detection but with a lower false detection rate of nontext features. We also demonstrate that the proposed segmentation algorithm can improve the quality of decoded documents while simultaneously lowering the bit rate.

  18. 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.

  19. Anatomy Comic Strips

    Science.gov (United States)

    Park, Jin Seo; Kim, Dae Hyun; Chung, Min Suk

    2011-01-01

    Comics are powerful visual messages that convey immediate visceral meaning in ways that conventional texts often cannot. This article's authors created comic strips to teach anatomy more interestingly and effectively. Four-frame comic strips were conceptualized from a set of anatomy-related humorous stories gathered from the authors' collective…

  20. Automatic anatomy recognition via multiobject oriented active shape models.

    Science.gov (United States)

    Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

    2010-12-01

    This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a

  1. 3D visualization and finite element mesh formation from wood anatomy samples, Part I – Theoretical approach

    Directory of Open Access Journals (Sweden)

    Petr Koňas

    2009-01-01

    Full Text Available The work summarizes created algorithms for formation of finite element (FE mesh which is derived from bitmap pattern. Process of registration, segmentation and meshing is described in detail. C++ library of STL from Insight Toolkit (ITK Project together with Visualization Toolkit (VTK were used for base processing of images. Several methods for appropriate mesh output are discussed. Multiplatform application WOOD3D for the task under GNU GPL license was assembled. Several methods of segmentation and mainly different ways of contouring were included. Tetrahedral and rectilinear types of mesh were programmed. Improving of mesh quality in some simple ways is mentioned. Testing and verification of final program on wood anatomy samples of spruce and walnut was realized. Methods of microscopic anatomy samples preparation are depicted. Final utilization of formed mesh in the simple structural analysis was performed.The article discusses main problems in image analysis due to incompatible colour spaces, samples preparation, thresholding and final conversion into finite element mesh. Assembling of mentioned tasks together and evaluation of the application are main original results of the presented work. In presented program two thresholding filters were used. By utilization of ITK two following filters were included. Otsu filter based and binary filter based were used. The most problematic task occurred in a production of wood anatomy samples in the unique light conditions with minimal or zero co­lour space shift and the following appropriate definition of thresholds (corresponding thresholding parameters and connected methods (prefiltering + registration which influence the continuity and mainly separation of wood anatomy structure. Solution in samples staining is suggested with the following quick image analysis realization. Next original result of the work is complex fully automated application which offers three types of finite element mesh

  2. Optimally segmented magnetic structures

    DEFF Research Database (Denmark)

    Insinga, Andrea Roberto; Bahl, Christian; Bjørk, Rasmus

    We present a semi-analytical algorithm for magnet design problems, which calculates the optimal way to subdivide a given design region into uniformly magnetized segments.The availability of powerful rare-earth magnetic materials such as Nd-Fe-B has broadened the range of applications of permanent...

  3. Anatomy of Sarcocaulon

    Directory of Open Access Journals (Sweden)

    R. L. Verhoeven

    1983-11-01

    Full Text Available The anatomy of the leaf blade, petiole, stem and root of the genus Sarcocaulon (DC. Sweet is discussed. On the basis of the leaf anatomy, the four sections recognized by Moffett (1979 can be identified: section Denticulati (dorsiventral leaves, section Multifidi (isobilateral leaves and adaxial and abaxial palisade continuous at midvein, section Crenati (isobilateral leaves, short curved trichomes and glandular hairs, section Sarcocaulon (isobilateral leaves and glandular hairs only. The anatomy of the stem is typically that of a herbaceous dicotyledon with a thick periderm. The root structure shows that the function of the root is not food storage.

  4. A multiparametric and multiscale approach to automated segmentation of brain veins.

    Science.gov (United States)

    Monti, S; Palma, G; Borrelli, P; Tedeschi, E; Cocozza, S; Salvatore, M; Mancini, M

    2015-08-01

    Cerebral vein analysis provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injuries. In order to assess the vascular anatomy, manual segmentation approaches can be used but are observer-dependent and time-consuming. In the present work, a fully automated cerebral vein segmentation method is proposed, based on a multiscale and multiparametric approach. The combined investigation of the R2(*)- and a Vesselness probability-map was used to obtain a fast and highly reliable classification of venous voxels. A semiquantitative analysis showed that our approach outperformed the previous state-of-the-art algorithm both in sensitivity and specificity. Inclusion of this tool within a parametric brain framework may therefore pave the way for a quantitative study of the intracranial venous system.

  5. Segmentation of multicolor fluorescence in situ hybridization images using an improved fuzzy C-means clustering algorithm by incorporating both spatial and spectral information.

    Science.gov (United States)

    Li, Jingyao; Lin, Dongdong; Wang, Yu-Ping

    2017-10-01

    Multicolor fluorescence in situ hybridization (M-FISH) is a multichannel imaging technique for rapid detection of chromosomal abnormalities. It is a critical and challenging step to segment chromosomes from M-FISH images toward better chromosome classification. Recently, several fuzzy C-means (FCM) clustering-based methods have been proposed for M-FISH image segmentation or classification, e.g., adaptive fuzzy C-means (AFCM) and improved AFCM (IAFCM), but most of these methods used only one channel imaging information with limited accuracy. To improve the segmentation for better accuracy and more robustness, we proposed an FCM clustering-based method, denoted by spatial- and spectral-FCM. Our method has the following advantages: (1) it is able to exploit information from neighboring pixels (spatial information) to reduce the noise and (2) it can incorporate pixel information across different channels simultaneously (spectral information) into the model. We evaluated the performance of our method by comparing with other FCM-based methods in terms of both accuracy and false-positive detection rate on synthetic, hybrid, and real images. The comparisons on 36 M-FISH images have shown that our proposed method results in higher segmentation accuracy ([Formula: see text]) and a lower false-positive ratio ([Formula: see text]) than conventional FCM (accuracy: [Formula: see text], and false-positive ratio: [Formula: see text]) and the IAFCM (accuracy: [Formula: see text] and false-positive ratio: [Formula: see text]) methods by incorporating both spatial and spectral information from M-FISH images.

  6. Comparison of a Gross Anatomy Laboratory to Online Anatomy Software for Teaching Anatomy

    Science.gov (United States)

    Mathiowetz, Virgil; Yu, Chih-Huang; Quake-Rapp, Cindee

    2016-01-01

    This study was designed to assess the grades, self-perceived learning, and satisfaction between occupational therapy students who used a gross anatomy laboratory versus online anatomy software (AnatomyTV) as tools to learn anatomy at a large public university and a satellite campus in the mid-western United States. The goal was to determine if…

  7. Algorithm for the automatic computation of the modified Anderson-Wilkins acuteness score of ischemia from the pre-hospital ECG in ST-segment elevation myocardial infarction

    DEFF Research Database (Denmark)

    Fakhri, Yama; Sejersten, Maria; Schoos, Mikkel Malby

    2017-01-01

    acute) to 4 (most acute)) identifies patients with substantial myocardial salvage potential regardless of patient reported symptom duration. However, due to the complexity of the score, it is not used in clinical practice. Therefore, we aimed to develop a reliable algorithm that automatically computes...

  8. Segmentation of MRI Volume Data Based on Clustering Method

    Directory of Open Access Journals (Sweden)

    Ji Dongsheng

    2016-01-01

    Full Text Available Here we analyze the difficulties of segmentation without tag line of left ventricle MR images, and propose an algorithm for automatic segmentation of left ventricle (LV internal and external profiles. Herein, we propose an Incomplete K-means and Category Optimization (IKCO method. Initially, using Hough transformation to automatically locate initial contour of the LV, the algorithm uses a simple approach to complete data subsampling and initial center determination. Next, according to the clustering rules, the proposed algorithm finishes MR image segmentation. Finally, the algorithm uses a category optimization method to improve segmentation results. Experiments show that the algorithm provides good segmentation results.

  9. Premedical anatomy experience and student performance in medical gross anatomy.

    Science.gov (United States)

    Kondrashov, Peter; McDaniel, Dalton J; Jordan, Rebecca M

    2017-04-01

    Gross anatomy is considered one of the most important basic science courses in medical education, yet few medical schools require its completion prior to matriculation. The effect of taking anatomy courses before entering medical school on performance in medical gross anatomy has been previously studied with inconsistent results. The effect of premedical anatomy coursework on performance in medical gross anatomy, overall medical school grade point average (GPA), and Comprehensive Osteopathic Medical Licensing Examination Level 1 (COMLEX 1) score was evaluated in 456 first-year osteopathic medical students along with a survey on its perceived benefits on success in medical gross anatomy course. No significant differences were found in gross anatomy grade, GPA, or COMLEX 1 score between students with premedical anatomy coursework and those without. However, significant differences and higher scores were observed in students who had taken three or more undergraduate anatomy courses including at least one with cadaveric laboratory. There was significantly lower perceived benefit for academic success in the medical gross anatomy course (Pstudents who had taken premedical anatomy courses (5.9 of 10) compared with those who had not (8.2 of 10). Results suggest that requiring any anatomy course as a prerequisite for medical school would not have significant effect on student performance in the medical gross anatomy course. However, requiring more specific anatomy coursework including taking three or more undergraduate anatomy courses, one with cadaveric laboratory component, may result in higher medical gross anatomy grades, medical school GPA, and COMLEX 1 scores. Clin. Anat. 30:303-311, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  10. Template characterization and correlation algorithm created from segmentation for the iris biometric authentication based on analysis of textures implemented on a FPGA

    International Nuclear Information System (INIS)

    Giacometto, F J; Vilardy, J M; Torres, C O; Mattos, L

    2011-01-01

    Among the most used biometric signals to set personal security permissions, taker increasingly importance biometric iris recognition based on their textures and images of blood vessels due to the rich in these two unique characteristics that are unique to each individual. This paper presents an implementation of an algorithm characterization and correlation of templates created for biometric authentication based on iris texture analysis programmed on a FPGA (Field Programmable Gate Array), authentication is based on processes like characterization methods based on frequency analysis of the sample, and frequency correlation to obtain the expected results of authentication.

  11. Hierarchical morphological segmentation for image sequence coding

    OpenAIRE

    Salembier Clairon, Philippe Jean; Pardàs Feliu, Montse

    1994-01-01

    This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then...

  12. Vascular anatomy of strictured small bowel.

    Science.gov (United States)

    Mansard, Magnus Jayaraj; Rao, Upender; Pradeep, R; Sekaran, Anuradha; Rao, G V; Reddy, D N

    2011-01-01

    To investigate the role of ischemia in the pathogenesis of small bowel strictures. Vascular anatomy of 39 small bowel strictures was studied using modified Spalteholtz method. Ten normal small bowel segments were studied as controls. 71.8% of small bowel strictures showed block in the mesenteric vessels (p=0.008). Subset analysis of tuberculous strictures showed block in the mesenteric vessels in 70.8% of strictures (p=0.0098). Ischemia plays a significant role in the pathogenesis of small bowel strictures. Mesenteric vasculopathy has a significant association with tuberculous strictures of the intestine.

  13. Anatomy and biomechanics of psoas major.

    Science.gov (United States)

    Bogduk, N; Pearcy, M; Hadfield, G

    1992-05-01

    The fascicular anatomy of the psoas major was determined by dissection in three cadavers. Its actions on the lumbar spine in the sagittal plane were modelled on erect, flexion, and extension radiographs of ten adult males. Calculations revealed that psoas exerts only very small moments that tend to extend the upper lumbar spine and to flex the lower lumbar spine, but at maximum contraction the psoas exerts severe compression forces on the lumbar segments, and large shear forces. Copyright © 1992. Published by Elsevier Ltd.

  14. Imaging of jaw with dental CT software program: Normal Anatomy

    International Nuclear Information System (INIS)

    Kim, Myong Gon; Seo, Kwang Hee; Jung, Hak Young; Sung, Nak Kwan; Chung, Duk Soo; Kim, Ok Dong; Lee, Young Hwan

    1994-01-01

    Dental CT software program can provide reformatted cross-sectional and panoramic images that cannot be obtained with conventional axial and direct coronal CT scan. The purpose of this study is to describe the method of the technique and to identify the precise anatomy of jaw. We evaluated 13 mandibles and 7 maxillae of 15 subjects without bony disease who were being considered for endosseous dental implants. Reformatted images obtained by the use of bone algorithm performed on GE HiSpeed Advantage CT scanner were retrospectively reviewed for detailed anatomy of jaw. Anatomy related to neurovascular bundle(mandibular foramen, inferior alveolar canal, mental foramen, canal for incisive artery, nutrient canal, lingual foramen and mylohyoid groove), muscular insertion(mylohyoid line, superior and inferior genial tubercle and digastric fossa) and other anatomy(submandibular fossa, sublingual fossa, contour of alveolar process, oblique line, retromolar fossa, temporal crest and retromolar triangle) were well delineated in mandible. In maxilla, anatomy related to neurovascular bundle(greater palatine foramen and groove, nasopalatine canal and incisive foramen) and other anatomy(alveolar process, maxillary sinus and nasal fossa) were also well delineated. Reformatted images using dental CT software program provided excellent delineation of the jaw anatomy. Therefore, dental CT software program can play an important role in the preoperative assessment of mandible and maxilla for dental implants and other surgical conditions

  15. Imaging of jaw with dental CT software program: Normal Anatomy

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Myong Gon; Seo, Kwang Hee; Jung, Hak Young; Sung, Nak Kwan; Chung, Duk Soo; Kim, Ok Dong [School of Medicine, Taegu Catholic University, Taegu (Korea, Republic of); Lee, Young Hwan [Taegu Armed Forces General Hospital, Taegu (Korea, Republic of)

    1994-07-15

    Dental CT software program can provide reformatted cross-sectional and panoramic images that cannot be obtained with conventional axial and direct coronal CT scan. The purpose of this study is to describe the method of the technique and to identify the precise anatomy of jaw. We evaluated 13 mandibles and 7 maxillae of 15 subjects without bony disease who were being considered for endosseous dental implants. Reformatted images obtained by the use of bone algorithm performed on GE HiSpeed Advantage CT scanner were retrospectively reviewed for detailed anatomy of jaw. Anatomy related to neurovascular bundle(mandibular foramen, inferior alveolar canal, mental foramen, canal for incisive artery, nutrient canal, lingual foramen and mylohyoid groove), muscular insertion(mylohyoid line, superior and inferior genial tubercle and digastric fossa) and other anatomy(submandibular fossa, sublingual fossa, contour of alveolar process, oblique line, retromolar fossa, temporal crest and retromolar triangle) were well delineated in mandible. In maxilla, anatomy related to neurovascular bundle(greater palatine foramen and groove, nasopalatine canal and incisive foramen) and other anatomy(alveolar process, maxillary sinus and nasal fossa) were also well delineated. Reformatted images using dental CT software program provided excellent delineation of the jaw anatomy. Therefore, dental CT software program can play an important role in the preoperative assessment of mandible and maxilla for dental implants and other surgical conditions.

  16. Skull Base Anatomy.

    Science.gov (United States)

    Patel, Chirag R; Fernandez-Miranda, Juan C; Wang, Wei-Hsin; Wang, Eric W

    2016-02-01

    The anatomy of the skull base is complex with multiple neurovascular structures in a small space. Understanding all of the intricate relationships begins with understanding the anatomy of the sphenoid bone. The cavernous sinus contains the carotid artery and some of its branches; cranial nerves III, IV, VI, and V1; and transmits venous blood from multiple sources. The anterior skull base extends to the frontal sinus and is important to understand for sinus surgery and sinonasal malignancies. The clivus protects the brainstem and posterior cranial fossa. A thorough appreciation of the anatomy of these various areas allows for endoscopic endonasal approaches to the skull base. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Variation in root wood anatomy

    NARCIS (Netherlands)

    Cutler, D.F.

    1976-01-01

    Variability in the anatomy of root wood of selected specimens particularly Fraxinus excelsior L. and Acer pseudoplatanus L. in the Kew reference microscope slide collection is discussed in relation to generalised statements in the literature on root wood anatomy.

  18. Segmentation of complex document

    Directory of Open Access Journals (Sweden)

    Souad Oudjemia

    2014-06-01

    Full Text Available In this paper we present a method for segmentation of documents image with complex structure. This technique based on GLCM (Grey Level Co-occurrence Matrix used to segment this type of document in three regions namely, 'graphics', 'background' and 'text'. Very briefly, this method is to divide the document image, in block size chosen after a series of tests and then applying the co-occurrence matrix to each block in order to extract five textural parameters which are energy, entropy, the sum entropy, difference entropy and standard deviation. These parameters are then used to classify the image into three regions using the k-means algorithm; the last step of segmentation is obtained by grouping connected pixels. Two performance measurements are performed for both graphics and text zones; we have obtained a classification rate of 98.3% and a Misclassification rate of 1.79%.

  19. An interactive anatomy dissection DVD

    OpenAIRE

    Al-Sabah, Fadel YS

    2013-01-01

    Anatomy remains the cornerstone of medical education. Human anatomy has not changed, yet our understanding of the topic and the methods by which we teach anatomy continue to evolve. At present lectures, tutorials and human cadaveric dissection in the anatomy room remain central to anatomical education in the Republic of Ireland and throughout many parts of the world. With the emergence of new technologies, new teaching methods can be explored. In-house and on-line teaching of Radiology and...

