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Sample records for anatomy segmentation algorithm

  1. Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms : VISCERAL Anatomy Benchmarks

    OpenAIRE

    Jimenez-del-Toro, Oscar; Muller, Henning; Krenn, Markus; Gruenberg, Katharina; Taha, Abdel Aziz; Winterstein, Marianne; Eggel, Ivan; Foncubierta-Rodriguez, Antonio; Goksel, Orcun; Jakab, Andres; Kontokotsios, Georgios; Langs, Georg; Menze, Bjoern H.; Fernandez, Tomas Salas; Schaer, Roger

    2016-01-01

    Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the ...

  2. CAnat: An algorithm for the automatic segmentation of anatomy of medical images

    International Nuclear Information System (INIS)

    Caon, M.; Gobert, L.; Mariusz, B.

    2011-01-01

    Full text: To develop a method to automatically categorise organs and tissues displayed in medical images. Dosimetry calculations using Monte Carlo methods require a mathematical representation of human anatomy e.g. a voxel phantom. For a whole body, their construction involves processing several hundred images to identify each organ and tissue-the process is very time-consuming. This project is developing a Computational Anatomy (CAnat) algorithm to automatically recognise and classify the different tissue in a tomographic image. Methods The algorithm utilizes the Statistical Region Merging technique (SRM). The SRM depends on one estimated parameter. The parameter is a measure of statistical complexity of the image and can be automatically adjusted to suit individual image features. This allows for automatic tuning of coarseness of the overall segmentation as well as object specific selection for further tasks. CAnat is tested on two CT images selected to represent different anatomical complexities. In the mid-thigh image, tissues/. regions of interest are air, fat, muscle, bone marrow and compact bone. In the pelvic image, fat, urinary bladder and anus/colon, muscle, cancellous bone, and compact bone. Segmentation results were evaluated using the Jaccard index which is a measure of set agreement. An index of one indicates perfect agreement between CAnat and manual segmentation. The Jaccard indices for the mid-thigh CT were 0.99, 0.89, 0.97, 0.63 and 0.88, respectively and for the pelvic CT were 0.99, 0.81, 0.77, 0.93, 0.53, 0.76, respectively. Conclusion The high accuracy preliminary segmentation results demonstrate the feasibility of the CAnat algorithm.

  3. FRAMEWORK FOR COMPARING SEGMENTATION ALGORITHMS

    Directory of Open Access Journals (Sweden)

    G. Sithole

    2015-05-01

    Full Text Available The notion of a ‘Best’ segmentation does not exist. A segmentation algorithm is chosen based on the features it yields, the properties of the segments (point sets it generates, and the complexity of its algorithm. The segmentation is then assessed based on a variety of metrics such as homogeneity, heterogeneity, fragmentation, etc. Even after an algorithm is chosen its performance is still uncertain because the landscape/scenarios represented in a point cloud have a strong influence on the eventual segmentation. Thus selecting an appropriate segmentation algorithm is a process of trial and error. Automating the selection of segmentation algorithms and their parameters first requires methods to evaluate segmentations. Three common approaches for evaluating segmentation algorithms are ‘goodness methods’, ‘discrepancy methods’ and ‘benchmarks’. Benchmarks are considered the most comprehensive method of evaluation. This paper shortcomings in current benchmark methods are identified and a framework is proposed that permits both a visual and numerical evaluation of segmentations for different algorithms, algorithm parameters and evaluation metrics. The concept of the framework is demonstrated on a real point cloud. Current results are promising and suggest that it can be used to predict the performance of segmentation algorithms.

  4. Multiatlas segmentation of thoracic and abdominal anatomy with level set-based local search.

    Science.gov (United States)

    Schreibmann, Eduard; Marcus, David M; Fox, Tim

    2014-07-08

    Segmentation of organs at risk (OARs) remains one of the most time-consuming tasks in radiotherapy treatment planning. Atlas-based segmentation methods using single templates have emerged as a practical approach to automate the process for brain or head and neck anatomy, but pose significant challenges in regions where large interpatient variations are present. We show that significant changes are needed to autosegment thoracic and abdominal datasets by combining multi-atlas deformable registration with a level set-based local search. Segmentation is hierarchical, with a first stage detecting bulk organ location, and a second step adapting the segmentation to fine details present in the patient scan. The first stage is based on warping multiple presegmented templates to the new patient anatomy using a multimodality deformable registration algorithm able to cope with changes in scanning conditions and artifacts. These segmentations are compacted in a probabilistic map of organ shape using the STAPLE algorithm. Final segmentation is obtained by adjusting the probability map for each organ type, using customized combinations of delineation filters exploiting prior knowledge of organ characteristics. Validation is performed by comparing automated and manual segmentation using the Dice coefficient, measured at an average of 0.971 for the aorta, 0.869 for the trachea, 0.958 for the lungs, 0.788 for the heart, 0.912 for the liver, 0.884 for the kidneys, 0.888 for the vertebrae, 0.863 for the spleen, and 0.740 for the spinal cord. Accurate atlas segmentation for abdominal and thoracic regions can be achieved with the usage of a multi-atlas and perstructure refinement strategy. To improve clinical workflow and efficiency, the algorithm was embedded in a software service, applying the algorithm automatically on acquired scans without any user interaction.

  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. Medical image segmentation using genetic algorithms.

    Science.gov (United States)

    Maulik, Ujjwal

    2009-03-01

    Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

  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. 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.)