  20. Synopsis of radiologic anatomy

    International Nuclear Information System (INIS)

    Meschan, I.

    1987-01-01

    The book is a compact version of earlier publications that appeared in 1975 as a one- and a two-volume issue under the title 'Atlas of Radiologic Anatomy'. A chapter on computed tomography has been added as this novel technique requires a new approach to radiologic anatomy. The radiologist will find all the information on the anatomic conditions he needs for analysing radiographs and CT pictures. More than 600 radiographs and CT pictures are given that illustrate typical and rare findings. The book also is useful as a source of reference for making good radiographs and evaluating the quality of radiographs or CT pictures. With 1413 figs., 18 tabs [de

  1. Patient specific anatomy: the new area of anatomy based on computer science illustrated on liver.

    Science.gov (United States)

    Soler, Luc; Mutter, Didier; Pessaux, Patrick; Marescaux, Jacques

    2015-01-01

    Over the past century, medical imaging has brought a new revolution: internal anatomy of a patient could be seen without any invasive technique. This revolution has highlighted the two main limits of current anatomy: the anatomical description is physician dependent, and the average anatomy is more and more frequently insufficient to describe anatomical variations. These drawbacks can sometimes be so important that they create mistakes but they can be overcome through the use of 3D patient-specific surgical anatomy. In this article, we propose to illustrate such improvement of standard anatomy on liver. We first propose a general scheme allowing to easily compare the four main liver anatomical descriptions by Takasaki, Goldsmith and Woodburne, Bismuth and Couinaud. From this general scheme we propose four rules to apply in order to correct these initial anatomical definitions. Application of these rules allows to correct usual vascular topological mistakes of standard anatomy. We finally validate such correction on a database of 20 clinical cases compared to the 111 clinical cases of a Couinaud article. Out of the 20 images of the database, we note a revealing difference in 14 cases (70%) on at least one important branch of the portal network. Only six cases (30%) do not present a revealing difference between both labellings. We also show that the right portal fissure location on our 20 cases defined between segment V and VI of our anatomical definition is well correlated with the real position described by Couinaud on 111 cases, knowing that the theoretical position was only found in 46 cases out of 111, i.e., 41.44% of cases with the non-corrected Couinaud definition. We have proposed a new anatomical segmentation of the liver based on four main rules to apply in order to correct topological errors of the four main standard segmentations. Our validation clearly illustrates that this new definition corrects the large amount of mistakes created by the current

  2. 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.

  3. Learning Anatomy Enhances Spatial Ability

    Science.gov (United States)

    Vorstenbosch, Marc A. T. M.; Klaassen, Tim P. F. M.; Donders, A. R. T.; Kooloos, Jan G. M.; Bolhuis, Sanneke M.; Laan, Roland F. J. M.

    2013-01-01

    Spatial ability is an important factor in learning anatomy. Students with high scores on a mental rotation test (MRT) systematically score higher on anatomy examinations. This study aims to investigate if learning anatomy also oppositely improves the MRT-score. Five hundred first year students of medicine ("n" = 242, intervention) and…

  4. Learning anatomy enhances spatial ability.

    NARCIS (Netherlands)

    Vorstenbosch, M.A.T.M.; Klaassen, T.P.; Donders, A.R.T.; Kooloos, J.G.M.; Bolhuis, S.M.; Laan, R.F.J.M.

    2013-01-01

    Spatial ability is an important factor in learning anatomy. Students with high scores on a mental rotation test (MRT) systematically score higher on anatomy examinations. This study aims to investigate if learning anatomy also oppositely improves the MRT-score. Five hundred first year students of

  5. Segmentation: Identification of consumer segments

    DEFF Research Database (Denmark)

    Høg, Esben

    2005-01-01

    It is very common to categorise people, especially in the advertising business. Also traditional marketing theory has taken in consumer segments as a favorite topic. Segmentation is closely related to the broader concept of classification. From a historical point of view, classification has its...... origin in other sciences as for example biology, anthropology etc. From an economic point of view, it is called segmentation when specific scientific techniques are used to classify consumers to different characteristic groupings. What is the purpose of segmentation? For example, to be able to obtain...... a basic understanding of grouping people. Advertising agencies may use segmentation totarget advertisements, while food companies may usesegmentation to develop products to various groups of consumers. MAPP has for example investigated the positioning of fish in relation to other food products...

  6. Segmental Vitiligo.

    Science.gov (United States)

    van Geel, Nanja; Speeckaert, Reinhart

    2017-04-01

    Segmental vitiligo is characterized by its early onset, rapid stabilization, and unilateral distribution. Recent evidence suggests that segmental and nonsegmental vitiligo could represent variants of the same disease spectrum. Observational studies with respect to its distribution pattern point to a possible role of cutaneous mosaicism, whereas the original stated dermatomal distribution seems to be a misnomer. Although the exact pathogenic mechanism behind the melanocyte destruction is still unknown, increasing evidence has been published on the autoimmune/inflammatory theory of segmental vitiligo. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Automatic Segmentation of the Corpus Callosum Using a Cell-Competition Algorithm: Diffusion Tensor Imaging-Based Evaluation of Callosal Atrophy and Tissue Alterations in Patients With Systemic Lupus Erythematosus.

    Science.gov (United States)

    Lee, Shiou-Ping; Wu, Chien-Sheng; Cheng, Jie-Zhi; Chen, Chung-Ming; Chen, Yu-Chiang; Chou, Ming-Chung

    2015-01-01

    Patients with neuropsychiatric systemic lupus erythematosus (NPSLE) may exhibit corpus callosal atrophy and tissue alterations. Measuring the callosal volume and tissue integrity using diffusion tensor imaging (DTI) could help to differentiate patients with NPSLE from patients without NPSLE. Hence, this study aimed to use an automatic cell-competition algorithm to segment the corpus callosum and to investigate the effects of central nervous system (CNS) involvement on the callosal volume and tissue integrity in patients with SLE. Twenty-two SLE patients with (N = 10, NPSLE) and without (N = 12, non-NPSLE) CNS involvement and 22 control subjects were enrolled in this study. For volumetric measurement, a cell-competition algorithm was used to automatically delineate corpus callosal boundaries based on a midsagittal fractional anisotropy (FA) map. After obtaining corpus callosal boundaries for all subjects, the volume, FA, and mean diffusivity (MD) of the corpus callosum were calculated. A post hoc Tamhane's T2 analysis was performed to statistically compare differences among NPSLE, non-NPSLE, and control subjects. A receiver operating characteristic curve analysis was also performed to compare the performance of the volume, FA, and MD of the corpus callosum in differentiating NPSLE from other subjects. Patients with NPSLE had significant decreases in volume and FA but an increase in MD in the corpus callosum compared with control subjects, whereas no significant difference was noted between patients without NPSLE and control subjects. The FA was found to have better performance in differentiating NPSLE from other subjects. A cell-competition algorithm could be used to automatically evaluate callosal atrophy and tissue alterations. Assessments of the corpus callosal volume and tissue integrity helped to demonstrate the effects of CNS involvement in patients with SLE.

  8. Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

    Science.gov (United States)

    Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

    2013-10-01

    Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

  9. Anatomy of lead poisoning

    African Journals Online (AJOL)

    ABEOLUGBENGAS

    Abstract. Objective: Lead poisoning and lead toxicity is usually often interchangeably used by different Scientists. The Anatomy of lead poisoning encompasses its effects on different organ-systems of different species of organisms. It also includes environmental, functional and biochemical components associated with most.

  10. The Anatomy Puzzle Book.

    Science.gov (United States)

    Jacob, Willis H.; Carter, Robert, III

    This document features review questions, crossword puzzles, and word search puzzles on human anatomy. Topics include: (1) Anatomical Terminology; (2) The Skeletal System and Joints; (3) The Muscular System; (4) The Nervous System; (5) The Eye and Ear; (6) The Circulatory System and Blood; (7) The Respiratory System; (8) The Urinary System; (9) The…

  11. Illustrated Speech Anatomy.

    Science.gov (United States)

    Shearer, William M.

    Written for students in the fields of speech correction and audiology, the text deals with the following: structures involved in respiration; the skeleton and the processes of inhalation and exhalation; phonation and pitch, the larynx, and esophageal speech; muscles involved in articulation; muscles involved in resonance; and the anatomy of the…

  12. Anatomy for Biomedical Engineers

    Science.gov (United States)

    Carmichael, Stephen W.; Robb, Richard A.

    2008-01-01

    There is a perceived need for anatomy instruction for graduate students enrolled in a biomedical engineering program. This appeared especially important for students interested in and using medical images. These students typically did not have a strong background in biology. The authors arranged for students to dissect regions of the body that…

  13. Anatomy of the Spine

    Science.gov (United States)

    ... Osteoporosis Back Pain Basics Book RESOURCES Patient Information Feature Articles Patient Q&A Success Stories Definitions Anatomy of the Spine Definitions A-Z Spine Specialists Videos 9 for Spine Epidural Steroid Injections Exercise: The Backbone of Spine Treatment ... Bones Vertebrae Each individual vertebra has unique features depending on the region in which it is ...

  14. Combining prior day contours to improve automated prostate segmentation

    International Nuclear Information System (INIS)

    Godley, Andrew; Sheplan Olsen, Lawrence J.; Stephans, Kevin; Zhao Anzi

    2013-01-01

    Purpose: To improve the accuracy of automatically segmented prostate, rectum, and bladder contours required for online adaptive therapy. The contouring accuracy on the current image guidance [image guided radiation therapy (IGRT)] scan is improved by combining contours from earlier IGRT scans via the simultaneous truth and performance level estimation (STAPLE) algorithm. Methods: Six IGRT prostate patients treated with daily kilo-voltage (kV) cone-beam CT (CBCT) had their original plan CT and nine CBCTs contoured by the same physician. Three types of automated contours were produced for analysis. (1) Plan: By deformably registering the plan CT to each CBCT and then using the resulting deformation field to morph the plan contours to match the CBCT anatomy. (2) Previous: The contour set drawn by the physician on the previous day CBCT is similarly deformed to match the current CBCT anatomy. (3) STAPLE: The contours drawn by the physician, on each prior CBCT and the plan CT, are deformed to match the CBCT anatomy to produce multiple contour sets. These sets are combined using the STAPLE algorithm into one optimal set. Results: Compared to plan and previous, STAPLE improved the average Dice's coefficient (DC) with the original physician drawn CBCT contours to a DC as follows: Bladder: 0.81 ± 0.13, 0.91 ± 0.06, and 0.92 ± 0.06; Prostate: 0.75 ± 0.08, 0.82 ± 0.05, and 0.84 ± 0.05; and Rectum: 0.79 ± 0.06, 0.81 ± 0.06, and 0.85 ± 0.04, respectively. The STAPLE results are within intraobserver consistency, determined by the physician blindly recontouring a subset of CBCTs. Comparing plans recalculated using the physician and STAPLE contours showed an average disagreement less than 1% for prostate D98 and mean dose, and 5% and 3% for bladder and rectum mean dose, respectively. One scan takes an average of 19 s to contour. Using five scans plus STAPLE takes less than 110 s on a 288 core graphics processor unit. Conclusions: Combining the plan and all prior days via

  15. Scorpion image segmentation system

    Science.gov (United States)

    Joseph, E.; Aibinu, A. M.; Sadiq, B. A.; Bello Salau, H.; Salami, M. J. E.

    2013-12-01

    Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two non-overlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.

  16. Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

    Science.gov (United States)

    Mansoor, Awais; Bagci, Ulas; Foster, Brent; Xu, Ziyue; Papadakis, Georgios Z; Folio, Les R; Udupa, Jayaram K; Mollura, Daniel J

    2015-01-01

    The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. ©RSNA, 2015.

  17. Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed.

    Directory of Open Access Journals (Sweden)

    Yu Huang

    Full Text Available Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS. The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG. The first step in generating such models is to obtain an accurate segmentation of individual head anatomy, including not only brain but also cerebrospinal fluid (CSF, skull and soft tissues, with a field of view (FOV that covers the whole head. Currently available automated segmentation tools only provide results for brain tissues, have a limited FOV, and do not guarantee continuity and smoothness of tissues, which is crucially important for accurate current-flow estimates. Here we present a tool that addresses these needs. It is based on a rigorous Bayesian inference framework that combines image intensity model, anatomical prior (atlas and morphological constraints using Markov random fields (MRF. The method is evaluated on 20 simulated and 8 real head volumes acquired with magnetic resonance imaging (MRI at 1 mm3 resolution. We find improved surface smoothness and continuity as compared to the segmentation algorithms currently implemented in Statistical Parametric Mapping (SPM. With this tool, accurate and morphologically correct modeling of the whole-head anatomy for individual subjects may now be feasible on a routine basis. Code and data are fully integrated into SPM software tool and are made publicly available. In addition, a review on the MRI segmentation using atlas and the MRF over the last 20 years is also provided, with the general mathematical framework clearly derived.

  18. Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed.

    Science.gov (United States)

    Huang, Yu; Parra, Lucas C

    2015-01-01

    Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS). The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). The first step in generating such models is to obtain an accurate segmentation of individual head anatomy, including not only brain but also cerebrospinal fluid (CSF), skull and soft tissues, with a field of view (FOV) that covers the whole head. Currently available automated segmentation tools only provide results for brain tissues, have a limited FOV, and do not guarantee continuity and smoothness of tissues, which is crucially important for accurate current-flow estimates. Here we present a tool that addresses these needs. It is based on a rigorous Bayesian inference framework that combines image intensity model, anatomical prior (atlas) and morphological constraints using Markov random fields (MRF). The method is evaluated on 20 simulated and 8 real head volumes acquired with magnetic resonance imaging (MRI) at 1 mm3 resolution. We find improved surface smoothness and continuity as compared to the segmentation algorithms currently implemented in Statistical Parametric Mapping (SPM). With this tool, accurate and morphologically correct modeling of the whole-head anatomy for individual subjects may now be feasible on a routine basis. Code and data are fully integrated into SPM software tool and are made publicly available. In addition, a review on the MRI segmentation using atlas and the MRF over the last 20 years is also provided, with the general mathematical framework clearly derived.

  19. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  20. Segmentation of Lung Structures in CT

    DEFF Research Database (Denmark)

    Lo, Pechin Chien Pau

    This thesis proposes and evaluates new algorithms for segmenting various lung structures in computed tomography (CT) images, namely the lungs, airway trees and vessel trees. The main objective of these algorithms is to facilitate a better platform for studying Chronic Obstructive Pulmonary Diseas....... Two approaches for extracting the airway tree using the voxel classification appearance model are proposed: a vessel guided approach and a locally optimal paths approach. The vessel guided approach exploits the fact that all airways are accompanied by arteries of similar orientation....... Segmented airway trees from the algorithms that participate in the study were used to construct the reference standard needed, circumventing the need for labour intensive manual segmentations. Each segmented trees is subdivided into its individual branch segments, where the branch segments are subjected...

  1. Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction.

    Science.gov (United States)

    Li, Rongjian; Zeng, Tao; Peng, Hanchuan; Ji, Shuiwang

    2017-07-01

    Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods, prior to applying tracing or reconstruction techniques. This preprocessing step is expected to remove noises in the data, thereby leading to improved reconstruction results. In this paper, we proposed to use 3-D convolutional neural networks (CNNs) for segmenting the neuronal microscopy images. Specifically, we designed a novel CNN architecture, that takes volumetric images as the inputs and their voxel-wise segmentation maps as the outputs. The developed architecture allows us to train and predict using large microscopy images in an end-to-end manner. We evaluated the performance of our model on a variety of challenging 3-D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.

  2. Hierarchical morphological segmentation for image sequence coding.

    Science.gov (United States)

    Salembier, P; Pardas, M

    1994-01-01

    This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of

  3. Segmentation of articular cartilage using active contours and prior knowledge.

    Science.gov (United States)

    Tejos, Cristian; Hall, Laurance; Cardenas-Blanco, Arturo

    2004-01-01

    A diffusion snake segmentation algorithm was evaluated on synthetic and real MR images of articular cartilage. The algorithm proved to be robust to missing boundaries and the initial contour converges over large distances. Compared with a standard B-spline snake, more accurate and reproducible segmentations were obtained, with less effort during initialization of the algorithm.

  4. Authenticity in Anatomy Art.

    Science.gov (United States)

    Adkins, Jessica

    2017-01-12

    The aim of this paper is to observe the evolution and evaluate the 'realness' and authenticity in Anatomy Art, an art form I define as one which incorporates accurate anatomical representations of the human body with artistic expression. I examine the art of 17th century wax anatomical models, the preservations of Frederik Ruysch, and Gunther von Hagens' Body Worlds plastinates, giving consideration to authenticity of both body and art. I give extra consideration to the works of Body Worlds since the exhibit creator believes he has created anatomical specimens with more educational value and bodily authenticity than ever before. Ultimately, I argue that von Hagens fails to offer Anatomy Art 'real human bodies,' and that the lack of bodily authenticity of his plastinates results in his creations being less pedagogic than he claims.

  5. Breast development and anatomy.

    Science.gov (United States)

    Pandya, Sonali; Moore, Richard G

    2011-03-01

    In this article, the development of the female breast, as well as the functional anatomy, blood supply, innervation and lymphatic drainage are described. A thorough understanding of the breast anatomy is an important adjunct to a meticulous clinical breast examination. Breast examination is a complex skill involving key maneuvers, including careful inspection and palpation. Clinical breast examination can provide an opportunity for the clinician to educate patients about their breast and about breast cancer, its symptoms, risk factors, early detection, and normal breast composition, and specifically variability. Clinical breast examination can help to detect some cancers not found by mammography, and clinicians should not override their examination findings if imaging is not supportive of the physical findings.

  6. Comparison of a gross anatomy laboratory to online anatomy software for teaching anatomy.

    Science.gov (United States)

    Mathiowetz, Virgil; Yu, Chih-Huang; Quake-Rapp, Cindee

    2016-01-01

    This study was designed to assess the grades, self-perceived learning, and satisfaction between occupational therapy students who used a gross anatomy laboratory versus online anatomy software (AnatomyTV) as tools to learn anatomy at a large public university and a satellite campus in the mid-western United States. The goal was to determine if equivalent learning outcomes could be achieved regardless of learning tool used. In addition, it was important to determine why students chose the gross anatomy laboratory over online AnatomyTV. A two group, post-test only design was used with data gathered at the end of the course. Primary outcomes were students' grades, self-perceived learning, and satisfaction. In addition, a survey was used to collect descriptive data. One cadaver prosection was available for every four students in the gross anatomy laboratory. AnatomyTV was available online through the university library. At the conclusion of the course, the gross anatomy laboratory group had significantly higher grade percentage, self-perceived learning, and satisfaction than the AnatomyTV group. However, the practical significance of the difference is debatable. The significantly greater time spent in gross anatomy laboratory during the laboratory portion of the course may have affected the study outcomes. In addition, some students may find the difference in (B+) versus (A-) grade as not practically significant. Further research needs to be conducted to identify what specific anatomy teaching resources are most effective beyond prosection for students without access to a gross anatomy laboratory. © 2015 American Association of Anatomists.

  7. Cerebellar anatomy as applied to cerebellar microsurgical resections

    Directory of Open Access Journals (Sweden)

    Alejandro Ramos

    2012-06-01

    Full Text Available OBJECTIVE: To define the anatomy of dentate nucleus and cerebellar peduncles, demonstrating the surgical application of anatomic landmarks in cerebellar resections. METHODS: Twenty cerebellar hemispheres were studied. RESULTS: The majority of dentate nucleus and cerebellar peduncles had demonstrated constant relationship to other cerebellar structures, which provided landmarks for surgical approaching. The lateral border is separated from the midline by 19.5 mm in both hemispheres. The posterior border of the cortex is separated 23.3 mm from the posterior segment of the dentate nucleus; the lateral one is separated 26 mm from the lateral border of the nucleus; and the posterior segment of the dentate nucleus is separated 25.4 mm from the posterolateral angle formed by the junction of lateral and posterior borders of cerebellar hemisphere. CONCLUSIONS: Microsurgical anatomy has provided important landmarks that could be applied to cerebellar surgical resections.

  8. TH-E-BRE-04: An Online Replanning Algorithm for VMAT

    International Nuclear Information System (INIS)

    Ahunbay, E; Li, X; Moreau, M

    2014-01-01

    Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filtered (FF) and flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT planning system ((Monaco, Elekta), enabling the output of detailed beam/machine parameters of original VMAT plans generated based on planning CTs for FF or FFF beams. A SAM algorithm, previously developed for fixed-beam IMRT, was modified to allow the algorithm to correct for interfractional variations (e.g., setup error, organ motion and deformation) by morphing apertures based on the geometric relationship between the beam's eye view of the anatomy from the planning CT and that from the daily CT for each control point. The algorithm was tested using daily CTs acquired using an in-room CT during daily IGRT for representative prostate cancer cases along with their planning CTs. The algorithm allows for restricted MLC leaf travel distance between control points of the VMAT delivery to prevent SAM from increasing leaf travel, and therefore treatment delivery time. Results: The VMAT plans adapted to the daily CT by SAM were found to improve the dosimetry relative to the IGRT repositioning plans for both FF and FFF beams. For the adaptive plans, the changes in leaf travel distance between control points were < 1cm for 80% of the control points with no restriction. When restricted to the original plans' maximum travel distance, the dosimetric effect was minimal. The adaptive plans were delivered successfully with similar delivery times as the original plans. The execution of the SAM algorithm was < 10 seconds. Conclusion: The SAM algorithm can quickly generate deliverable online-adaptive VMAT plans based on the anatomy of the day for both FF and FFF beams

  9. TH-E-BRE-04: An Online Replanning Algorithm for VMAT

    Energy Technology Data Exchange (ETDEWEB)

    Ahunbay, E; Li, X [Medical College of Wisconsin, Milwaukee, WI (United States); Moreau, M [Elekta, Inc, Verona, WI (Italy)

    2014-06-15

    Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filtered (FF) and flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT planning system ((Monaco, Elekta), enabling the output of detailed beam/machine parameters of original VMAT plans generated based on planning CTs for FF or FFF beams. A SAM algorithm, previously developed for fixed-beam IMRT, was modified to allow the algorithm to correct for interfractional variations (e.g., setup error, organ motion and deformation) by morphing apertures based on the geometric relationship between the beam's eye view of the anatomy from the planning CT and that from the daily CT for each control point. The algorithm was tested using daily CTs acquired using an in-room CT during daily IGRT for representative prostate cancer cases along with their planning CTs. The algorithm allows for restricted MLC leaf travel distance between control points of the VMAT delivery to prevent SAM from increasing leaf travel, and therefore treatment delivery time. Results: The VMAT plans adapted to the daily CT by SAM were found to improve the dosimetry relative to the IGRT repositioning plans for both FF and FFF beams. For the adaptive plans, the changes in leaf travel distance between control points were < 1cm for 80% of the control points with no restriction. When restricted to the original plans' maximum travel distance, the dosimetric effect was minimal. The adaptive plans were delivered successfully with similar delivery times as the original plans. The execution of the SAM algorithm was < 10 seconds. Conclusion: The SAM algorithm can quickly generate deliverable online-adaptive VMAT plans based on the anatomy of the day for both FF and FFF beams.

  10. A new framework for interactive images segmentation

    International Nuclear Information System (INIS)

    Ashraf, M.; Sarim, M.; Shaikh, A.B.

    2017-01-01

    Image segmentation has become a widely studied research problem in image processing. There exist different graph based solutions for interactive image segmentation but the domain of image segmentation still needs persistent improvements. The segmentation quality of existing techniques generally depends on the manual input provided in beginning, therefore, these algorithms may not produce quality segmentation with initial seed labels provided by a novice user. In this work we investigated the use of cellular automata in image segmentation and proposed a new algorithm that follows a cellular automaton in label propagation. It incorporates both the pixel's local and global information in the segmentation process. We introduced the novel global constraints in automata evolution rules; hence proposed scheme of automata evolution is more effective than the automata based earlier evolution schemes. Global constraints are also effective in deceasing the sensitivity towards small changes made in manual input; therefore proposed approach is less dependent on label seed marks. It can produce the quality segmentation with modest user efforts. Segmentation results indicate that the proposed algorithm performs better than the earlier segmentation techniques. (author)

  11. Introduction to anatomy on Wikipedia.

    Science.gov (United States)

    Ledger, Thomas Stephen

    2017-09-01

    Wikipedia (www.wikipedia.com) is the largest encyclopaedia in existence. Of over five million English-language articles, about 6000 relate to Anatomy, which are viewed roughly 30 million times monthly. No work parallels the amount of attention, scope or interdisciplinary layout of Wikipedia, and it offers a unique opportunity to improve the anatomical literacy of the masses. Anatomy on Wikipedia is introduced from an editor's perspective. Article contributors, content, layout and accuracy are discussed, with a view to demystifying editing for anatomy professionals. A final request for edits or on-site feedback from anatomy professionals is made. © 2017 Anatomical Society.

  12. 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.

  13. Field Sampling from a Segmented Image

    CSIR Research Space (South Africa)

    Debba, Pravesh

    2008-06-01

    Full Text Available This paper presents a statistical method for deriving the optimal prospective field sampling scheme on a remote sensing image to represent different categories in the field. The iterated conditional modes algorithm (ICM) is used for segmentation...

  14. Who Is Repeating Anatomy? Trends in an Undergraduate Anatomy Course

    Science.gov (United States)

    Schutte, Audra F.

    2016-01-01

    Anatomy courses frequently serve as prerequisites or requirements for health sciences programs. Due to the challenging nature of anatomy, each semester there are students remediating the course (enrolled in the course for a second time), attempting to earn a grade competitive for admissions into a program of study. In this retrospective study,…

  15. Surgical anatomy of the profunda brachii artery | Pulei | Anatomy ...

    African Journals Online (AJOL)

    Knowledge of this unusual anatomy is important during brachial artery catheterization and harvesting of lateral arm flaps. One hundred and forty four arms from 72 cadavers of black Kenyans were dissected and examined for the origin and termination of PBA at the Department of Human Anatomy, University of Nairobi, ...

  16. Segmenting Trajectories by Movement States

    NARCIS (Netherlands)

    Buchin, M.; Kruckenberg, H.; Kölzsch, A.; Timpf, S.; Laube, P.

    2013-01-01

    Dividing movement trajectories according to different movement states of animals has become a challenge in movement ecology, as well as in algorithm development. In this study, we revisit and extend a framework for trajectory segmentation based on spatio-temporal criteria for this purpose. We adapt

  17. Efficient threshold for volumetric segmentation

    Science.gov (United States)

    Burdescu, Dumitru D.; Brezovan, Marius; Stanescu, Liana; Stoica Spahiu, Cosmin; Ebanca, Daniel

    2015-07-01

    Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

  18. Brain tissue segmentation using fuzzy clustering techniques.

    Science.gov (United States)

    Sucharitha, M; Geetha, K Parimala

    2015-01-01

    Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images. To classify MR brain image into three segments such as Grey Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Classification of brain into tissues helps to diagnose several diseases such as tumors, Alzheimer's disease, stroke, multiple sclerosis. An unsupervised clustering technique such as Fuzzy C-Means (FCM) algorithm has been widely used in segmenting the images. The spatial information is not fully utilized by the conventional clustering algorithm and hence it is not applicable for clustering a noisy image. We incorporate a method for image clustering called out as Reformulated Fuzzy Local information C-Means Clustering algorithm [RFLICM] which is a variant of traditional Clustering algorithm by considering both spatial and gray level information. In RFLICM, spatial distance is replaced by local coefficient of variation in a fuzzy manner. Experiments are conducted on brain images to validate the performance of the proposed technique in segmenting the medical images and the efficiency achieved in the presence of salt and pepper noise is 99.86%. Standard FCM, Fuzzy Local information C-means clustering algorithm [FLICM], Reformulated Fuzzy Local information C-means clustering algorithm [RFLICM] are compared to explore the accuracy of our proposed approach. Clustering results show that RFLICM segmentation method is appropriate for classifying tissues in brain MR image.

  19. A contrario line segment detection

    CERN Document Server

    von Gioi, Rafael Grompone

    2014-01-01

    The reliable detection of low-level image structures is an old and still challenging problem in computer vision. This?book leads a detailed tour through the LSD algorithm, a line segment detector designed to be fully automatic. Based on the a contrario framework, the algorithm works efficiently without the need of any parameter tuning. The design criteria are thoroughly explained and the algorithm's good and bad results are illustrated on real and synthetic images. The issues involved, as well as the strategies used, are common to many geometrical structure detection problems and some possible

  20. 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.

  1. Speech Segmentation Using Bayesian Autoregressive Changepoint Detector

    Directory of Open Access Journals (Sweden)

    P. Sovka

    1998-12-01

    Full Text Available This submission is devoted to the study of the Bayesian autoregressive changepoint detector (BCD and its use for speech segmentation. Results of the detector application to autoregressive signals as well as to real speech are given. BCD basic properties are described and discussed. The novel two-step algorithm consisting of cepstral analysis and BCD for automatic speech segmentation is suggested.

  2. Segmentation of sows in farrowing pens

    DEFF Research Database (Denmark)

    Tu, Gang Jun; Karstoft, Henrik; Pedersen, Lene Juul

    2014-01-01

    The correct segmentation of a foreground object in video recordings is an important task for many surveillance systems. The development of an effective and practical algorithm to segment sows in grayscale video recordings captured under commercial production conditions is described. The segmentat...... to detect and classify the sow behaviours in farrowing pens....

  3. FISICO: Fast Image SegmentatIon COrrection.

    Directory of Open Access Journals (Sweden)

    Waldo Valenzuela

    Full Text Available In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

  4. FISICO: Fast Image SegmentatIon COrrection.

    Science.gov (United States)

    Valenzuela, Waldo; Ferguson, Stephen J; Ignasiak, Dominika; Diserens, Gaëlle; Häni, Levin; Wiest, Roland; Vermathen, Peter; Boesch, Chris; Reyes, Mauricio

    2016-01-01

    In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis. We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images. Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

  5. Health Instruction Packages: Cardiac Anatomy.

    Science.gov (United States)

    Phillips, Gwen; And Others

    Text, illustrations, and exercises are utilized in these five learning modules to instruct nurses, students, and other health care professionals in cardiac anatomy and functions and in fundamental electrocardiographic techniques. The first module, "Cardiac Anatomy and Physiology: A Review" by Gwen Phillips, teaches the learner to draw…

  6. Archives: Anatomy Journal of Africa

    African Journals Online (AJOL)

    Items 1 - 13 of 13 ... Archives: Anatomy Journal of Africa. Journal Home > Archives: Anatomy Journal of Africa. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register · Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives. 1 - 13 of 13 Items. 2017 ...

  7. CONTRIBUTIONS OF SUSHRUTA TO ANATOMY

    African Journals Online (AJOL)

    2005-08-08

    Aug 8, 2005 ... Probably, the exhaustive knowledge of basic sciences he had would have made him a versatile surgeon. This article has compiled the contributions of this great stalwart to anatomy and interprets his perspective towards teaching this subject. Keywords: Sushruta, Dissection, Cadaver, Anatomy, Preservation.

  8. The quail anatomy portal.

    Science.gov (United States)

    Ruparelia, Avnika A; Simkin, Johanna E; Salgado, David; Newgreen, Donald F; Martins, Gabriel G; Bryson-Richardson, Robert J

    2014-01-01

    The Japanese quail is a widely used model organism for the study of embryonic development; however, anatomical resources are lacking. The Quail Anatomy Portal (QAP) provides 22 detailed three-dimensional (3D) models of quail embryos during development from embryonic day (E)1 to E15 generated using optical projection tomography. The 3D models provided can be virtually sectioned to investigate anatomy. Furthermore, using the 3D nature of the models, we have generated a tool to assist in the staging of quail samples. Volume renderings of each stage are provided and can be rotated to allow visualization from multiple angles allowing easy comparison of features both between stages in the database and between images or samples in the laboratory. The use of JavaScript, PHP and HTML ensure the database is accessible to users across different operating systems, including mobile devices, facilitating its use in the laboratory.The QAP provides a unique resource for researchers using the quail model. The ability to virtually section anatomical models throughout development provides the opportunity for researchers to virtually dissect the quail and also provides a valuable tool for the education of students and researchers new to the field. DATABASE URL: http://quail.anatomyportal.org (For review username: demo, password: quail123).

  9. 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....

  10. MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

    Energy Technology Data Exchange (ETDEWEB)

    Zhen, X; Chen, H; Zhou, L [Southern Medical University, Guangzhou, Guangdong, 510515 (China); Yan, H; Jiang, S; Jia, X; Gu, X [Southwestern Medical Center, Dallas, TX (United States); Mell, L; Yashar, C; Cervino, L [University of California, San Diego, La Jolla, CA (United States)

    2014-06-15

    Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the random walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no

  11. Mixed segmentation

    DEFF Research Database (Denmark)

    Hansen, Allan Grutt; Bonde, Anders; Aagaard, Morten

    This book is about using recent developments in the fields of data analytics and data visualization to frame new ways of identifying target groups in media communication. Based on a mixed-methods approach, the authors combine psychophysiological monitoring (galvanic skin response) with textual...... content analysis and audience segmentation in a single-source perspective. The aim is to explain and understand target groups in relation to, on the one hand, emotional response to commercials or other forms of audio-visual communication and, on the other hand, living preferences and personality traits...

  12. Unsupervised Segmentation Methods of TV Contents

    Directory of Open Access Journals (Sweden)

    Elie El-Khoury

    2010-01-01

    Full Text Available We present a generic algorithm to address various temporal segmentation topics of audiovisual contents such as speaker diarization, shot, or program segmentation. Based on a GLR approach, involving the ΔBIC criterion, this algorithm requires the value of only a few parameters to produce segmentation results at a desired scale and on most typical low-level features used in the field of content-based indexing. Results obtained on various corpora are of the same quality level than the ones obtained by other dedicated and state-of-the-art methods.