  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. Leaf sequencing algorithms for segmented multileaf collimation

    International Nuclear Information System (INIS)

    Kamath, Srijit; Sahni, Sartaj; Li, Jonathan; Palta, Jatinder; Ranka, Sanjay

    2003-01-01

    The delivery of intensity-modulated radiation therapy (IMRT) with a multileaf collimator (MLC) requires the conversion of a radiation fluence map into a leaf sequence file that controls the movement of the MLC during radiation delivery. It is imperative that the fluence map delivered using the leaf sequence file is as close as possible to the fluence map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf sequencing algorithms for segmental multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under most common leaf movement constraints that include minimum leaf separation constraint and leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bidirectional movement of the MLC leaves

  11. Leaf sequencing algorithms for segmented multileaf collimation

    Energy Technology Data Exchange (ETDEWEB)

    Kamath, Srijit [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Sahni, Sartaj [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Li, Jonathan [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Palta, Jatinder [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Ranka, Sanjay [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States)

    2003-02-07

    The delivery of intensity-modulated radiation therapy (IMRT) with a multileaf collimator (MLC) requires the conversion of a radiation fluence map into a leaf sequence file that controls the movement of the MLC during radiation delivery. It is imperative that the fluence map delivered using the leaf sequence file is as close as possible to the fluence map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf sequencing algorithms for segmental multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under most common leaf movement constraints that include minimum leaf separation constraint and leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bidirectional movement of the MLC leaves.

  12. Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

    OpenAIRE

    Carolina Wählby; Joakim Lindblad; Mikael Vondrus; Ewert Bengtsson; Lennart Björkesten

    2002-01-01

    Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre?processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical ana...

  13. NUCLEAR SEGMENTATION IN MICROSCOPE CELL IMAGES: A HAND-SEGMENTED DATASET AND COMPARISON OF ALGORITHMS

    OpenAIRE

    Coelho, Luís Pedro; Shariff, Aabid; Murphy, Robert F.

    2009-01-01

    Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms.

  14. 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%.

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

    Directory of Open Access Journals (Sweden)

    Javadpour A.

    2016-06-01

    Full Text Available 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.

  16. Improved document image segmentation algorithm using multiresolution morphology

    Science.gov (United States)

    Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

    2011-01-01

    Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

  17. 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.

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

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    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.

  19. Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

    Directory of Open Access Journals (Sweden)

    Carolina Wählby

    2002-01-01

    Full Text Available Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

  20. 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.

  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. 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

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

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    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.

  4. Multilevel Image Segmentation Based on an Improved Firefly Algorithm

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    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.

  5. 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.

  6. An Algorithm to Automate Yeast Segmentation and Tracking

    Science.gov (United States)

    Doncic, Andreas; Eser, Umut; Atay, Oguzhan; Skotheim, Jan M.

    2013-01-01

    Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation. PMID:23520484

  7. An algorithm to automate yeast segmentation and tracking.

    Directory of Open Access Journals (Sweden)

    Andreas Doncic

    Full Text Available Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

  8. The speech signal segmentation algorithm using pitch synchronous analysis

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    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.

  9. A Novel Plant Root Foraging Algorithm for Image Segmentation Problems

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    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO. The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem.

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

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    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.

  11. FCM Clustering Algorithms for Segmentation of Brain MR Images

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    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.

  12. Algorithm of Defect Segmentation for AFP Based on Prepregs

    Directory of Open Access Journals (Sweden)

    CAI Zhiqiang

    2017-04-01

    Full Text Available In order to ensure the performance of the automated fiber placement forming parts, according to the homogeneity of the image of the prepreg surface along the fiber direction, a defect segmentation algorithm which was the combination of gray compensation and substraction algorithm based on image processing technology was proposed. The gray compensation matrix of image was used to compensate the gray image, and the maximum error point of the image matrix was eliminated according to the characteristics that the gray error obeys the normal distribution. The standard image was established, using the allowed deviation coefficient K as a criterion for substraction segmentation. Experiments show that the algorithm has good effect, fast speed in segmenting two kinds of typical laying defect of bubbles or foreign objects, and provides a good theoretical basis to realize automatic laying defect online monitoring.

  13. Objectness Supervised Merging Algorithm for Color Image Segmentation

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    Haifeng Sima

    2016-01-01

    Full Text Available Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

  14. 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.

  15. 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)

  16. 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

  17. Comparison of segmentation algorithms for fluorescence microscopy images of cells.

    Science.gov (United States)

    Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

    2011-07-01

    The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

  18. Video Segmentation Using Fast Marching and Region Growing Algorithms

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    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.

  19. CT examination of segmental liver transplants from living donors. Anatomy and pathological findings

    International Nuclear Information System (INIS)

    Krupski, G.; Maas, R.; Rogiers, X.; Burdelski, M.; Broelsch, C.E.

    1994-01-01

    A lack of suitable pediatric donors and significantly better results than conventional transplantation have contributed to the steady increase in the number of segmental liver transplants from living donors throughout the world. This article describes the diagnostic impact of axial CT scans following transplantation in a retrospective evaluation of 18 CT examinations of 10 children with an average age of two years. Both spiral and conventional CT scans permit precise visualization of the postoperative anatomy of the upper abdomen that is more distinct than the images provided by ultrasonic scans. Thus, CT scans better facilitate detection of pathological findings. In 60% of the patients (67% of the examinations), the CT scan permitted a definite diagnosis; in the remaining cases, no morphological correlate to the clinical and laboratory findings was detected. In addition to traditional ultrasonic scanning, computed tomography represents a further noninvasive imaging technique for postoperative diagnostics following segmental liver transplants from living donors. (orig.) [de

  20. Optimization-Based Image Segmentation by Genetic Algorithms

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    Rosenberger C

    2008-01-01

    Full Text Available Abstract Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

  1. Optimization-Based Image Segmentation by Genetic Algorithms

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    H. Laurent

    2008-05-01

    Full Text Available Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

  2. Study of the renal segmental arterial anatomy with contrast-enhanced multi-detector computed tomography.

    Science.gov (United States)