  13. Adaptive Maneuvering Target Tracking Algorithm

    Directory of Open Access Journals (Sweden)

    Chunling Wu

    2014-07-01

    Full Text Available Based on the current statistical model, a new adaptive maneuvering target tracking algorithm, CS-MSTF, is presented. The new algorithm keep the merits of high tracking precision that the current statistical model and strong tracking filter (STF have in tracking maneuvering target, and made the modifications as such: First, STF has the defect that it achieves the perfect performance in maneuvering segment at a cost of the precision in non-maneuvering segment, so the new algorithm modified the prediction error covariance matrix and the fading factor to improve the tracking precision both of the maneuvering segment and non-maneuvering segment; The estimation error covariance matrix was calculated using the Joseph form, which is more stable and robust in numerical. The Monte- Carlo simulation shows that the CS-MSTF algorithm has a more excellent performance than CS-STF and can estimate efficiently.

  14. Anatomy Journal of Africa: Editorial Policies

    African Journals Online (AJOL)

    This journal has its editorial office based at the department of Human Anatomy, University of Nairobi, and has biannual issues (January and July issues). We accept and publish a wide variety of papers including: - Applied anatomy - Clinical anatomy - Morphology, - Embryology - Anatomical techniques and Variant anatomy.

  15. The Anatomy of Galaxies

    Science.gov (United States)

    D'Onofrio, Mauro; Rampazzo, Roberto; Zaggia, Simone; Longair, Malcolm S.; Ferrarese, Laura; Marziani, Paola; Sulentic, Jack W.; van der Kruit, Pieter C.; Laurikainen, Eija; Elmegreen, Debra M.; Combes, Françoise; Bertin, Giuseppe; Fabbiano, Giuseppina; Giovanelli, Riccardo; Calzetti, Daniela; Moss, David L.; Matteucci, Francesca; Djorgovski, Stanislav George; Fraix-Burnet, Didier; Graham, Alister W. McK.; Tully, Brent R.

    Just after WWII Astronomy started to live its "Golden Age", not differently to many other sciences and human activities, especially in the west side countries. The improved resolution of telescopes and the appearance of new efficient light detectors (e.g. CCDs in the middle eighty) greatly impacted the extragalactic researches. The first morphological analysis of galaxies were rapidly substituted by "anatomic" studies of their structural components, star and gas content, and in general by detailed investigations of their properties. As for the human anatomy, where the final goal was that of understanding the functionality of the organs that are essential for the life of the body, galaxies were dissected to discover their basic structural components and ultimately the mystery of their existence.

  16. Blended learning in anatomy

    DEFF Research Database (Denmark)

    Østergaard, Gert Værge; Brogner, Heidi Marie

    behind DBR is that new knowledge is generated through processes that simultaneously develop, test and improve a design, in this case, an educational design (1) The main principles used in the project is blended learning and flipped learning (2). …"I definitely learn best in practice, but the theory...... in working with the assignments in the classroom."... External assesor, observer and interviewer Based on the different evaluations, the conclusion are that the blended learning approach combined with the ‘flipped classroom’ is a very good way to learn and apply the anatomy, both for the students......The aim of the project was to bridge the gap between theory and practice by working more collaboratively, both peer-to-peer and between student and lecturer. Furthermore the aim was to create active learning environments. The methodology of the project is Design-Based Research (DBR). The idea...

  17. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

    Science.gov (United States)

    Raudaschl, Patrik F; Zaffino, Paolo; Sharp, Gregory C; Spadea, Maria Francesca; Chen, Antong; Dawant, Benoit M; Albrecht, Thomas; Gass, Tobias; Langguth, Christoph; Lüthi, Marcel; Jung, Florian; Knapp, Oliver; Wesarg, Stefan; Mannion-Haworth, Richard; Bowes, Mike; Ashman, Annaliese; Guillard, Gwenael; Brett, Alan; Vincent, Graham; Orbes-Arteaga, Mauricio; Cárdenas-Peña, David; Castellanos-Dominguez, German; Aghdasi, Nava; Li, Yangming; Berens, Angelique; Moe, Kris; Hannaford, Blake; Schubert, Rainer; Fritscher, Karl D

    2017-05-01

    Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms. © 2017 American Association of Physicists in Medicine.

  18. On the evaluation of segmentation editing tools

    Science.gov (United States)

    Heckel, Frank; Moltz, Jan H.; Meine, Hans; Geisler, Benjamin; Kießling, Andreas; D’Anastasi, Melvin; dos Santos, Daniel Pinto; Theruvath, Ashok Joseph; Hahn, Horst K.

    2014-01-01

    Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings. PMID:26158063

  19. VISUALIZATION OF REGISTERED SUBSURFACE ANATOMY

    DEFF Research Database (Denmark)

    2010-01-01

    A system and method for visualization of subsurface anatomy includes obtaining a first image from a first camera and a second image from a second camera or a second channel of the first camera, where the first and second images contain shared anatomical structures. The second camera and the second...... channel of the first camera are capable of imaging anatomy beneath the surface in ultra-violet, visual, or infra-red spectrum. A data processor is configured for computing registration of the first image to the second image to provide visualization of subsurface anatomy during surgical procedures...

  20. Editorial: Anatomy Journal Of Africa | Kramer | Anatomy Journal of ...

    African Journals Online (AJOL)

    Anatomy Journal of Africa. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 2, No 2 (2013) >. Log in or Register to get access to full text downloads.

  1. An augmented reality tool for learning spatial anatomy on mobile devices.

    Science.gov (United States)

    Jain, Nishant; Youngblood, Patricia; Hasel, Matthew; Srivastava, Sakti

    2017-09-01

    Augmented Realty (AR) offers a novel method of blending virtual and real anatomy for intuitive spatial learning. Our first aim in the study was to create a prototype AR tool for mobile devices. Our second aim was to complete a technical evaluation of our prototype AR tool focused on measuring the system's ability to accurately render digital content in the real world. We imported Computed Tomography (CT) data derived virtual surface models into a 3D Unity engine environment and implemented an AR algorithm to display these on mobile devices. We investigated the accuracy of the virtual renderings by comparing a physical cube with an identical virtual cube for dimensional accuracy. Our comparative study confirms that our AR tool renders 3D virtual objects with a high level of accuracy as evidenced by the degree of similarity between measurements of the dimensions of a virtual object (a cube) and the corresponding physical object. We developed an inexpensive and user-friendly prototype AR tool for mobile devices that creates highly accurate renderings. This prototype demonstrates an intuitive, portable, and integrated interface for spatial interaction with virtual anatomical specimens. Integrating this AR tool with a library of CT derived surface models provides a platform for spatial learning in the anatomy curriculum. The segmentation methodology implemented to optimize human CT data for mobile viewing can be extended to include anatomical variations and pathologies. The ability of this inexpensive educational platform to deliver a library of interactive, 3D models to students worldwide demonstrates its utility as a supplemental teaching tool that could greatly benefit anatomical instruction. Clin. Anat. 30:736-741, 2017. © 2017Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  2. Optimally segmented permanent magnet structures

    DEFF Research Database (Denmark)

    Insinga, Andrea Roberto; Bjørk, Rasmus; Smith, Anders

    2016-01-01

    We present an optimization approach which can be employed to calculate the globally optimal segmentation of a two-dimensional magnetic system into uniformly magnetized pieces. For each segment the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector......, with respect to a linear objective functional. We illustrate the approach with results for magnet design problems from different areas, such as a permanent magnet electric motor, a beam focusing quadrupole magnet for particle accelerators and a rotary device for magnetic refrigeration....

  3. WORD BASED TAMIL SPEECH RECOGNITION USING TEMPORAL FEATURE BASED SEGMENTATION

    Directory of Open Access Journals (Sweden)

    A. Akila

    2015-05-01

    Full Text Available Speech recognition system requires segmentation of speech waveform into fundamental acoustic units. Segmentation is a process of decomposing the speech signal into smaller units. Speech segmentation could be done using wavelet, fuzzy methods, Artificial Neural Networks and Hidden Markov Model. Speech segmentation is a process of breaking continuous stream of sound into some basic units like words, phonemes or syllable that could be recognized. Segmentation could be used to distinguish different types of audio signals from large amount of audio data, often referred as audio classification. The speech segmentation can be divided into two categories based on whether the algorithm uses previous knowledge of data to process the speech. The categories are blind segmentation and aided segmentation.The major issues with the connected speech recognition algorithms were the vocabulary size will be larger with variation in the combination of words in the connected speech and the complexity of the algorithm is more to find the best match for the given test pattern. To overcome these issues, the connected speech has to be segmented into words using the attributes of speech. A methodology using the temporal feature Short Term Energy was proposed and compared with an existing algorithm called Dynamic Thresholding segmentation algorithm which uses spectrogram image of the connected speech for segmentation.

  4. A Hybrid Technique for Medical Image Segmentation

    Directory of Open Access Journals (Sweden)

    Alamgir Nyma

    2012-01-01

    Full Text Available Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

  5. Prototype implementation of segment assembling software

    Directory of Open Access Journals (Sweden)

    Pešić Đorđe

    2018-01-01

    Full Text Available IT education is very important and a lot of effort is put into the development of tools for helping students to acquire programming knowledge and for helping teachers in automating the examination process. This paper describes a prototype of the program segment assembling software used in the context of making tests in the field of algorithmic complexity. The proposed new program segment assembling model uses rules and templates. A template is a simple program segment. A rule defines combining method and data dependencies if they exist. One example of program segment assembling by the proposed system is given. Graphical user interface is also described.

  6. VIDEO SEGMENTATION USING A NOVEL LBP DESCRIPTOR

    Directory of Open Access Journals (Sweden)

    Zhongkun He

    2014-08-01

    Full Text Available Video segmentation is the basis for content-based video retrieval, object recognition, object tracking, and video compression. This paper proposes a kind of novel and easy spatial-temporal LBP coding method, using the spatial-temporal 2 × 2 × 2 neighborhood clique to encode the changes in a video. Based on the coding method, a scheme of video segmentation is developed. Compared to the traditional segmentation method, its distinguished advantage is that it does not need to construct the background model and is simple in computation. Experimental results indicate that this new algorithm can give satisfying segmentation results.

  7. Semi-automated segment generation for geographic novelty ...

    African Journals Online (AJOL)

    Charles

    traverses the parameter space of the segmentation algorithm, searching for results most closely resembling the user-provided reference. Because segmentation is typically computationally expensive, population-based stochastic search methods, such as genetic algorithms, are recommended to produce usable results in a ...

  8. Computer Aided Segmentation Analysis: New Software for College Admissions Marketing.

    Science.gov (United States)

    Lay, Robert S.; Maguire, John J.

    1983-01-01

    Compares segmentation solutions obtained using a binary segmentation algorithm (THAID) and a new chi-square-based procedure (CHAID) that segments the prospective pool of college applicants using application and matriculation as criteria. Results showed a higher number of estimated qualified inquiries and more accurate estimates with CHAID. (JAC)

  9. Generative Anatomy Modeling Language (GAML).

    Science.gov (United States)

    Demirel, Doga; Yu, Alexander; Baer-Cooper, Seth; Halic, Tansel; Bayrak, Coskun

    2017-12-01

    This paper presents the Generative Anatomy Modeling Language (GAML) for generating variation of 3D virtual human anatomy in real-time. This framework provides a set of operators for modification of a reference base 3D anatomy. The perturbation of the 3D models is satisfied with nonlinear geometry constraints to create an authentic human anatomy. GAML was used to create 3D difficult anatomical scenarios for virtual simulation of airway management techniques such as Endotracheal Intubation (ETI) and Cricothyroidotomy (CCT). Difficult scenarios for each technique were defined and the model variations procedurally created with GAML. This study presents details of the GAML design, set of operators, types of constraints. Cases of CCT and ETI difficulty were generated and confirmed by expert surgeons. Execution performance pertaining to an increasing complexity of constraints using nonlinear programming was in real-time execution. Copyright © 2017 John Wiley & Sons, Ltd.

  10. Olfaction: anatomy, physiology and behavior

    OpenAIRE

    Benignus, Vernon A.; Prah, James D.

    1982-01-01

    The anatomy, physiology and function of the olfactory system are reviewed, as are the normal effects of olfactory stimulation. It is speculated that olfaction may have important but unobtrusive effects on human behavior.

  11. Superresolution improves MRI cortical segmentation with FACE

    DEFF Research Database (Denmark)

    Eskildsen, Simon Fristed; Manjón, José V.; Coupé, Pierrick

    Brain cortical surface extraction from MRI has applications for measurement of gray matter (GM) atrophy, functional mapping, source localization and preoperative neurosurgical planning. Accurate cortex segmentation requires high resolution morphological images and several methods for extracting...... the cerebral cortex have been developed during the last decade (Dale 1999, Kim 2005, Eskildsen 2006). In many studies, the resolution of the morphological image acquisition sequence is chosen to be relatively low (~1mm3) due to time and equipment constraints. To improve segmentation accuracy, such low...... the ability to effectively increase the image resolution while preserving sharp features of the underlying anatomy. In this study, we investigated the effect of applying superresolution as proposed in (Manjon 2010a) to the accuracy of cerebral cortex segmentation....

  12. Skeletal anatomy of the hand.

    Science.gov (United States)

    Panchal-Kildare, Surbhi; Malone, Kevin

    2013-11-01

    The skeletal anatomy of the hand is composed of phalanges, metacarpal bones, and carpal bones. Its function is a product of the complex interactions between the power provided by the intrinsic and extrinsic musculature, the stability provided by the ligaments, and the structure provided by the bones, which serve as insertion and attachment sites for the muscles and ligaments. This article provides a detailed description of the skeletal anatomy of the human hand. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Incorporating gross anatomy education into radiation oncology residency: a 2-year curriculum with evaluation of resident satisfaction.

    Science.gov (United States)

    Cabrera, Alvin R; Lee, W Robert; Madden, Richard; Sims, Ershela; Hoang, Jenny K; White, Leonard E; Marks, Lawrence B; Chino, Junzo P

    2011-05-01

    Radiation oncologists require a thorough understanding of anatomy, but gross anatomy is not part of the standard residency curriculum. "Oncoanatomy" is an educational program for radiation oncology residents at Duke University that integrates cadaver dissection into the instruction of oncologic anatomy, imaging, and treatment planning. In this report, the authors document their experience with a 2-year curriculum. Nineteen radiation oncology residents from Duke University and the University of North Carolina participated during academic years 2008-2009 and 2009-2010. Monthly modules, based on anatomic site, consisted of one or two clinically oriented hour-long lectures, followed by a 1-hour gross anatomy session. Clinical lectures were case based and focused on radiographic anatomy, image segmentation, and field design. Gross anatomy sessions centered on cadaver prosections, with small groups rotating through stations at which anatomists led cadaver exploration. Adjacent monitors featured radiologic imaging to facilitate synthesis of gross anatomy with imaging anatomy. Satisfaction was assessed on a 10-point scale via anonymous survey. Twenty modules were held over the 2-year period. Participants gave the course a median rating of 8 (interquartile range, 7-9), with 1 signifying "as effective as the worst educational activities" and 10 "as effective as the best educational activities." High resident satisfaction was seen with all module components. Incorporating a structured, 2-year gross anatomy-based curriculum into radiation oncology residency is feasible and associated with high resident satisfaction. Copyright © 2011 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  14. Optimization of Segmentation Quality of Integrated Circuit Images

    Directory of Open Access Journals (Sweden)

    Gintautas Mušketas

    2012-04-01

    Full Text Available The paper presents investigation into the application of genetic algorithms for the segmentation of the active regions of integrated circuit images. This article is dedicated to a theoretical examination of the applied methods (morphological dilation, erosion, hit-and-miss, threshold and describes genetic algorithms, image segmentation as optimization problem. The genetic optimization of the predefined filter sequence parameters is carried out. Improvement to segmentation accuracy using a non optimized filter sequence makes 6%.Artcile in Lithuanian

  15. Algorithming the Algorithm

    DEFF Research Database (Denmark)

    Mahnke, Martina; Uprichard, Emma

    2014-01-01

    changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...

  16. A Literature Review of Renal Surgical Anatomy and Surgical Strategies for Partial Nephrectomy.

    Science.gov (United States)

    Klatte, Tobias; Ficarra, Vincenzo; Gratzke, Christian; Kaouk, Jihad; Kutikov, Alexander; Macchi, Veronica; Mottrie, Alexandre; Porpiglia, Francesco; Porter, James; Rogers, Craig G; Russo, Paul; Thompson, R Houston; Uzzo, Robert G; Wood, Christopher G; Gill, Inderbir S

    2015-12-01

    A detailed understanding of renal surgical anatomy is necessary to optimize preoperative planning and operative technique and provide a basis for improved outcomes. To evaluate the literature regarding pertinent surgical anatomy of the kidney and related structures, nephrometry scoring systems, and current surgical strategies for partial nephrectomy (PN). A literature review was conducted. Surgical renal anatomy fundamentally impacts PN surgery. The renal artery divides into anterior and posterior divisions, from which approximately five segmental terminal arteries originate. The renal veins are not terminal. Variations in the vascular and lymphatic channels are common; thus, concurrent lymphadenectomy is not routinely indicated during PN for cT1 renal masses in the setting of clinically negative lymph nodes. Renal-protocol contrast-enhanced computed tomography or magnetic resonance imaging is used for standard imaging. Anatomy-based nephrometry scoring systems allow standardized academic reporting of tumor characteristics and predict PN outcomes (complications, remnant function, possibly histology). Anatomy-based novel surgical approaches may reduce ischemic time during PN; these include early unclamping, segmental clamping, tumor-specific clamping (zero ischemia), and unclamped PN. Cancer cure after PN relies on complete resection, which can be achieved by thin margins. Post-PN renal function is impacted by kidney quality, remnant quantity, and ischemia type and duration. Surgical renal anatomy underpins imaging, nephrometry scoring systems, and vascular control techniques that reduce global renal ischemia and may impact post-PN function. A contemporary ideal PN excises the tumor with a thin negative margin, delicately secures the tumor bed to maximize vascularized remnant parenchyma, and minimizes global ischemia to the renal remnant with minimal complications. In this report we review renal surgical anatomy. Renal mass imaging allows detailed delineation of the

  17. Pulmonary Lobe Segmentation with Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior

    Science.gov (United States)

    Bragman, Felix J.S.; McClelland, Jamie R.; Jacob, Joseph; Hurst, John R.; Hawkes, David J.

    2017-01-01

    A fully automated, unsupervised lobe segmentation algorithm is presented based on a probabilistic segmentation of the fissures and the simultaneous construction of a population model of the fissures. A two-class probabilistic segmentation segments the lung into candidate fissure voxels and the surrounding parenchyma. This was combined with anatomical information and a groupwise fissure prior to drive non-parametric surface fitting to obtain the final segmentation. The performance of our fissure segmentation was validated on 30 patients from the COPDGene cohort, achieving a high median F1-score of 0.90 and showed general insensitivity to filter parameters. We evaluated our lobe segmentation algorithm on the LOLA11 dataset, which contains 55 cases at varying levels of pathology. We achieved the highest score of 0.884 of the automated algorithms. Our method was further tested quantitatively and qualitatively on 80 patients from the COPDGene study at varying levels of functional impairment. Accurate segmentation of the lobes is shown at various degrees of fissure incompleteness for 96% of all cases. We also show the utility of including a groupwise prior in segmenting the lobes in regions of grossly incomplete fissures. PMID:28436850

  18. CDIS: Circle Density Based Iris Segmentation

    Science.gov (United States)

    Gupta, Anand; Kumari, Anita; Kundu, Boris; Agarwal, Isha

    Biometrics is an automated approach of measuring and analysing physical and behavioural characteristics for identity verification. The stability of the Iris texture makes it a robust biometric tool for security and authentication purposes. Reliable Segmentation of Iris is a necessary precondition as an error at this stage will propagate into later stages and requires proper segmentation of non-ideal images having noises like eyelashes, etc. Iris Segmentation work has been done earlier but we feel it lacks in detecting iris in low contrast images, removal of specular reflections, eyelids and eyelashes. Hence, it motivates us to enhance the said parameters. Thus, we advocate a new approach CDIS for Iris segmentation along with new algorithms for removal of eyelashes, eyelids and specular reflections and pupil segmentation. The results obtained have been presented using GAR vs. FAR graphs at the end and have been compared with prior works related to segmentation of iris.

  19. Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation.

    Science.gov (United States)

    Shekhar, Raj; Lei, Peng; Castro-Pareja, Carlos R; Plishker, William L; D'Souza, Warren D

    2007-07-01

    Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the

  20. RADIOGRAPHIC ABDOMINAL ANATOMY IN CAPTIVE RED PANDAS ( AILURUS FULGENS).

    Science.gov (United States)

    Makungu, Modesta; du Plessis, Wencke M; Barrows, Michelle; Groenewald, Hermanus B; Koeppel, Katja N

    2018-03-01

    The red panda ( Ailurus fulgens) is classified as an endangered species by the International Union for Conservation of Nature and Natural Resources. The aim of this study was to describe the normal radiographic abdominal anatomy in red pandas to provide guidance for clinical use. Radiography of the abdomen was performed in nine captive red pandas during their annual health examinations. Seven of nine animals had six lumbar vertebrae. The sacrum consisted mainly (8/9) of three fused segments. Hypaxial muscles were easily seen in animals weighing 5 kg and above. The pylorus was located to the right of the midline and cranially to the fundus in 8/9 individuals. Bunching of small intestine in the right central abdomen occurred in animals weighing 6 kg and above. The spleen was prominent. Knowledge of the normal radiographic abdominal anatomy of red pandas is important in the diagnosis of diseases and in routine health examinations.

  1. Penile Embryology and Anatomy

    Directory of Open Access Journals (Sweden)

    Jenny H. Yiee

    2010-01-01

    Full Text Available Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7thand 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile skin sensation and lie within Buck's fascia. These nerves are notably absent at the 12 o'clock position. Perineal nerves supply skin sensation to the ventral shaft skin and frenulum. Cavernosal nerves lie within the corpora cavernosa and are responsible for sexual function. Paired cavernosal, dorsal, and bulbourethral arteries have extensive anastomotic connections. During erection, the cavernosal artery causes engorgement of the cavernosa, while the deep dorsal artery leads to glans enlargement. The majority of venous drainage occurs through a single, deep dorsal vein into which multiple emissary veins from the corpora and circumflex veins from the spongiosum drain. The corpora cavernosa and spongiosum are all made of spongy erectile tissue. Buck's fascia circumferentially envelops all three structures, splitting into two leaves ventrally at the spongiosum. The male urethra is composed of six parts: bladder neck, prostatic, membranous, bulbous, penile, and fossa navicularis. The urethra receives its blood supply from both proximal and distal directions.

  2. 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.

  3. Papilian's anatomy - celebrating six decades.

    Science.gov (United States)

    Dumitraşcu, Dinu Iuliu; Crivii, Carmen Bianca; Opincariu, Iulian

    2017-01-01

    Victor Papilian was born an artist, during high school he studied music in order to become a violinist in two professional orchestras in Bucharest. Later on he enrolled in the school of medicine, being immediately attracted by anatomy. After graduating, with a briliant dissertation, he became a member of the faculty and continued to teach in his preferred field. His masters, Gh. Marinescu and Victor Babes, proposed him for the position of professor at the newly established Faculty of Medicine of Cluj. Here he reorganized the department radically, created an anatomy museum and edited the first dissection handbook and the first Romanian anatomy (descriptive and topographic) treatise, both books received with great appreciation. He received the Romanian Academy Prize. His knowledge and skills gained him a well deserved reputation and he created a prestigious school of anatomy. He published over 250 scientific papers in national and international journals, ranging from morphology to functional, pathological and anthropological topics. He founded the Society of Anthropology, with its own newsletter; he was elected as a member of the French Society of Anatomy. In parallel he had a rich artistic and cultural activity as writer and playwright: he was president of the Transylvanian Writers' Society, editor of a literary review, director of the Cluj theater and opera, leader of a book club and founder of a symphony orchestra.

  4. Trajectory Based Optimal Segment Computation in Road Network Databases

    DEFF Research Database (Denmark)

    Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.

    traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms...... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....

  5. Neurovascular anatomy: a practical guide.

    Science.gov (United States)

    Bell, Randy; Severson, Meryl A; Armonda, Rocco A

    2009-07-01

    Students of cerebrovascular anatomy and physiology tend to model their learning based on normal patterns of blood flow. As such, the focus tends toward arterial physiology and pathology with less than adequate understanding of the significance of the venous system. This article presents a different approach to neurovascular anatomy, starting with the venous system and demonstrating both normal and pathologic states. It reviews the cerebral circulation with attention to the microsurgical relationships, angiographic patterns, and fusion of dual-volume imaging. The importance of bony, sulcal, and ventricular anatomy is presented as it relates to the angiographic representation of pathologic lesions. Examples are given of anatomic variants seen with the operating microscope, biplanar angiography, and three-dimensional rotational angiography." Note that in the synopsis and throughout the article, first person usage has been changed to third person per journal style.

  6. Segmentation and segment connection of obstructed colon

    Science.gov (United States)

    Medved, Mario; Truyen, Roel; Likar, Bostjan; Pernus, Franjo

    2004-05-01

    Segmentation of colon CT images is the main factor that inhibits automation of virtual colonoscopy. There are two main reasons that make efficient colon segmentation difficult. First, besides the colon, the small bowel, lungs, and stomach are also gas-filled organs in the abdomen. Second, peristalsis or residual feces often obstruct the colon, so that it consists of multiple gas-filled segments. In virtual colonoscopy, it is very useful to automatically connect the centerlines of these segments into a single colon centerline. Unfortunately, in some cases this is a difficult task. In this study a novel method for automated colon segmentation and connection of colon segments' centerlines is proposed. The method successfully combines features of segments, such as centerline and thickness, with information on main colon segments. The results on twenty colon cases show that the method performs well in cases of small obstructions of the colon. Larger obstructions are mostly also resolved properly, especially if they do not appear in the sigmoid part of the colon. Obstructions in the sigmoid part of the colon sometimes cause improper classification of the small bowel segments. If a segment is too small, it is classified as the small bowel segment. However, such misclassifications have little impact on colon analysis.

  7. Anatomy of a Cancer Treatment Scam

    Medline Plus

    Full Text Available ... for Comment Report An Antitrust Violation File Documents in Adjudicative Proceedings You are here Home » News & Events » Audio/Video » Anatomy of a Cancer Treatment Scam Anatomy of a Cancer Treatment Scam ...

  8. Efficient segmentation of skin epidermis in whole slide histopathological images.

    Science.gov (United States)

    Xu, Hongming; Mandal, Mrinal

    2015-01-01

    Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.

  9. A Quantitative Comparison of Semantic Web Page Segmentation Approaches

    NARCIS (Netherlands)

    Kreuzer, Robert; Hage, J.; Feelders, A.J.

    2015-01-01

    We compare three known semantic web page segmentation algorithms, each serving as an example of a particular approach to the problem, and one self-developed algorithm, WebTerrain, that combines two of the approaches. We compare the performance of the four algorithms for a large benchmark of modern

  10. Pocket atlas of radiographic anatomy

    International Nuclear Information System (INIS)

    Moeller, T.B.; Reif, E.; Stark, P.

    1993-01-01

    The 'Pocket Atlas of Radiographic Anatomy' presents 170 radiographs of the various body regions of adults, showing only the normal radiographic anatomy. Each radiograph is supplemented on the opposite page by a drawing of the particular body region. There is no commenting text, but the drawings are provided with captions in English. The atlas is a useful guide for interpreting radiographs. The pictures are arranged in chapters entitled as follows: Skeletal Imaging (skull, spine, upper extremity), lower extremity; Miscellaneous Plain Films (chest, mammogram, trachea, lung tomograms); Contrast Examinations (gastrointestinal tract, intravenous contrast examinations, arthrography, angiography); Special Examinations (myelograms, lymphangiograms, bronchograms, sialograms). (UWA). 348 figs [de

  11. Surgical anatomy of the anterior supralabyrinthine air cell tract.

    Science.gov (United States)

    Gluth, M B; Cohen, M A; Friedland, P L; Atlas, M D

    2011-10-01

    In order to safely explore the medial wall of the attic, a working knowledge of the anatomy of the anterior supralabyrinthine air cell tract is required. To clarify the surgically relevant anatomical relationships that comprise the anterior supralabyrinthine air cell tract. Surgical dissection of 10 fresh cadaveric temporal bones was undertaken, including measurement of distances between the key anterior supralabyrinthine anatomical landmarks. The following mean distances were calculated: the labyrinthine segment between the geniculate ganglion and the ampullated end of the superior semicircular canal, 2.33 mm (range 1.75-2.75); the tympanic segment between the anterior margin of the oval window niche and the geniculate ganglion, 3.58 mm (range 3.25-4); and from the tympanic segment adjacent to the anterior margin of the oval window niche to the labyrinthine segment adjacent to the superior semicircular canal, 3.48 mm (range 3-4.25). The key anatomical landmarks of the anterior supralabyrinthine air cell tract define a distinct triangular segment of bone, knowledge of which is helpful in surgical dissection.

  12. Parallel fuzzy connected image segmentation on GPU.

    Science.gov (United States)

    Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

    2011-07-01

    Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

  13. Clinical anatomy research in a research-driven anatomy department.

    Science.gov (United States)

    Jones, D Gareth; Dias, G J; Mercer, S; Zhang, M; Nicholson, H D

    2002-05-01

    Clinical anatomy is too often viewed as a discipline that reiterates the wisdom of the past, characterized more by description of what is known than by active investigation and critical analysis of hypotheses and ideas. Various misconceptions follow from an acceptance of this premise: the teaching of clinical anatomists is textbook based, there is no clinical anatomy research worthy of the name, and any research that does exist fails to utilize modern technology and does not stand comparison with serious biomedical research as found in cell and molecular biology. The aim of this paper is to challenge each of these contentions by reference to ongoing clinical research studies within this department. It is argued that all teaching (including that of clinical anatomy) should be research-informed and that the discipline of clinical anatomy should have at its base a vigorous research ethos driven by clinically related problems. In interacting with physicians, the role of the clinical anatomist should be to promulgate a questioning scientific spirit, with its willingness to test and challenge accepted anatomic dicta. Copyright 2002 Wiley-Liss, Inc.

  14. 3D virtual table in anatomy education

    DEFF Research Database (Denmark)

    Dahl, Mads Ronald; Simonsen, Eivind Ortind

    The ‘Anatomage’ is a 3D virtual human anatomy table, with touchscreen functionality, where it is possible to upload CT-scans and digital. Learning the human anatomy terminology requires time, a very good memory, anatomy atlas, books and lectures. Learning the 3 dimensional structure, connections...... and intersections can be supported by technology like the Anatomage....

  15. Evaluation of manual and automatic segmentation of the mouse heart from CINE MR images

    NARCIS (Netherlands)

    Heijman, Edwin; Aben, Jean-Paul; Penners, Cindy; Niessen, Petra; Guillaume, René; van Eys, Guillaume; Nicolay, Klaas; Strijkers, Gustav J.

    2008-01-01

    To compare global functional parameters determined from a stack of cinematographic MR images of mouse heart by a manual segmentation and an automatic segmentation algorithm. The manual and automatic segmentation results of 22 mouse hearts were compared. The automatic segmentation was based on

  16. Segmentation of knee injury swelling on infrared images

    Science.gov (United States)

    Puentes, John; Langet, Hélène; Herry, Christophe; Frize, Monique

    2011-03-01

    Interpretation of medical infrared images is complex due to thermal noise, absence of texture, and small temperature differences in pathological zones. Acute inflammatory response is a characteristic symptom of some knee injuries like anterior cruciate ligament sprains, muscle or tendons strains, and meniscus tear. Whereas artificial coloring of the original grey level images may allow to visually assess the extent inflammation in the area, their automated segmentation remains a challenging problem. This paper presents a hybrid segmentation algorithm to evaluate the extent of inflammation after knee injury, in terms of temperature variations and surface shape. It is based on the intersection of rapid color segmentation and homogeneous region segmentation, to which a Laplacian of a Gaussian filter is applied. While rapid color segmentation enables to properly detect the observed core of swollen area, homogeneous region segmentation identifies possible inflammation zones, combining homogeneous grey level and hue area segmentation. The hybrid segmentation algorithm compares the potential inflammation regions partially detected by each method to identify overlapping areas. Noise filtering and edge segmentation are then applied to common zones in order to segment the swelling surfaces of the injury. Experimental results on images of a patient with anterior cruciate ligament sprain show the improved performance of the hybrid algorithm with respect to its separated components. The main contribution of this work is a meaningful automatic segmentation of abnormal skin temperature variations on infrared thermography images of knee injury swelling.

  17. Intrahepatic Vascular Anatomy in Rats and Mice--Variations and Surgical Implications.

    Directory of Open Access Journals (Sweden)

    Constanze Sänger

    Full Text Available The intra-hepatic vascular anatomy in rodents, its variations and corresponding supplying and draining territories in respect to the lobar structure of the liver have not been described. We performed a detailed anatomical imaging study in rats and mice to allow for further refinement of experimental surgical approaches.LEWIS-Rats and C57Bl/6N-Mice were subjected to ex-vivo imaging using μCT. The image data were used for semi-automated segmentation to extract the hepatic vascular tree as prerequisite for 3D visualization. The underlying vascular anatomy was reconstructed, analysed and used for determining hepatic vascular territories.The four major liver lobes have their own lobar portal supply and hepatic drainage territories. In contrast, the paracaval liver is supplied by various small branches from right and caudate portal veins and drains directly into the vena cava. Variations in hepatic vascular anatomy were observed in terms of branching pattern and distance of branches to each other. The portal vein anatomy is more variable than the hepatic vein anatomy. Surgically relevant variations were primarily observed in portal venous supply.For the first time the key variations of intrahepatic vascular anatomy in mice and rats and their surgical implications were described. We showed that lobar borders of the liver do not always match vascular territorial borders. These findings are of importance for the design of new surgical procedures and for understanding eventual complications following hepatic surgery.

  18. Improving image segmentation by learning region affinities

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.