    Rocco, Francesco; Cozzi, Luigi Alberto; Cozzi, Gabriele

    2015-07-01

    To use triphasic multi-detector computed tomography (MDCT) to study the renal segmental arterial anatomy and its relationship with the urinary tract to plan nephron-sparing surgery (NSS). One hundred and fifty nine patients underwent abdominal contrast-enhanced MDCT. We evaluated renal arteries and parenchymal vasculature. In 61 patients, the arteries and the urinary tract were represented simultaneously. 86.60% presented a single renal artery; 13.4%, multiple arteries. All single renal arteries divided into anterior and posterior branch before the hilum. The anterior artery branched into a superior, middle, and inferior branch. In 43.14%, the inferior artery arose before the others; in 45.75%, the superior artery arose before the others; in 9.80%, the branches shared a common trunk. In 26.80%, the posterior artery supplies the entire posterior surface; in 73.20%, it ends along the inferior calyx. In 96.73%, the upper pole was vascularized by the anterior superior branch and the posterior artery: the "tuning fork". MDCT showed four vascular segments in 96.73% and five in 3.27%. MDCT showed two avascular areas: the first along the projection of the inferior calyx on the posterior aspect, the second between the branches of the "tuning fork". The arterial phase provides the arterial tree representation; the delayed phase shows arteries and urinary tract simultaneously. MDCT provides a useful representation of the renal anatomy prior to intervascular-intrarenal NSS.

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

    Science.gov (United States)

    Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

    2014-03-01

    This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured 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 connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

  4. WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION

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    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.

  5. Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

    Science.gov (United States)

    Karami, Elham; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

    2015-03-01

    The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

  6. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

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    Jiayin Liu

    2017-06-01

    Full Text Available 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.

  7. 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.

  8. An Alternative to Chaid Segmentation Algorithm Based on Entropy.

    Directory of Open Access Journals (Sweden)

    María Purificación Galindo Villardón

    2010-07-01

    Full Text Available The CHAID (Chi-Squared Automatic Interaction Detection treebased segmentation technique has been found to be an effective approach for obtaining meaningful segments that are predictive of a K-category (nominal or ordinal criterion variable. CHAID was designed to detect, in an automatic way, the  nteraction between several categorical or ordinal predictors in explaining a categorical response, but, this may not be true when Simpson’s paradox is present. This is due to the fact that CHAID is a forward selection algorithm based on the marginal counts. In this paper we propose a backwards elimination algorithm that starts with the full set of predictors (or full tree and eliminates predictors progressively. The elimination procedure is based on Conditional Independence contrasts using the concept of entropy. The proposed procedure is compared to CHAID.

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

    Science.gov (United States)

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

    2013-04-30

    This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients ('benchmarking cases'), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 "RADAR" trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation 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

  10. Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain Images

    Directory of Open Access Journals (Sweden)

    Guohua Zou

    2016-12-01

    Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.

  11. Modeling and segmentation of intra-cochlear anatomy in conventional CT

    Science.gov (United States)

    Noble, Jack H.; Rutherford, Robert B.; Labadie, Robert F.; Majdani, Omid; Dawant, Benoit M.

    2010-03-01

    Cochlear implant surgery is a procedure performed to treat profound hearing loss. Since the cochlea is not visible in surgery, the physician uses anatomical landmarks to estimate the pose of the cochlea. Research has indicated that implanting the electrode in a particular cavity of the cochlea, the scala tympani, results in better hearing restoration. The success of the scala tympani implantation is largely dependent on the point of entry and angle of electrode insertion. Errors can occur due to the imprecise nature of landmark-based, manual navigation as well as inter-patient variations between scala tympani and the anatomical landmarks. In this work, we use point distribution models of the intra-cochlear anatomy to study the inter-patient variations between the cochlea and the typical anatomic landmarks, and we implement an active shape model technique to automatically localize intra-cochlear anatomy in conventional CT images, where intra-cochlear structures are not visible. This fully automatic segmentation could aid the surgeon to choose the point of entry and angle of approach to maximize the likelihood of scala tympani insertion, resulting in more substantial hearing restoration.

  12. 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.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

    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.

  14. 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.

  15. 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 "contrast 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

  16. 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.

  17. Do Three-dimensional Visualization and Three-dimensional Printing Improve Hepatic Segment Anatomy Teaching? A Randomized Controlled Study.

    Science.gov (United States)

    Kong, Xiangxue; Nie, Lanying; Zhang, Huijian; Wang, Zhanglin; Ye, Qiang; Tang, Lei; Li, Jianyi; Huang, Wenhua

    2016-01-01

    Hepatic segment anatomy is difficult for medical students to learn. Three-dimensional visualization (3DV) is a useful tool in anatomy teaching, but current models do not capture haptic qualities. However, three-dimensional printing (3DP) can produce highly accurate complex physical models. Therefore, in this study we aimed to develop a novel 3DP hepatic segment model and compare the teaching effectiveness of a 3DV model, a 3DP model, and a traditional anatomical atlas. A healthy candidate (female, 50-years old) was recruited and scanned with computed tomography. After three-dimensional (3D) reconstruction, the computed 3D images of the hepatic structures were obtained. The parenchyma model was divided into 8 hepatic segments to produce the 3DV hepatic segment model. The computed 3DP model was designed by removing the surrounding parenchyma and leaving the segmental partitions. Then, 6 experts evaluated the 3DV and 3DP models using a 5-point Likert scale. A randomized controlled trial was conducted to evaluate the educational effectiveness of these models compared with that of the traditional anatomical atlas. The 3DP model successfully displayed the hepatic segment structures with partitions. All experts agreed or strongly agreed that the 3D models provided good realism for anatomical instruction, with no significant differences between the 3DV and 3DP models in each index (p > 0.05). Additionally, the teaching effects show that the 3DV and 3DP models were significantly better than traditional anatomical atlas in the first and second examinations (p < 0.05). Between the first and second examinations, only the traditional method group had significant declines (p < 0.05). A novel 3DP hepatic segment model was successfully developed. Both the 3DV and 3DP models could improve anatomy teaching significantly. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

  18. 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.

  19. Linear segmentation algorithm for detecting layer boundary with lidar.

    Science.gov (United States)

    Mao, Feiyue; Gong, Wei; Logan, Timothy

    2013-11-04

    The automatic detection of aerosol- and cloud-layer boundary (base and top) is important in atmospheric lidar data processing, because the boundary information is not only useful for environment and climate studies, but can also be used as input for further data processing. Previous methods have demonstrated limitations in defining the base and top, window-size setting, and have neglected the in-layer attenuation. To overcome these limitations, we present a new layer detection scheme for up-looking lidars based on linear segmentation with a reasonable threshold setting, boundary selecting, and false positive removing strategies. Preliminary results from both real and simulated data show that this algorithm cannot only detect the layer-base as accurate as the simple multi-scale method, but can also detect the layer-top more accurately than that of the simple multi-scale method. Our algorithm can be directly applied to uncalibrated data without requiring any additional measurements or window size selections.

  20. A segmentation algorithm based on image projection for complex text layout

    Science.gov (United States)

    Zhu, Wangsheng; Chen, Qin; Wei, Chuanyi; Li, Ziyang

    2017-10-01

    Segmentation algorithm is an important part of layout analysis, considering the efficiency advantage of the top-down approach and the particularity of the object, a breakdown of projection layout segmentation algorithm. Firstly, the algorithm will algorithm first partitions the text image, and divided into several columns, then for each column scanning projection, the text image is divided into several sub regions through multiple projection. The experimental results show that, this method inherits the projection itself and rapid calculation speed, but also can avoid the effect of arc image information page segmentation, and also can accurate segmentation of the text image layout is complex.

  1. 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

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

    CSIR Research Space (South Africa)

    Mabuza-Hocquet, G

    2015-06-01

    Full Text Available The performance of an iris recognition system relies on automated processes from the segmentation stage to the matching stage. Each stage has traditional algorithms used successfully over the years. The drawback is that these algorithms assume...

  3. 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.

  4. 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.

  5. Sectional anatomy aid for improvement of decompression surgery approach to vertical segment of facial nerve.

    Science.gov (United States)

    Feng, Yan; Zhang, Yi Qun; Liu, Min; Jin, Limin; Huangfu, Mingmei; Liu, Zhenyu; Hua, Peiyan; Liu, Yulong; Hou, Ruida; Sun, Yu; Li, You Qiong; Wang, Yu Fa; Feng, Jia Chun

    2012-05-01

    The aim of this study was to find a surgical approach to a vertical segment of the facial nerve (VFN) with a relatively wide visual field and small lesion by studying the location and structure of VFN with cross-sectional anatomy. High-resolution spiral computed tomographic multiplane reformation was used to reform images that were parallel to the Frankfort horizontal plane. To locate the VFN, we measured the distances as follows: from the VFN to the paries posterior bony external acoustic meatus on 5 typical multiplane reformation images, to the promontorium tympani and the root of the tympanic ring on 2 typical images. The mean distances from the VFN to the paries posterior bony external acoustic meatus are as follows: 4.47 mm on images showing the top of the external acoustic meatus, 4.20 mm on images with the best view of the window niche, 3.35 mm on images that show the widest external acoustic meatus, 4.22 mm on images with the inferior margin of the sulcus tympanicus, and 5.49 mm on images that show the bottom of the external acoustic meatus. The VFN is approximately 4.20 mm lateral to the promontorium tympani on images with the best view of the window niche and 4.12 mm lateral to the root of the tympanic ring on images with the inferior margin of the sulcus tympanicus. The other results indicate that the area and depth of the surgical wound from the improved approach would be much smaller than that from the typical approach. The surgical approach to the horizontal segment of the facial nerve through the external acoustic meatus and the tympanic cavity could be improved by grinding off the external acoustic meatus to show the VFN. The VFN can be found by taking the promontorium tympani and tympanic ring as references. This improvement is of high potential to expand the visual field to the facial nerve, remarkably without significant injury to the patients compared with the typical approach through the mastoid process.

  6. Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action.

    Science.gov (United States)

    Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter; Egger, Jan

    2018-01-01

    Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p 0.94) for any of the comparison made between the two groups. Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a

  7. Motion based segmentation for robot vision using adapted EM algorithm

    NARCIS (Netherlands)

    Zhao, Wei; Roos, Nico

    2016-01-01

    Robots operate in a dynamic world in which objects are often moving. The movement of objects may help the robot to segment the objects from the background. The result of the segmentation can subsequently be used to identify the objects. This paper investigates the possibility of segmenting objects

  8. 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.

  9. A medial point cloud based algorithm for dental cast segmentation

    NARCIS (Netherlands)

    Kustra, J.; Jager, de M.K.J.; Jalba, A.C.; Telea, A.C.

    2014-01-01

    Although the detailed oral anatomy can be acquired using techniques such as Computer Tomography, the development of mass consumer products is dependent of non-invasive, safe acquisition techniques such as the bite imprint, commonly used in orthodontic treatments. However, bite imprints provide only

  10. 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

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

    International Nuclear Information System (INIS)

    Dominguez, Alfonso Rojas; Nandi, Asoke K.

    2007-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Mingying Zhang

    2016-12-01

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

  13. 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.

  14. 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%.

  15. 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.

  16. 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.

  17. Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

    Science.gov (United States)

    Sawyer, Travis W.; Rice, Photini F. S.; Sawyer, David M.; Koevary, Jennifer W.; Barton, Jennifer K.