    2010-11-03

    We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

  19. Bayesian automated cortical segmentation for neonatal MRI

    Science.gov (United States)

    Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

    2017-11-01

    Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

  20. Anatomy of the thymus gland.

    Science.gov (United States)

    Safieddine, Najib; Keshavjee, Shaf

    2011-05-01

    In the case of the thymus gland, the most common indications for resection are myasthenia gravis or thymoma. The consistency and appearance of the thymus gland make it difficult at times to discern from mediastinal fatty tissues. Having a clear understanding of the anatomy and the relationship of the gland to adjacent structures is important. Copyright © 2011 Elsevier Inc. All rights reserved.

  1. DAGAL: Detailed Anatomy of Galaxies

    Science.gov (United States)

    Knapen, Johan H.

    2017-03-01

    The current IAU Symposium is closely connected to the EU-funded network DAGAL (Detailed Anatomy of Galaxies), with the final annual network meeting of DAGAL being at the core of this international symposium. In this short paper, we give an overview of DAGAL, its training activities, and some of the scientific advances that have been made under its umbrella.

  2. Curriculum Guidelines for Microscopic Anatomy.

    Science.gov (United States)

    Journal of Dental Education, 1993

    1993-01-01

    The American Association of Dental Schools' guidelines for curricula in microscopic anatomy offer an overview of the histology curriculum, note primary educational goals, outline specific content for general and oral histology, suggest prerequisites, and make recommendations for sequencing. Appropriate faculty and facilities are also suggested.…

  3. Soul Anatomy: A virtual cadaver

    Directory of Open Access Journals (Sweden)

    Moaz Bambi

    2014-01-01

    Full Text Available In the traditional science of medicine and medical education, teaching human anatomy in the class has always been done using human cadavers. Not only does this violate human sanctity, but according to our research, it is not adequate to provide students with the alleged educational value that it is supposed to deliver. It is very cumbersome to organise all the aspects of cadaver care. Cadavers are also very limited when it comes to controlling their structures and any benefit is almost completely altered the first time the cadaver is used (dissected, and ironically, it is very weak at delivering actual real-life scenarios of a human body to students. Virtual anatomy has been a promising solution that many are counting on. But even today, we have not found a complete solution that combines all the benefits of using human cadavers and those introduced by its technical counterparts. "Soul Anatomy" aims to do just that. It brings the best of all worlds, from a natural intuitive control system, life-like feel of organs, precise accuracy in moving and controlling bodily structures, to the smallest details of being able to show medical information overlays from various medical databases connected to the internet; thus making use of technology in teaching human anatomy by providing a modern learning experience.

  4. Stem anatomy variation in cottonwood

    Science.gov (United States)

    A.N. Foulger; J. Hacskaylo

    1968-01-01

    Investigations of mineral nutrient-tree growth relationships have dealt mainly with associations involving foliage composition, root formation, or volume production of wood. Few studies have been concerned with changes in wood anatomy associated with element deficiency. In 1949 Davis reported that calcium deficiency was accompanied by a reduction of primary tissue and...

  5. Anatomy of the trigeminal nerve

    NARCIS (Netherlands)

    van Eijden, T.M.G.J.; Langenbach, G.E.J.; Baart, J.A.; Brand, H.S.

    2017-01-01

    The trigeminal nerve is the fifth cranial nerve (n. V), which plays an important role in the innervation of the head and neck area, together with other cranial and spinal nerves. Knowledge of the nerve’s anatomy is very important for the correct application of local anaesthetics.

  6. A diffusion tensor imaging tractography algorithm based on Navier-Stokes fluid mechanics.

    Science.gov (United States)

    Hageman, Nathan S; Toga, Arthur W; Narr, Katherine L; Shattuck, David W

    2009-03-01

    We introduce a fluid mechanics based tractography method for estimating the most likely connection paths between points in diffusion tensor imaging (DTI) volumes. We customize the Navier-Stokes equations to include information from the diffusion tensor and simulate an artificial fluid flow through the DTI image volume. We then estimate the most likely connection paths between points in the DTI volume using a metric derived from the fluid velocity vector field. We validate our algorithm using digital DTI phantoms based on a helical shape. Our method segmented the structure of the phantom with less distortion than was produced using implementations of heat-based partial differential equation (PDE) and streamline based methods. In addition, our method was able to successfully segment divergent and crossing fiber geometries, closely following the ideal path through a digital helical phantom in the presence of multiple crossing tracts. To assess the performance of our algorithm on anatomical data, we applied our method to DTI volumes from normal human subjects. Our method produced paths that were consistent with both known anatomy and directionally encoded color images of the DTI dataset.

  7. 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.

  8. Segmentation of large images based on super-pixels and community detection in graphs

    OpenAIRE

    Linares, Oscar A. C.; Botelho, Glenda Michele; Rodrigues, Francisco Aparecido; Neto, João Batista

    2016-01-01

    Image segmentation has many applications which range from machine learning to medical diagnosis. In this paper, we propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches, such as tho...

  9. Anatomy of Teaching Anatomy: Do Prosected Cross Sections Improve Students Understanding of Spatial and Radiological Anatomy?

    Directory of Open Access Journals (Sweden)

    L. B. Samarakoon

    2016-01-01

    Full Text Available Introduction. Cadaveric dissections and prosections have traditionally been part of undergraduate medical teaching. Materials and Methods. Hundred and fifty-nine first-year students in the Faculty of Medicine, University of Colombo, were invited to participate in the above study. Students were randomly allocated to two age and gender matched groups. Both groups were exposed to identical series of lectures regarding anatomy of the abdomen and conventional cadaveric prosections of the abdomen. The test group (n=77, 48.4% was also exposed to cadaveric cross-sectional slices of the abdomen to which the control group (n=82, 51.6% was blinded. At the end of the teaching session both groups were assessed by using their performance in a timed multiple choice question paper as well as ability to identify structures in abdominal CT films. Results. Scores for spatial and radiological anatomy were significantly higher among the test group when compared with the control group (P<0.05, CI 95%. Majority of the students in both control and test groups agreed that cadaveric cross section may be useful for them to understand spatial and radiological anatomy. Conclusion. Introduction of cadaveric cross-sectional prosections may help students to understand spatial and radiological anatomy better.

  10. Classic versus millennial medical lab anatomy.

    Science.gov (United States)

    Benninger, Brion; Matsler, Nik; Delamarter, Taylor

    2014-10-01

    This study investigated the integration, implementation, and use of cadaver dissection, hospital radiology modalities, surgical tools, and AV technology during a 12-week contemporary anatomy course suggesting a millennial laboratory. The teaching of anatomy has undergone the greatest fluctuation of any of the basic sciences during the past 100 years in order to make room for the meteoric rise in molecular sciences. Classically, anatomy consisted of a 2-year methodical, horizontal, anatomy course; anatomy has now morphed into a 12-week accelerated course in a vertical curriculum, at most institutions. Surface and radiological anatomy is the language for all clinicians regardless of specialty. The objective of this study was to investigate whether integration of full-body dissection anatomy and modern hospital technology, during the anatomy laboratory, could be accomplished in a 12-week anatomy course. Literature search was conducted on anatomy text, journals, and websites regarding contemporary hospital technology integrating multiple image mediums of 37 embalmed cadavers, surgical suite tools and technology, and audio/visual technology. Surgical and radiology professionals were contracted to teach during the anatomy laboratory. Literature search revealed no contemporary studies integrating full-body dissection with hospital technology and behavior. About 37 cadavers were successfully imaged with roentograms, CT, and MRI scans. Students were in favor of the dynamic laboratory consisting of multiple activity sessions occurring simultaneously. Objectively, examination scores proved to be a positive outcome and, subjectively, feedback from students was overwhelmingly positive. Despite the surging molecular based sciences consuming much of the curricula, full-body dissection anatomy is irreplaceable regarding both surface and architectural, radiological anatomy. Radiology should not be a small adjunct to understand full-body dissection, but rather, full-body dissection

  11. Dancers' Perceived and Actual Knowledge of Anatomy.

    Science.gov (United States)

    Kotler, Dana H; Lynch, Meaghan; Cushman, Daniel; Hu, Jason; Garner, Jocelyn

    2017-06-15

    Dancers are highly susceptible to musculoskeletal injuries and frequently require interaction with medical professionals. While many dancers have a finely tuned awareness of their bodies, their knowledge of the fundamentals of human anatomy is not uniform. There is a paucity of literature on the benefits of human anatomy education in dancers, though it seems intuitive that there should be a relationship. The purpose of this study was to assess dancers' perceived and actual knowledge of basic musculoskeletal anatomy and its relationship to function. Adult dancers at the undergraduate, pre-professional, and professional levels were surveyed through an anonymous online questionnaire. Questions included demographic information, dance techniques studied, anatomy training, and injury history. Subjects rated their perceived knowledge of anatomy and were tested with 15 multiple-choice questions on basic musculoskeletal anatomy. Four hundred seventy-five surveys were completed. Ordinal regression showed a correlation of perceived to actual knowledge of anatomy (p < 0.001). Factors that correlated with increases in both perceived and actual knowledge of anatomy included having taken an anatomy course of any type (p < 0.001) and increased age (p ≤ 0.001). Years of dance training and professional dancer status both significantly correlated with increased knowledge of anatomy (p < 0.001) but not perceived knowledge. Chi-square analysis showed that dancers with training in either modern or jazz dance had a significantly higher perceived, but not actual, knowledge when compared to those without training in those styles of dance (p < 0.001 and p = 0.011, respectively). In conclusion, dancers generally scored well on questions pertaining to basic musculoskeletal anatomy, and their perception correlated with their actual knowledge of anatomy. Factors that contribute to dancers' knowledge of anatomy include age, years of experience, professional dancer status, and anatomy training.

  12. A digital database of wrist bone anatomy and carpal kinematics.

    Science.gov (United States)

    Moore, Douglas C; Crisco, Joseph J; Trafton, Theodore G; Leventhal, Evan L

    2007-01-01

    The skeletal wrist consists of eight small, intricately shaped carpal bones. The motion of these bones is complex, occurs in three dimensions, and remains incompletely defined. Our previous efforts have been focused on determining the in vivo three-dimensional (3-D) kinematics of the normal and abnormal carpus. In so doing we have developed an extensive database of carpal bone anatomy and kinematics from a large number of healthy subjects. The purpose of this paper is to describe that database and to make it available to other researchers. CT volume images of both wrists from 30 healthy volunteers (15 males and 15 females) were acquired in multiple wrist positions throughout the normal range of wrist motion. The outer cortical surfaces of the carpal bones, radius and ulna, and proximal metacarpals were segmented and the 3-D motion of each bone was calculated for each wrist position. The database was constructed to include high-resolution surface models, measures of bone volume and shape, and the 3-D kinematics of each segmented bone. The database does not include soft tissues of the wrist. While there are numerous digital anatomical databases, this one is unique in that it includes a large number of subjects and it contains in vivo kinematic data as well as the bony anatomy.

  13. A comparative study on medical image segmentation methods

    Directory of Open Access Journals (Sweden)

    Praylin Selva Blessy SELVARAJ ASSLEY

    2014-03-01

    Full Text Available Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disadvantages in segmenting different medical images are discussed.

  14. [Segmentation Method for Liver Organ Based on Image Sequence Context].

    Science.gov (United States)

    Zhang, Meiyun; Fang, Bin; Wang, Yi; Zhong, Nanchang

    2015-10-01

    In view of the problems of more artificial interventions and segmentation defects in existing two-dimensional segmentation methods and abnormal liver segmentation errors in three-dimensional segmentation methods, this paper presents a semi-automatic liver organ segmentation method based on the image sequence context. The method takes advantage of the existing similarity between the image sequence contexts of the prior knowledge of liver organs, and combines region growing and level set method to carry out semi-automatic segmentation of livers, along with the aid of a small amount of manual intervention to deal with liver mutation situations. The experiment results showed that the liver segmentation algorithm presented in this paper had a high precision, and a good segmentation effect on livers which have greater variability, and can meet clinical application demands quite well.

  15. Reflection symmetry-integrated image segmentation.

    Science.gov (United States)

    Sun, Yu; Bhanu, Bir

    2012-09-01

    This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.

  16. Spinal cord grey matter segmentation challenge.

    Science.gov (United States)

    Prados, Ferran; Ashburner, John; Blaiotta, Claudia; Brosch, Tom; Carballido-Gamio, Julio; Cardoso, Manuel Jorge; Conrad, Benjamin N; Datta, Esha; Dávid, Gergely; Leener, Benjamin De; Dupont, Sara M; Freund, Patrick; Wheeler-Kingshott, Claudia A M Gandini; Grussu, Francesco; Henry, Roland; Landman, Bennett A; Ljungberg, Emil; Lyttle, Bailey; Ourselin, Sebastien; Papinutto, Nico; Saporito, Salvatore; Schlaeger, Regina; Smith, Seth A; Summers, Paul; Tam, Roger; Yiannakas, Marios C; Zhu, Alyssa; Cohen-Adad, Julien

    2017-05-15

    An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  17. -Means Based Fingerprint Segmentation with Sensor Interoperability

    Directory of Open Access Journals (Sweden)

    Yang Xiukun

    2010-01-01

    Full Text Available A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a -means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the -means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV. SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors.

  18. Edge Segment-Based Automatic Video Surveillance

    Directory of Open Access Journals (Sweden)

    Oksam Chae

    2007-12-01

    Full Text Available This paper presents a moving-object segmentation algorithm using edge information as segment. The proposed method is developed to address challenges due to variations in ambient lighting and background contents. We investigated the suitability of the proposed algorithm in comparison with the traditional-intensity-based as well as edge-pixel-based detection methods. In our method, edges are extracted from video frames and are represented as segments using an efficiently designed edge class. This representation helps to obtain the geometric information of edge in the case of edge matching and moving-object segmentation; and facilitates incorporating knowledge into edge segment during background modeling and motion tracking. An efficient approach for background initialization and robust method of edge matching is presented, to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object. Detected moving edges are utilized along with watershed algorithm for extracting video object plane (VOP with more accurate boundary. Experiment results with real image sequence reflect that the proposed method is suitable for automated video surveillance applications in various monitoring systems.

  19. Robustness of Spann-Wilson segmentation on SAR imagery

    Science.gov (United States)

    Daida, Jason M.; Vesecky, John F.

    1992-01-01

    The performances of the Spann-Wilson algorithm on simulated synthetic aperture radar (SAR) images with varying degrees of speckle (one to four looks and varying amounts of white noise) is described. One hundred forty-eight test images are considered, of which the algorithm segmented most without any adjustment to the algorithm's parameters. The effect of speckle on fractal boundaries is studied. The effect of varying multiplicative and additive noise distributions for a fixed set of segmentation parameters is examined. The modified Spann-Wilson algorithm on four-look imagery is evaluated.

  20. Dynamic Edge Tracing for 2D Image Segmentation

    National Research Council Canada - National Science Library

    Withey, D

    2001-01-01

    ...) image segmentation is presented. The technique utilizes a dynamic target tracking algorithm and a Kalman filter and permits edges to be followed in the presence of intensity variation similar to that found in MR images...

  1. Uncertainties in segmentation and their visualisation

    NARCIS (Netherlands)

    Lucieer, Arko

    2004-01-01

    This thesis focuses on uncertainties in remotely sensed image segmentation and their visualisation. The first part describes a visualisation tool, allowing interaction with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions of classes in a 3D feature

  2. Segmentation and Classification of Burn Color Images

    National Research Council Canada - National Science Library

    Acha, Begonya

    2001-01-01

    .... In the classification part, we take advantage of color information by clustering, with a vector quantization algorithm, the color centroids of small squares, taken from the burnt segmented part of the image, in the (V1, V2) plane into two possible groups, where V1 and V2 are the two chrominance components of the CIE Lab representation.

  3. Segmentation by watersheds : definition and parallel implementation

    NARCIS (Netherlands)

    Roerdink, Jos B.T.M.; Meijster, Arnold

    1997-01-01

    The watershed algorithm is a method for image segmentation widely used in the area of mathematical morphology. In this paper we first address the problem of how to define watersheds. It is pointed out that various existing definitions are not equivalent. In particular we explain the differences

  4. Simultaneous optimization of beam orientations, wedge filters and field weights for inverse planning with anatomy-based MLC fields

    International Nuclear Information System (INIS)

    Beaulieu, Frederic; Beaulieu, Luc; Tremblay, Daniel; Roy, Rene

    2004-01-01

    As an alternative between manual planning and beamlet-based IMRT, we have developed an optimization system for inverse planning with anatomy-based MLC fields. In this system, named Ballista, the orientation (table and gantry), the wedge filter and the field weights are simultaneously optimized for every beam. An interesting feature is that the system is coupled to Pinnacle3 by means of the PinnComm interface, and uses its convolution dose calculation engine. A fully automatic MLC segmentation algorithm is also included. The plan evaluation is based on a quasi-random sampling and on a quadratic objective function with penalty-like constraints. For efficiency, optimal wedge angles and wedge orientations are determined using the concept of the super-omni wedge. A bound-constrained quasi-Newton algorithm performs field weight optimization, while a fast simulated annealing algorithm selects the optimal beam orientations. Moreover, in order to generate directly deliverable plans, the following practical considerations have been incorporated in the system: collision between the gantry and the table as well as avoidance of the radio-opaque elements of a table top. We illustrate the performance of the new system on two patients. In a rhabdomyosarcoma case, the system generated plans improving both the target coverage and the sparing of the parotide, as compared to a manually designed plan. In the second case presented, the system successfully produced an adequate plan for the treatment of the prostate while avoiding both hip prostheses. For the many cases where full IMRT may not be necessary, the system efficiently generates satisfactory plans meeting the clinical objectives, while keeping the treatment verification much simpler

  5. Unsupervised tattoo segmentation combining bottom-up and top-down cues

    Science.gov (United States)

    Allen, Josef D.; Zhao, Nan; Yuan, Jiangbo; Liu, Xiuwen

    2011-06-01

    Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a figureground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is efficient and suitable for further tattoo classification and retrieval purpose.