    2018-02-01

    Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depthresolved, high-resolution images of biological tissue in real time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must rst be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluated a set of algorithms to segment OCT images of mouse ovaries. We examined ve preprocessing techniques and six segmentation algorithms. While all pre-processing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% +/- 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 0.948 +/- 0.012 compared with manual segmentation (1.0 being identical). Nonetheless, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.

  18. 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.)

  19. 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.

  20. 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.

  1. 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.

  2. 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...... delineation shows that the two methods are interchangeable according to the Bland and Altman criterion, and thus equally accurate. The repeatability of the watershed method and the manual method are compared by looking at the similarity of the segmented volumes. The similarity for intraobserver...

  3. 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.

  4. 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

  5. Nasal Anatomy

    Science.gov (United States)

    ... Caregivers Contact ARS HOME ANATOMY Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ... Size + - Home > ANATOMY > Nasal Anatomy Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ...

  6. Sinus Anatomy

    Science.gov (United States)

    ... Caregivers Contact ARS HOME ANATOMY Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ... Size + - Home > ANATOMY > Sinus Anatomy Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ...

  7. 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.

  8. 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)

  9. 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.

  10. 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.

  11. 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.

  12. A kind of color image segmentation algorithm based on super-pixel and PCNN

    Science.gov (United States)

    Xu, GuangZhu; Wang, YaWen; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

    2018-04-01

    Image segmentation is a very important step in the low-level visual computing. Although image segmentation has been studied for many years, there are still many problems. PCNN (Pulse Coupled Neural network) has biological background, when it is applied to image segmentation it can be viewed as a region-based method, but due to the dynamics properties of PCNN, many connectionless neurons will pulse at the same time, so it is necessary to identify different regions for further processing. The existing PCNN image segmentation algorithm based on region growing is used for grayscale image segmentation, cannot be directly used for color image segmentation. In addition, the super-pixel can better reserve the edges of images, and reduce the influences resulted from the individual difference between the pixels on image segmentation at the same time. Therefore, on the basis of the super-pixel, the original PCNN algorithm based on region growing is improved by this paper. First, the color super-pixel image was transformed into grayscale super-pixel image which was used to seek seeds among the neurons that hadn't been fired. And then it determined whether to stop growing by comparing the average of each color channel of all the pixels in the corresponding regions of the color super-pixel image. Experiment results show that the proposed algorithm for the color image segmentation is fast and effective, and has a certain effect and accuracy.

  13. 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.

  14. 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.

  15. Algorithms for automatic segmentation of bovine embryos produced in vitro

    International Nuclear Information System (INIS)

    Melo, D H; Oliveira, D L; Nascimento, M Z; Neves, L A; Annes, K

    2014-01-01

    In vitro production has been employed in bovine embryos and quantification of lipids is fundamental to understand the metabolism of these embryos. This paper presents a unsupervised segmentation method for histological images of bovine embryos. In this method, the anisotropic filter was used in the differents RGB components. After pre-processing step, the thresholding technique based on maximum entropy was applied to separate lipid droplets in the histological slides in different stages: early cleavage, morula and blastocyst. In the postprocessing step, false positives are removed using the connected components technique that identify regions with excess of dye near pellucid zone. The proposed segmentation method was applied in 30 histological images of bovine embryos. Experiments were performed with the images and statistical measures of sensitivity, specificity and accuracy were calculated based on reference images (gold standard). The value of accuracy of the proposed method was 96% with standard deviation of 3%

  16. 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 images Subject RIV: JC - Computer Hardware ; Software Impact factor: 2.136, year: 2015 http://library.utia.cas.cz/separaty/2014/ZOI/zitova-0434809-DOI.pdf

  17. Color segmentation in the HSI color space using the K-means algorithm

    Science.gov (United States)

    Weeks, Arthur R.; Hague, G. Eric

    1997-04-01

    Segmentation of images is an important aspect of image recognition. While grayscale image segmentation has become quite a mature field, much less work has been done with regard to color image segmentation. Until recently, this was predominantly due to the lack of available computing power and color display hardware that is required to manipulate true color images (24-bit). TOday, it is not uncommon to find a standard desktop computer system with a true-color 24-bit display, at least 8 million bytes of memory, and 2 gigabytes of hard disk storage. Segmentation of color images is not as simple as segmenting each of the three RGB color components separately. The difficulty of using the RGB color space is that it doesn't closely model the psychological understanding of color. A better color model, which closely follows that of human visual perception is the hue, saturation, intensity model. This color model separates the color components in terms of chromatic and achromatic information. Strickland et al. was able to show the importance of color in the extraction of edge features form an image. His method enhances the edges that are detectable in the luminance image with information from the saturation image. Segmentation of both the saturation and intensity components is easily accomplished with any gray scale segmentation algorithm, since these spaces are linear. The modulus 2(pi) nature of the hue color component makes its segmentation difficult. For example, a hue of 0 and 2(pi) yields the same color tint. Instead of applying separate image segmentation to each of the hue, saturation, and intensity components, a better method is to segment the chromatic component separately from the intensity component because of the importance that the chromatic information plays in the segmentation of color images. This paper presents a method of using the gray scale K-means algorithm to segment 24-bit color images. Additionally, this paper will show the importance the hue

  18. 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.

  19. 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

  20. A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images

    Directory of Open Access Journals (Sweden)

    Siyan Liu

    2017-01-01

    Full Text Available Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level. Then, two adjacent regions are merged in each iteration by a new designed scheme, and a threshold is removed each time. To improve the accuracy of the merger operation, variance and probability are used as energy. No matter how many the thresholds are, the time complexity of the algorithm is stable at O(L. Finally, the experiment is conducted on many MR brain images to verify the performance of the proposed algorithm. Experiment results show that our method can reduce the running time effectively and obtain segmentation results with high accuracy.

  1. Efficient Algorithms for Analyzing Segmental Duplications, Deletions, and Inversions in Genomes

    Science.gov (United States)

    Kahn, Crystal L.; Mozes, Shay; Raphael, Benjamin J.

    Segmental duplications, or low-copy repeats, are common in mammalian genomes. In the human genome, most segmental duplications are mosaics consisting of pieces of multiple other segmental duplications. This complex genomic organization complicates analysis of the evolutionary history of these sequences. Earlier, we introduced a genomic distance, called duplication distance, that computes the most parsimonious way to build a target string by repeatedly copying substrings of a source string. We also showed how to use this distance to describe the formation of segmental duplications according to a two-step model that has been proposed to explain human segmental duplications. Here we describe polynomial-time exact algorithms for several extensions of duplication distance including models that allow certain types of substring deletions and inversions. These extensions will permit more biologically realistic analyses of segmental duplications in genomes.

  2. An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Zoran N. Milivojevic

    2011-09-01

    Full Text Available The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

  3. 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

  4. 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.

  5. 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.

  6. Fuzzy C-Means Algorithm for Segmentation of Aerial Photography Data Obtained Using Unmanned Aerial Vehicle

    Science.gov (United States)

    Akinin, M. V.; Akinina, N. V.; Klochkov, A. Y.; Nikiforov, M. B.; Sokolova, A. V.

    2015-05-01

    The report reviewed the algorithm fuzzy c-means, performs image segmentation, give an estimate of the quality of his work on the criterion of Xie-Beni, contain the results of experimental studies of the algorithm in the context of solving the problem of drawing up detailed two-dimensional maps with the use of unmanned aerial vehicles. According to the results of the experiment concluded that the possibility of applying the algorithm in problems of decoding images obtained as a result of aerial photography. The considered algorithm can significantly break the original image into a plurality of segments (clusters) in a relatively short period of time, which is achieved by modification of the original k-means algorithm to work in a fuzzy task.

  7. 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)

  8. Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably

    Energy Technology Data Exchange (ETDEWEB)

    Ashraf, H.; Bach, K.S.; Hansen, H. [Copenhagen University, Department of Radiology, Gentofte Hospital, Hellerup (Denmark); Hoop, B. de [University Medical Centre Utrecht, Department of Radiology, Utrecht (Netherlands); Shaker, S.B.; Dirksen, A. [Copenhagen University, Department of Respiratory Medicine, Gentofte Hospital, Hellerup (Denmark); Prokop, M. [University Medical Centre Utrecht, Department of Radiology, Utrecht (Netherlands); Radboud University Nijmegen, Department of Radiology, Nijmegen (Netherlands); Pedersen, J.H. [Copenhagen University, Department of Cardiothoracic Surgery RT, Rigshospitalet, Copenhagen (Denmark)

    2010-08-15

    We examined the reproducibility of lung nodule volumetry software that offers three different volumetry algorithms. In a lung cancer screening trial, 188 baseline nodules >5 mm were identified. Including follow-ups, these nodules formed a study-set of 545 nodules. Nodules were independently double read by two readers using commercially available volumetry software. The software offers readers three different analysing algorithms. We compared the inter-observer variability of nodule volumetry when the readers used the same and different algorithms. Both readers were able to correctly segment and measure 72% of nodules. In 80% of these cases, the readers chose the same algorithm. When readers used the same algorithm, exactly the same volume was measured in 50% of readings and a difference of >25% was observed in 4%. When the readers used different algorithms, 83% of measurements showed a difference of >25%. Modern volumetric software failed to correctly segment a high number of screen detected nodules. While choosing a different algorithm can yield better segmentation of a lung nodule, reproducibility of volumetric measurements deteriorates substantially when different algorithms were used. It is crucial even in the same software package to choose identical parameters for follow-up. (orig.)

  9. Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably

    International Nuclear Information System (INIS)

    Ashraf, H.; Bach, K.S.; Hansen, H.; Hoop, B. de; Shaker, S.B.; Dirksen, A.; Prokop, M.; Pedersen, J.H.

    2010-01-01

    We examined the reproducibility of lung nodule volumetry software that offers three different volumetry algorithms. In a lung cancer screening trial, 188 baseline nodules >5 mm were identified. Including follow-ups, these nodules formed a study-set of 545 nodules. Nodules were independently double read by two readers using commercially available volumetry software. The software offers readers three different analysing algorithms. We compared the inter-observer variability of nodule volumetry when the readers used the same and different algorithms. Both readers were able to correctly segment and measure 72% of nodules. In 80% of these cases, the readers chose the same algorithm. When readers used the same algorithm, exactly the same volume was measured in 50% of readings and a difference of >25% was observed in 4%. When the readers used different algorithms, 83% of measurements showed a difference of >25%. Modern volumetric software failed to correctly segment a high number of screen detected nodules. While choosing a different algorithm can yield better segmentation of a lung nodule, reproducibility of volumetric measurements deteriorates substantially when different algorithms were used. It is crucial even in the same software package to choose identical parameters for follow-up. (orig.)

  10. 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

    2016-01-01

    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 (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and

  11. 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 (<1 ms) with a satisfying accuracy (Dice=0.95). When the 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

  12. 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.

  13. 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

  14. Novel algorithm by low complexity filter on retinal vessel segmentation

    Science.gov (United States)

    Rostampour, Samad

    2011-10-01

    This article shows a new method to detect blood vessels in the retina by digital images. Retinal vessel segmentation is important for detection of side effect of diabetic disease, because diabetes can form new capillaries which are very brittle. The research has been done in two phases: preprocessing and processing. Preprocessing phase consists to apply a new filter that produces a suitable output. It shows vessels in dark color on white background and make a good difference between vessels and background. The complexity is very low and extra images are eliminated. The second phase is processing and used the method is called Bayesian. It is a built-in in supervision classification method. This method uses of mean and variance of intensity of pixels for calculate of probability. Finally Pixels of image are divided into two classes: vessels and background. Used images are related to the DRIVE database. After performing this operation, the calculation gives 95 percent of efficiency average. The method also was performed from an external sample DRIVE database which has retinopathy, and perfect result was obtained

  15. 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.