  6. Novel block segmentation and processing for Chinese-English document

    Science.gov (United States)

    Chien, Bing-Shan; Jeng, Bor-Shenn; Sun, San-Wei; Chang, Gan-How; Shyu, Keh-Hwa; Shih, Chun-Hsi

    1991-11-01

    The block segmentation and block classification of digitized printed documents segmented into regions of texts, graphics, tables, and images are very important in automatic document analysis and understanding. Conventionally, the constrained run length algorithm (CRLA) has been proposed to segment digitized documents, however, it is space-consuming and time- consuming. The CRLA method must define some constrained parameters, so it cannot proceed automatically, and its performance may degrade significantly due to improper parameters. This paper proposes an efficient and effective method for document analysis, sequence connected segmentation and mapping matrix cell algorithm (SCSMMC). This method can analyze both simple and complex documents automatically and it need not define any constraint parameters. This method, which only needs one-reading image of document, can proceed completely and the techniques of segmentation, classification, labeling, and character segmentation proceed at the same time. The proposed document analysis method may also combine with the optical character recognizer to form an adaptive document understanding system.

  7. An Interactive Image Segmentation Method in Hand Gesture Recognition.

    Science.gov (United States)

    Chen, Disi; Li, Gongfa; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Tang, Heng; Ju, Zhaojie; Yu, Hui; Liu, Honghai

    2017-01-27

    In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

  8. Ultrasound biomicroscopy of the anterior segment.

    Science.gov (United States)

    Liebmann, J M; Ritch, R

    1996-08-01

    New imaging technologies are revolutionizing the understanding and treatment of a wide variety of ocular disorders. Confocal scanning laser ophthalmoscopy, ultrasound biomicroscopy, confocal scanning laser polarimetry, color doppler imaging of blood flow, and optical coherence tomography are providing important information regarding disease pathophysiology, diagnosis, progression, and treatment. High frequency (50 MHz), high resolution ultrasound biomicroscopy of the anterior segment was obtained in a wide variety of disorders of the anterior segment. Tissue resolution is approximately 50 microns and the penetration depth is 5 mm. Ultrasound biomicroscopy is capable of imaging the comea, iris, anterior chamber, anterior chamber angle, posterior chamber, and ciliary body with great detail. The structures surrounding the posterior chamber, previously hidden from clinical observation, can be imaged and their normal anatomic relationships assessed. The various forms of angle closure glaucoma, such as pupillary block and plateau iris configuration, can be differentiated. The concave iris found in pigment dispersion and its response to treatment can be assessed. Visualization of anterior segment anatomy in eyes with opaque media is possible. Ultrasound biomicroscopy assists in the management of eyes with disorders of the anterior segment. Future applications of this technology will yield important information regarding accommodation, normal ocular physiology and disease pathophysiology.

  9. Controlling the vocabulary for anatomy.

    Science.gov (United States)

    Baud, R H; Lovis, C; Rassinoux, A M; Ruch, P; Geissbuhler, A

    2002-01-01

    When confronted with the representation of human anatomy, natural language processing (NLP) system designers are facing an unsolved and frequent problem: the lack of a suitable global reference. The available sources in electronic format are numerous, but none fits adequately all the constraints and needs of language analysis. These sources are usually incomplete, difficult to use or tailored to specific needs. The anatomist's or ontologist's view does not necessarily match that of the linguist. The purpose of this paper is to review most recognized sources of knowledge in anatomy usable for linguistic analysis. Their potential and limits are emphasized according to this point of view. Focus is given on the role of the consensus work of the International Federation of Associations of Anatomists (IFAA) giving the Terminologia Anatomica.

  10. Segmented trapped vortex cavity

    Science.gov (United States)

    Grammel, Jr., Leonard Paul (Inventor); Pennekamp, David Lance (Inventor); Winslow, Jr., Ralph Henry (Inventor)

    2010-01-01

    An annular trapped vortex cavity assembly segment comprising includes a cavity forward wall, a cavity aft wall, and a cavity radially outer wall there between defining a cavity segment therein. A cavity opening extends between the forward and aft walls at a radially inner end of the assembly segment. Radially spaced apart pluralities of air injection first and second holes extend through the forward and aft walls respectively. The segment may include first and second expansion joint features at distal first and second ends respectively of the segment. The segment may include a forward subcomponent including the cavity forward wall attached to an aft subcomponent including the cavity aft wall. The forward and aft subcomponents include forward and aft portions of the cavity radially outer wall respectively. A ring of the segments may be circumferentially disposed about an axis to form an annular segmented vortex cavity assembly.

  11. Sipunculans and segmentation.

    Science.gov (United States)

    Wanninger, Andreas; Kristof, Alen; Brinkmann, Nora

    2009-01-01

    Comparative molecular, developmental and morphogenetic analyses show that the three major segmented animal groups-Lophotrochozoa, Ecdysozoa and Vertebrata-use a wide range of ontogenetic pathways to establish metameric body organization. Even in the life history of a single specimen, different mechanisms may act on the level of gene expression, cell proliferation, tissue differentiation and organ system formation in individual segments. Accordingly, in some polychaete annelids the first three pairs of segmental peripheral neurons arise synchronously, while the metameric commissures of the ventral nervous system form in anterior-posterior progression. Contrary to traditional belief, loss of segmentation may have occurred more often than commonly assumed, as exemplified in the sipunculans, which show remnants of segmentation in larval stages but are unsegmented as adults. The developmental plasticity and potential evolutionary lability of segmentation nourishes the controversy of a segmented bilaterian ancestor versus multiple independent evolution of segmentation in respective metazoan lineages.

  12. Anatomy and physiology of cisternostomy.

    Science.gov (United States)

    Cherian, Iype; Grasso, Giovanni; Bernardo, Antonio; Munakomi, Sunil

    2016-01-01

    Cisternostomy is defined as opening the basal cisterns to atmospheric pressure. This technique helps to reduce the intracranial pressure in severe head trauma as well as other conditions when the so-called sudden "brain swelling" troubles the surgeon. We elaborated the surgical anatomy of this procedure as well as the proposed physiology of how cisternostomy works. This novel technique may change the current trends in neurosurgery.

  13. Ecological anatomy of ferns fronds

    Directory of Open Access Journals (Sweden)

    Nina M. Derzhavina

    2014-04-01

    Full Text Available Structural types of frond anatomy are distinguished on the basis of investigation of 30 species of homosporous ferns and with regard for literature: hydromorphic, hygromorphic, mesomorphic, subxeromorphic, and subsucculent (cryptic succulent. Following frond traits are of highest adaptive value: their area and thickness, type of mesophyll, dry weight of an area unit – specific superficial density, cellular volume, and number of cells per unit of frond area.

  14. Postpartum Coccydynia: an Anatomy Overview

    OpenAIRE

    Maulana, Reza; Wahyuniati, Nur; Indra, Imai

    2015-01-01

    Coccydynia is a term that refers to a painful condition in and around the coccyx. This symptom is typically a discomfort or pain which is felt when sitting for long time and when rising from sitting position. Many physiologic and psychological factors contribute to its etiology, but the majority of cases were found to be aggravated by pregnancy and childbirth (postpartum). Luxation and fracture of the coccyx are the two most common lesion of postpartum coccydynia. This poster shows an anatomy...

  15. Magkänslans anatomi

    DEFF Research Database (Denmark)

    Ahlström, Kristoffer

    Varför dog 1500 personer i onödan i biltrafiken efter den 11 september 2001? Vad har FBI-agenter gemensamt med barn till alkoholister? Och vad fick författaren George Orwell att börja utöva svart magi? Magkänslans anatomi är en fascinerande kartläggning av de psykologiska mekanismer som ligger ba...

  16. Automatic lung segmentation in the presence of alveolar collapse

    Directory of Open Access Journals (Sweden)

    Noshadi Areg

    2017-09-01

    Full Text Available Lung ventilation and perfusion analyses using chest imaging methods require a correct segmentation of the lung to offer anatomical landmarks for the physiological data. An automatic segmentation approach simplifies and accelerates the analysis. However, the segmentation of the lungs has shown to be difficult if collapsed areas are present that tend to share similar gray values with surrounding non-pulmonary tissue. Our goal was to develop an automatic segmentation algorithm that is able to approximate dorsal lung boundaries even if alveolar collapse is present in the dependent lung areas adjacent to the pleura. Computed tomography data acquired in five supine pigs with injured lungs were used for this purpose. First, healthy lung tissue was segmented using a standard 3D region growing algorithm. Further, the bones in the chest wall surrounding the lungs were segmented to find the contact points of ribs and pleura. Artificial boundaries of the dorsal lung were set by spline interpolation through these contact points. Segmentation masks of the entire lung including the collapsed regions were created by combining the splines with the segmentation masks of the healthy lung tissue through multiple morphological operations. The automatically segmented images were then evaluated by comparing them to manual segmentations and determining the Dice similarity coefficients (DSC as a similarity measure. The developed method was able to accurately segment the lungs including the collapsed regions (DSCs over 0.96.

  17. A Fully Automated Penumbra Segmentation Tool

    DEFF Research Database (Denmark)

    Nagenthiraja, Kartheeban; Ribe, Lars Riisgaard; Hougaard, Kristina Dupont

    2012-01-01

    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. Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique

    Directory of Open Access Journals (Sweden)

    Youssef El Merabet

    2015-02-01

    Full Text Available In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc. affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM, 84% with mean shift, 82% with color structure code (CSC, 80% with efficient graph-based segmentation algorithm (EGBIS and 71% with JSEG.

  19. Building roof segmentation from aerial images using a lineand region-based watershed segmentation technique.

    Science.gov (United States)

    El Merabet, Youssef; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

    2015-02-02

    In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG.

  20. [Image post-processing, part 1: visualization and segmentation].

    Science.gov (United States)

    Baumann, T; Langer, M

    2013-09-01

    Image post-processing of large thin-slice radiological datasets relies on increasingly diverse and complex algorithms. Basic techniques of visualization, segmentation and data analysis will be presented in this article focusing on methods which are integrated into the majority of current viewing and reporting tools, such as multiplanar reformation, volume rendering or basic segmentation. Subsequently, more complex methods and a possible role of post-processing algorithms in the radiology of the future will be discussed.

  1. Color Segmentation of Homogeneous Areas on Colposcopical Images

    Directory of Open Access Journals (Sweden)

    Kosteley Yana

    2016-01-01

    Full Text Available The article provides an analysis of image processing and color segmentation applied to the problem of selection of homogeneous regions in the parameters of the color model. Methods of image processing such as Gaussian filter, median filter, histogram equalization and mathematical morphology are considered. The segmentation algorithm with the parameters of color components is presented, followed by isolation of the resulting connected component of a binary segmentation mask. Analysis of methods performed on images colposcopic research.

  2. Accent phrase segmentation using transition probabilities between pitch pattern templates.

    OpenAIRE

    Shimodaira, Hiroshi; Nakai, Mitsuru

    1993-01-01

    This paper proposes a novel method for segmenting continuous speech into accent phrases by using a prosodic feature 'pitch pattern'. The pitch pattern extracted from input speech signals is divided into the accent segments automatically by using the One-Stage DP algorithm, in which reference templates representing various types of accent patterns and connectivity between them are used to find out the optimum sequence of accent segments. In case of making the reference templates from a large n...

  3. Watershed regions and watershed lines based cooperation strategy for image segmentation. Application to roof detection

    OpenAIRE

    EL MERABET, Youssef; MEURIE, Cyril; RUICHEK, Yassine; SBIHI, Abderrahmane; TOUAHNI, Raja

    2011-01-01

    In this paper, we present a strategy of image segmentation for roof detection from aerial images. For that, an orthophotoplan segmentation method based on a cooperation technique including edge based segmentation and region based segmentation is proposed. Both segmentation techniques are assured by watershed algorithm. The proposed strategy is composed of three steps: (i) A simplification step that consists in simplifying the image with an appropriate couple of invariant/gradient optimized fo...

  4. Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging

    NARCIS (Netherlands)

    Anbeek, Petronella; Vincken, Koen L.; Groenendaal, Floris; Koeman, Annemieke; Van Osch, Matthias J. P.; Van der Grond, Jeroen

    A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and

  5. Heuristically improved Bayesian segmentation of brain MR images ...

    African Journals Online (AJOL)

    Heuristically improved Bayesian segmentation of brain MR images. ... or even the most prevalent task in medical image processing is image segmentation. Among them, brain MR images suffer ... show that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating ...

  6. Segmenting Student Markets with a Student Satisfaction and Priorities Survey.

    Science.gov (United States)

    Borden, Victor M. H.

    1995-01-01

    A market segmentation analysis of 872 university students compared 2 hierarchical clustering procedures for deriving market segments: 1 using matching-type measures and an agglomerative clustering algorithm, and 1 using the chi-square based automatic interaction detection. Results and implications for planning, evaluating, and improving academic…

  7. heuristically improved bayesian segmentation of brain mr images

    African Journals Online (AJOL)

    Aging Using ANN Based MR Brain Image Segmentation. Proceedings of the International Conference on Frontiers of. Intelligent Computing: Theory and Applications (FICTA) 2013,. Springer. Wang, J., J. Kong, et al. (2008). "A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial.

  8. Prostate MR image segmentation using 3D active appearance models

    NARCIS (Netherlands)

    Maan, Bianca; van der Heijden, Ferdinand

    2012-01-01

    This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain

  9. Automatic Melody Segmentation

    NARCIS (Netherlands)

    Rodríguez López, Marcelo

    2016-01-01

    The work presented in this dissertation investigates music segmentation. In the field of Musicology, segmentation refers to a score analysis technique, whereby notated pieces or passages of these pieces are divided into “units” referred to as sections, periods, phrases, and so on. Segmentation

  10. Image segmentation using information bottleneck method.

    Science.gov (United States)

    Bardera, Anton; Rigau, Jaume; Boada, Imma; Feixas, Miquel; Sbert, Mateu

    2009-07-01

    In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.

  11. Algorithm for programming function generators

    International Nuclear Information System (INIS)

    Bozoki, E.

    1981-01-01

    The present paper deals with a mathematical problem, encountered when driving a fully programmable μ-processor controlled function generator. An algorithm is presented to approximate a desired function by a set of straight segments in such a way that additional restrictions (hardware imposed) are also satisfied. A computer program which incorporates this algorithm and automatically generates the necessary input for the function generator for a broad class of desired functions is also described

  12. 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.)

  13. Variational mesh segmentation via quadric surface fitting

    KAUST Repository

    Yan, Dongming

    2012-11-01

    We present a new variational method for mesh segmentation by fitting quadric surfaces. Each component of the resulting segmentation is represented by a general quadric surface (including plane as a special case). A novel energy function is defined to evaluate the quality of the segmentation, which combines both L2 and L2 ,1 metrics from a triangle to a quadric surface. The Lloyd iteration is used to minimize the energy function, which repeatedly interleaves between mesh partition and quadric surface fitting. We also integrate feature-based and simplification-based techniques in the segmentation framework, which greatly improve the performance. The advantages of our algorithm are demonstrated by comparing with the state-of-the-art methods. © 2012 Elsevier Ltd. All rights reserved.

  14. Segment LLL Reduction of Lattice Bases Using Modular Arithmetic

    Directory of Open Access Journals (Sweden)

    Sanjay Mehrotra

    2010-07-01

    Full Text Available The algorithm of Lenstra, Lenstra, and Lovász (LLL transforms a given integer lattice basis into a reduced basis. Storjohann improved the worst case complexity of LLL algorithms by a factor of O(n using modular arithmetic. Koy and Schnorr developed a segment-LLL basis reduction algorithm that generates lattice basis satisfying a weaker condition than the LLL reduced basis with O(n improvement than the LLL algorithm. In this paper we combine Storjohann’s modular arithmetic approach with the segment-LLL approach to further improve the worst case complexity of the segment-LLL algorithms by a factor of n0.5.

  15. Colour application on mammography image segmentation

    Science.gov (United States)

    Embong, R.; Aziz, N. M. Nik Ab.; Karim, A. H. Abd; Ibrahim, M. R.

    2017-09-01

    The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).