  16. 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.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  18. 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.

  19. Ambient occlusion - A powerful algorithm to segment shell and skeletal intrapores in computed tomography data

    Science.gov (United States)

    Titschack, J.; Baum, D.; Matsuyama, K.; Boos, K.; Färber, C.; Kahl, W.-A.; Ehrig, K.; Meinel, D.; Soriano, C.; Stock, S. R.

    2018-06-01

    During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably. Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance-map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising

  20. Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology from in vivo MRI

    International Nuclear Information System (INIS)

    Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Filgueiras-Rama, David; Pizarro, Gonzalo; Ibañez, Borja; Berenfeld, Omer; Boyers, Pamela; Gold, Jeffrey

    2012-01-01

    This paper presents an automated method to segment left ventricle (LV) tissues from functional and delayed-enhancement (DE) cardiac magnetic resonance imaging (MRI) scans using a sequential multi-step approach. First, a region of interest (ROI) is computed to create a subvolume around the LV using morphological operations and image arithmetic. From the subvolume, the myocardial contours are automatically delineated using difference of Gaussians (DoG) filters and GSV snakes. These contours are used as a mask to identify pathological tissues, such as fibrosis or scar, within the DE-MRI. The presented automated technique is able to accurately delineate the myocardium and identify the pathological tissue in patient sets. The results were validated by two expert cardiologists, and in one set the automated results are quantitatively and qualitatively compared with expert manual delineation. Furthermore, the method is patient-specific, performed on an entire patient MRI series. Thus, in addition to providing a quick analysis of individual MRI scans, the fully automated segmentation method is used for effectively tagging regions in order to reconstruct computerized patient-specific 3D cardiac models. These models can then be used in electrophysiological studies and surgical strategy planning. (paper)

  1. 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.

  2. 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

  3. Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

    Directory of Open Access Journals (Sweden)

    Erik Cuevas

    2015-01-01

    Full Text Available As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1 they frequently obtain suboptimal results (misclassifications as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2 the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS, is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness.

  4. 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.

  5. Segmentation algorithm on smartphone dual camera: application to plant organs in the wild

    Science.gov (United States)

    Bertrand, Sarah; Cerutti, Guillaume; Tougne, Laure

    2018-04-01

    In order to identify the species of a tree, the different organs that are the leaves, the bark, the flowers and the fruits, are inspected by botanists. So as to develop an algorithm that identifies automatically the species, we need to extract these objects of interest from their complex natural environment. In this article, we focus on the segmentation of flowers and fruits and we present a new method of segmentation based on an active contour algorithm using two probability maps. The first map is constructed via the dual camera that we can find on the back of the latest smartphones. The second map is made with the help of a multilayer perceptron (MLP). The combination of these two maps to drive the evolution of the object contour allows an efficient segmentation of the organ from a natural background.

  6. Analysis of the Command and Control Segment (CCS) attitude estimation algorithm

    Science.gov (United States)

    Stockwell, Catherine

    1993-01-01

    This paper categorizes the qualitative behavior of the Command and Control Segment (CCS) differential correction algorithm as applied to attitude estimation using simultaneous spin axis sun angle and Earth cord length measurements. The categories of interest are the domains of convergence, divergence, and their boundaries. Three series of plots are discussed that show the dependence of the estimation algorithm on the vehicle radius, the sun/Earth angle, and the spacecraft attitude. Common qualitative dynamics to all three series are tabulated and discussed. Out-of-limits conditions for the estimation algorithm are identified and discussed.

  7. Fast and Accurate Ground Truth Generation for Skew-Tolerance Evaluation of Page Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Okun Oleg

    2006-01-01

    Full Text Available Many image segmentation algorithms are known, but often there is an inherent obstacle in the unbiased evaluation of segmentation quality: the absence or lack of a common objective representation for segmentation results. Such a representation, known as the ground truth, is a description of what one should obtain as the result of ideal segmentation, independently of the segmentation algorithm used. The creation of ground truth is a laborious process and therefore any degree of automation is always welcome. Document image analysis is one of the areas where ground truths are employed. In this paper, we describe an automated tool called GROTTO intended to generate ground truths for skewed document images, which can be used for the performance evaluation of page segmentation algorithms. Some of these algorithms are claimed to be insensitive to skew (tilt of text lines. However, this fact is usually supported only by a visual comparison of what one obtains and what one should obtain since ground truths are mostly available for upright images, that is, those without skew. As a result, the evaluation is both subjective; that is, prone to errors, and tedious. Our tool allows users to quickly and easily produce many sufficiently accurate ground truths that can be employed in practice and therefore it facilitates automatic performance evaluation. The main idea is to utilize the ground truths available for upright images and the concept of the representative square [9] in order to produce the ground truths for skewed images. The usefulness of our tool is demonstrated through a number of experiments with real-document images of complex layout.