  16. Brachial Plexus Anatomy: Normal and Variant

    Directory of Open Access Journals (Sweden)

    Steven L. Orebaugh

    2009-01-01

    Full Text Available Effective brachial plexus blockade requires a thorough understanding of the anatomy of the plexus, as well as an appreciation of anatomic variations that may occur. This review summarizes relevant anatomy of the plexus, along with variations and anomalies that may affect nerve blocks conducted at these levels. The Medline, Cochrane Library, and PubMed electronic databases were searched in order to compile reports related to the anatomy of the brachial plexus using the following free terms: "brachial plexus", "median nerve", "ulnar nerve", "radial nerve", "axillary nerve", and "musculocutanous nerve". Each of these was then paired with the MESH terms "anatomy", "nerve block", "anomaly", "variation", and "ultrasound". Resulting articles were hand searched for additional relevant literature. A total of 68 searches were conducted, with a total of 377 possible articles for inclusion. Of these, 57 were found to provide substantive information for this review. The normal anatomy of the brachial plexus is briefly reviewed, with an emphasis on those features revealed by use of imaging technologies. Anomalies of the anatomy that might affect the conduct of the various brachial plexus blocks are noted. Brachial plexus blockade has been effectively utilized as a component of anesthesia for upper extremity surgery for a century. Over that period, our understanding of anatomy and its variations has improved significantly. The ability to explore anatomy at the bedside, with real-time ultrasonography, has improved our appreciation of brachial plexus anatomy as well.

  17. MR Imaging of Prostate Zonal Anatomy.

    Science.gov (United States)

    Yacoub, Joseph H; Oto, Aytekin

    2018-03-01

    McNeal first described the zonal anatomy of the prostate about 40 years ago, outlining 4 zones of the prostate and defining their relation to the urethra and the ejaculatory ducts. The zonal anatomy remains the accepted model for describing the prostate and the zones are well-depicted on MR imaging, including the central zone, which until recently was grouped with the transition zone in the radiology literature. An accurate understanding of the zonal anatomy and periprostatic anatomy is key for accurate interpretation of the prostate MR imaging. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Algorithmic cryptanalysis

    CERN Document Server

    Joux, Antoine

    2009-01-01

    Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic

  19. Automatic segmentation of clinical texts.

    Science.gov (United States)

    Apostolova, Emilia; Channin, David S; Demner-Fushman, Dina; Furst, Jacob; Lytinen, Steven; Raicu, Daniela

    2009-01-01

    Clinical narratives, such as radiology and pathology reports, are commonly available in electronic form. However, they are also commonly entered and stored as free text. Knowledge of the structure of clinical narratives is necessary for enhancing the productivity of healthcare departments and facilitating research. This study attempts to automatically segment medical reports into semantic sections. Our goal is to develop a robust and scalable medical report segmentation system requiring minimum user input for efficient retrieval and extraction of information from free-text clinical narratives. Hand-crafted rules were used to automatically identify a high-confidence training set. This automatically created training dataset was later used to develop metrics and an algorithm that determines the semantic structure of the medical reports. A word-vector cosine similarity metric combined with several heuristics was used to classify each report sentence into one of several pre-defined semantic sections. This baseline algorithm achieved 79% accuracy. A Support Vector Machine (SVM) classifier trained on additional formatting and contextual features was able to achieve 90% accuracy. Plans for future work include developing a configurable system that could accommodate various medical report formatting and content standards.

  20. New algorithms for binary wavefront optimization

    Science.gov (United States)

    Zhang, Xiaolong; Kner, Peter

    2015-03-01

    Binary amplitude modulation promises to allow rapid focusing through strongly scattering media with a large number of segments due to the faster update rates of digital micromirror devices (DMDs) compared to spatial light modulators (SLMs). While binary amplitude modulation has a lower theoretical enhancement than phase modulation, the faster update rate should more than compensate for the difference - a factor of π2 /2. Here we present two new algorithms, a genetic algorithm and a transmission matrix algorithm, for optimizing the focus with binary amplitude modulation that achieve enhancements close to the theoretical maximum. Genetic algorithms have been shown to work well in noisy environments and we show that the genetic algorithm performs better than a stepwise algorithm. Transmission matrix algorithms allow complete characterization and control of the medium but require phase control either at the input or output. Here we introduce a transmission matrix algorithm that works with only binary amplitude control and intensity measurements. We apply these algorithms to binary amplitude modulation using a Texas Instruments Digital Micromirror Device. Here we report an enhancement of 152 with 1536 segments (9.90%×N) using a genetic algorithm with binary amplitude modulation and an enhancement of 136 with 1536 segments (8.9%×N) using an intensity-only transmission matrix algorithm.

  1. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

    Science.gov (United States)

    Lavdas, Ioannis; Glocker, Ben; Kamnitsas, Konstantinos; Rueckert, Daniel; Mair, Henrietta; Sandhu, Amandeep; Taylor, Stuart A; Aboagye, Eric O; Rockall, Andrea G

    2017-10-01

    As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance

  2. Algorithmic mathematics

    CERN Document Server

    Hougardy, Stefan

    2016-01-01

    Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.

  3. Cache-Oblivious Red-Blue Line Segment Intersection

    DEFF Research Database (Denmark)

    Arge, Lars; Mølhave, Thomas; Zeh, Norbert

    2008-01-01

    We present an optimal cache-oblivious algorithm for finding all intersections between a set of non-intersecting red segments and a set of non-intersecting blue segments in the plane. Our algorithm uses $O(\\frac{N}{B}\\log_{M/B}\\frac{N}{B}+T/B)$ memory transfers, where N is the total number...... of segments, M and B are the memory and block transfer sizes of any two consecutive levels of any multilevel memory hierarchy, and T is the number of intersections....

  4. Refining image segmentation by integration of edge and region data

    Science.gov (United States)

    Le Moigne, Jacqueline; Tilton, James C.

    1992-01-01

    An iterative parallel region growing (IPRG) algorithm previously developed by Tilton (1989) produces hierarchical segmentations of images from finer to coarser resolution. An ideal segmentation does not always correspond to one single iteration but to several different ones, each one producing the 'best' result for a separate part of the image. With the goal of finding this ideal segmentation, the results of the IPRG algorithm are refined by utilizing some additional information, such as edge features, and by interpreting the tree of hierarchical regions.

  5. Joint model of motion and anatomy for PET image reconstruction

    International Nuclear Information System (INIS)

    Qiao Feng; Pan Tinsu; Clark, John W. Jr.; Mawlawi, Osama

    2007-01-01

    Anatomy-based positron emission tomography (PET) image enhancement techniques have been shown to have the potential for improving PET image quality. However, these techniques assume an accurate alignment between the anatomical and the functional images, which is not always valid when imaging the chest due to respiratory motion. In this article, we present a joint model of both motion and anatomical information by integrating a motion-incorporated PET imaging system model with an anatomy-based maximum a posteriori image reconstruction algorithm. The mismatched anatomical information due to motion can thus be effectively utilized through this joint model. A computer simulation and a phantom study were conducted to assess the efficacy of the joint model, whereby motion and anatomical information were either modeled separately or combined. The reconstructed images in each case were compared to corresponding reference images obtained using a quadratic image prior based maximum a posteriori reconstruction algorithm for quantitative accuracy. Results of these studies indicated that while modeling anatomical information or motion alone improved the PET image quantitation accuracy, a larger improvement in accuracy was achieved when using the joint model. In the computer simulation study and using similar image noise levels, the improvement in quantitation accuracy compared to the reference images was 5.3% and 19.8% when using anatomical or motion information alone, respectively, and 35.5% when using the joint model. In the phantom study, these results were 5.6%, 5.8%, and 19.8%, respectively. These results suggest that motion compensation is important in order to effectively utilize anatomical information in chest imaging using PET. The joint motion-anatomy model presented in this paper provides a promising solution to this problem

  6. Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

    Directory of Open Access Journals (Sweden)

    Christoff Fourie

    2014-11-01

    Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

  7. Anatomy of the Corrugator Muscle.

    Science.gov (United States)

    Hwang, Kun; Lee, Jung Hun; Lim, Hee Joong

    2017-03-01

    The aim of this article is to systematically review the anatomy and action of the corrugator muscle. PubMed and Scopus were searched using the terms "corrugator" AND "anatomy." Among the 60 full texts from the 145 relevant abstracts, 34 articles without sufficient content were excluded and 4 articles drawn from the reference lists were added. Among the 30 articles analyzed (721 hemifaces), 28% classified by oblique head and transverse head, and 72% did not. Corrugator originated mostly from the medial supraorbital rim (45%), followed by the medial frontal bone (31%), the medial infraorbital rim (17%), and the upper nasal process (7%). Corrugator extended through the frontalis and orbicularis oculi (41%), only the frontalis (41%), or only the orbicularis oculi (18%). Corrugator ran superolaterally (59%), or laterally (41%). Corrugators inserted mostly to the middle of the eyebrow (57%), or the medial half of the eyebrow (36%), but also to the glabella region (7%). The length of the corrugator ranged 38 to 53 mm. The transverse head (23.38 mm) was longer than the oblique head (19.75 mm). Corrugator was thicker at the medial canthus than at the midpupillary line. Corrugator was innervated by the temporal branch of the facial nerve (66%), the zygomatic branch (17%), or the angular nerve (zygomatic branch and buccal branch, 17%). Supraorbital nerve (60%) or supratrochlear nerve (40%) penetrated the corrugator. The action was depressing, pulling the eyebrow medially (91%), or with medial eyebrow elevation and lateral eyebrow depression (9%). Surgeons must keep this anatomy in mind during surgical procedures.

  8. Venous chest anatomy: clinical implications

    International Nuclear Information System (INIS)

    Chasen, M.H.; Charnsangavej, C.

    1998-01-01

    This article provides a practical approach to the clinical implications and importance of understanding the collateral venous anatomy of the thorax. Routine radiography, conventional venography, computed tomography (CT), and magnetic resonance (MR) imaging studies provide correlative anatomic models for the demonstration of how interconnecting collateral vascular networks within the thorax maintain venous stability at all times. Five major systems comprise the collateral venous network of the thorax ( Fig. 1 ). These include the paravertebral, azygos-hemiazygos, internal mammary, lateral thoracic, and anterior jugular venous systems (AJVS). The five systems are presented in the following sequence: (a) a brief introduction to the importance of catheter position and malposition in understanding access to the thoracic venous system, (b) the anatomy of the azygos-hemiazygos systems and their relationship with the paravertebral plexus, (c) the importance of the AJVS, (d) 'loop' concepts interconnecting the internal mammary and azygos-hemiazygos systems by means of the lateral thoracic and intercostal veins, and (e) the interconnecting venous networks on the thoracic side of the thoracoabdominal junction. Certain aspects of the venous anatomy of the thorax will not be discussed in this chapter and include (a) the intra-abdominal anastomoses between the superior and inferior vena cavae (IVC) via the internal mammary, lateral thoracic, and azygos-hemiazygos systems (beyond the scope of this article), (b) potential collateral vessels involving vertebral, parascapular, thyroidal, thymic, and other smaller veins that might anastomose with the major systems, and (c) anatomic variants and pitfalls that may mimic pathologic conditions (space limitations). (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

  9. Total algorithms

    NARCIS (Netherlands)

    Tel, G.

    We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of

  10. 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)

  11. Gross anatomy of network security

    Science.gov (United States)

    Siu, Thomas J.

    2002-01-01

    Information security involves many branches of effort, including information assurance, host level security, physical security, and network security. Computer network security methods and implementations are given a top-down description to permit a medically focused audience to anchor this information to their daily practice. The depth of detail of network functionality and security measures, like that of the study of human anatomy, can be highly involved. Presented at the level of major gross anatomical systems, this paper will focus on network backbone implementation and perimeter defenses, then diagnostic tools, and finally the user practices (the human element). Physical security measures, though significant, have been defined as beyond the scope of this presentation.

  12. A new chapter in Anatomy

    OpenAIRE

    Şengül, Gülgün

    2015-01-01

    Gülgün Şengül (MD) is a Professor of Anatomy in Ege University, School of Medicine, Izmir, Turkey. Her research field is neuroanatomy. She is one of the authors of the comprehensive spinal cord text book The Spinal Cord: A Christopher and Dana Reeve Foundation Text and Atlas (Elsevier, 2009). Her recent work Atlas of the Spinal Cord of the Rat, Mouse, Marmoset, Rhesus, and Human (Elsevier, 2013) comprises the first marmoset and rhesus monkey and human spinal cord atlases published. These prov...

  13. ZBrush Digital Sculpting Human Anatomy

    CERN Document Server

    Spencer, Scott

    2010-01-01

    Taking into account that many of today?s digital artists?particularly 3D character animators?lack foundational artistic instruction, this book teaches anatomy in a coherent and succinct style. A clear writing style explains how to sculpt an accurate human figure, starting with the skeleton and working out to muscle, fat, and skin. Insightful explanations enable you to quickly and easily create and design characters that can be used in film, game, or print, and allows you to gain a strong understanding of the foundational artistic concepts.

  14. A Rough Set Approach for Customer Segmentation

    Directory of Open Access Journals (Sweden)

    Prabha Dhandayudam

    2014-04-01

    Full Text Available Customer segmentation is a process that divides a business's total customers into groups according to their diversity of purchasing behavior and characteristics. The data mining clustering technique can be used to accomplish this customer segmentation. This technique clusters the customers in such a way that the customers in one group behave similarly when compared to the customers in other groups. The customer related data are categorical in nature. However, the clustering algorithms for categorical data are few and are unable to handle uncertainty. Rough set theory (RST is a mathematical approach that handles uncertainty and is capable of discovering knowledge from a database. This paper proposes a new clustering technique called MADO (Minimum Average Dissimilarity between Objects for categorical data based on elements of RST. The proposed algorithm is compared with other RST based clustering algorithms, such as MMR (Min-Min Roughness, MMeR (Min Mean Roughness, SDR (Standard Deviation Roughness, SSDR (Standard deviation of Standard Deviation Roughness, and MADE (Maximal Attributes DEpendency. The results show that for the real customer data considered, the MADO algorithm achieves clusters with higher cohesion, lower coupling, and less computational complexity when compared to the above mentioned algorithms. The proposed algorithm has also been tested on a synthetic data set to prove that it is also suitable for high dimensional data.

  15. Interactive segmentation framework of the Medical Imaging Interaction Toolkit.

    Science.gov (United States)

    Maleike, D; Nolden, M; Meinzer, H-P; Wolf, I

    2009-10-01

    Interactive methods are indispensable for real world applications of segmentation in medicine, at least to allow for convenient and fast verification and correction of automated techniques. Besides traditional interactive tasks such as adding or removing parts of a segmentation, adjustment of contours or the placement of seed points, the relatively recent Graph Cut and Random Walker segmentation methods demonstrate an interest in advanced interactive strategies for segmentation. Though the value of toolkits and extensible applications is generally accepted for the development of new segmentation algorithms, the topic of interactive segmentation applications is rarely addressed by current toolkits and applications. In this paper, we present the extension of the Medical Imaging Interaction Toolkit (MITK) with a framework for the development of interactive applications for image segmentation. The framework provides a clear structure for the development of new applications and offers a plugin mechanism to easily extend existing applications with additional segmentation tools. In addition, the framework supports shape-based interpolation and multi-level undo/redo of modifications to binary images. To demonstrate the value of the framework, we also present a free, open-source application named InteractiveSegmentation for manual segmentation of medical images (including 3D+t), which is built based on the extended MITK framework. The application includes several features to effectively support manual segmentation, which are not found in comparable freely available applications. InteractiveSegmentation is fully developed and successfully and regularly used in several projects. Using the plugin mechanism, the application enables developers of new algorithms to begin algorithmic work more quickly.

  16. Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

    DEFF Research Database (Denmark)

    Katsarakis, Nikolaos; Pnevmatikakis, Aristodemos; Tan, Zheng-Hua

    2016-01-01

    Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic...

  17. Adaptive geodesic transform for segmentation of vertebrae on CT images

    Science.gov (United States)

    Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

    2014-03-01

    Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

  18. Design Projects in Human Anatomy & Physiology

    Science.gov (United States)

    Polizzotto, Kristin; Ortiz, Mary T.

    2008-01-01

    Very often, some type of writing assignment is required in college entry-level Human Anatomy and Physiology courses. This assignment can be anything from an essay to a research paper on the literature, focusing on a faculty-approved topic of interest to the student. As educators who teach Human Anatomy and Physiology at an urban community college,…

  19. Frontal anatomy and reaction time in Autism

    NARCIS (Netherlands)

    Schmitz, Nicole; Daly, Eileen; Murphy, Declan

    2007-01-01

    Widespread frontal lobe abnormalities, encompassing anatomy and function, are known to be implicated in Autistic Spectrum Disorders (ASD). The correlation between neurobiology and behaviour, however, is poorly understood in ASD. The aim of this study was to investigate frontal lobe anatomy and

  20. Anatomy of a Cancer Treatment Scam

    Medline Plus

    Full Text Available ... All Events Weekly Calendar Weekly Calendar Archive Speeches Audio/Video Featured Videos FTC Events For Consumers For ... Adjudicative Proceedings You are here Home » News & Events » Audio/Video » Anatomy of a Cancer Treatment Scam Anatomy ...