  8. On the importance of FIB-SEM specific segmentation algorithms for porous media

    Energy Technology Data Exchange (ETDEWEB)

    Salzer, Martin, E-mail: martin.salzer@uni-ulm.de [Institute of Stochastics, Faculty of Mathematics and Economics, Ulm University, D-89069 Ulm (Germany); Thiele, Simon, E-mail: simon.thiele@imtek.uni-freiburg.de [Laboratory for MEMS Applications, IMTEK, Department of Microsystems Engineering, University of Freiburg, D-79110 Freiburg (Germany); Zengerle, Roland, E-mail: zengerle@imtek.uni-freiburg.de [Laboratory for MEMS Applications, IMTEK, Department of Microsystems Engineering, University of Freiburg, D-79110 Freiburg (Germany); Schmidt, Volker, E-mail: volker.schmidt@uni-ulm.de [Institute of Stochastics, Faculty of Mathematics and Economics, Ulm University, D-89069 Ulm (Germany)

    2014-09-15

    A new algorithmic approach to segmentation of highly porous three dimensional image data gained by focused ion beam tomography is described which extends the key-principle of local threshold backpropagation described in Salzer et al. (2012). The technique of focused ion beam tomography has shown to be capable of imaging the microstructure of functional materials. In order to perform a quantitative analysis on the corresponding microstructure a segmentation task needs to be performed. However, algorithmic segmentation of images obtained with focused ion beam tomography is a challenging problem for highly porous materials if filling the pore phase, e.g. with epoxy resin, is difficult. The gray intensities of individual voxels are not sufficient to determine the phase represented by them and usual thresholding methods are not applicable. We thus propose a new approach to segmentation that pays respect to the specifics of the imaging process of focused ion beam tomography. As an application of our approach, the segmentation of three dimensional images for a cathode material used in polymer electrolyte membrane fuel cells is discussed. We show that our approach preserves significantly more of the original nanostructure than a thresholding approach. - Highlights: • We describe a new approach to the segmentation of FIB-SEM images of porous media. • The first and last occurrences of structures are detected by analysing the z-profiles. • The algorithm is validated by comparing it to a manual segmentation. • The new approach shows significantly less artifacts than a thresholding approach. • A structural analysis also shows improved results for the obtained microstructure.

  9. 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...

  10. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Lopez, Jose J.; Aguado, Jose A.; Martin, F.; Munoz, F.; Rodriguez, A.; Ruiz, Jose E. [Department of Electrical Engineering, University of Malaga, C/ Dr. Ortiz Ramos, sn., Escuela de Ingenierias, 29071 Malaga (Spain)

    2011-02-15

    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)

  12. 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)

  13. An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm

    Science.gov (United States)

    Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin

    2018-04-01

    Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

  14. 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.

  15. 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.

  16. An anatomy-based beam segmentation tool for intensity-modulated radiation therapy and its application to head-and-neck cancer

    International Nuclear Information System (INIS)

    Gersem, Werner de; Claus, Filip; Wagter, Carlos de; Neve, Wilfried de

    2001-01-01

    Purpose: In segmental intensity-modulated radiation therapy (IMRT), the beam fluences result from superposition of unmodulated beamlets (segments). In the inverse planning approach, segments are a result of 'clipping' intensity maps. At Ghent University Hospital, segments are created by an anatomy-based segmentation tool (ABST). The objective of this report is to describe ABST. Methods and Materials: For each beam direction, ABST generates segments by a multistep procedure. During the initial steps, beam's eye view (BEV) projections of the planning target volumes (PTVs) and organs at risk (OARs) are generated. These projections are used to make a segmentation grid with negative values across the expanded OAR projections and positive values elsewhere inside the expanded PTV projections. Outside these regions, grid values are set to zero. Subsequent steps transform the positive values of the segmentation grid to increase with decreasing distance to the OAR projections and to increase with longer pathlengths measured along rays from their entrance point through the skin contours to their respective grid point. The final steps involve selection of iso-value lines of the segmentation grid as segment outlines which are transformed to leaf and jaw positions of a multileaf collimator (MLC). Segment shape approximations, if imposed by MLC constraints, are done in a way that minimizes overlap between the expanded OAR projections and the segment aperture. Results: The ABST procedure takes about 3 s/segment on a Compaq Alpha XP900 workstation. In IMRT planning problems with little complexity, such as laryngeal (example shown) or thyroid cancer, plans that are in accordance with the clinical protocol can be generated by weighting the segments generated by ABST without further optimization of their shapes. For complex IMRT plans such as paranasal sinus cancer (not shown), ABST generates a start assembly of segments from which the shapes and weights are further optimized

  17. 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.

  18. A combination of compositional index and genetic algorithm for predicting transmembrane helical segments.

    Directory of Open Access Journals (Sweden)

    Nazar Zaki

    Full Text Available Transmembrane helix (TMH topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method.The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

  19. A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

    Science.gov (United States)

    Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

    2015-01-01

    Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

  20. 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.

  1. Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images.

    Directory of Open Access Journals (Sweden)

    Assaf Zaritsky

    Full Text Available Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional

  2. Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images.

    Science.gov (United States)

    Zaritsky, Assaf; Natan, Sari; Horev, Judith; Hecht, Inbal; Wolf, Lior; Ben-Jacob, Eshel; Tsarfaty, Ilan

    2011-01-01

    Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single

  3. 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...

  4. US-Cut: interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions

    Science.gov (United States)

    Egger, Jan; Voglreiter, Philip; Dokter, Mark; Hofmann, Michael; Chen, Xiaojun; Zoller, Wolfram G.; Schmalstieg, Dieter; Hann, Alexander

    2016-04-01

    Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing the user to get real-time feedback of the segmentation results. The algorithm has been developed and tested hand-in-hand by physicians and computer scientists to make sure a future practical usage in a clinical setting is feasible. To cover typical acquisitions from the clinical routine, the approach has been evaluated with dozens of datasets where the tumors are hyperechoic (brighter), hypoechoic (darker) or isoechoic (similar) in comparison to the surrounding liver tissue. Due to the interactive real-time behavior of the approach, it was possible even in difficult cases to find satisfying segmentations of the tumors within seconds and without parameter settings, and the average tumor deviation was only 1.4mm compared with manual measurements. However, the long term goal is to ease the volumetric acquisition of liver tumors in order to evaluate for treatment response. Additional aim is the registration of intraoperative US images via the interactive segmentations to the patient's pre-interventional CT acquisitions.

  5. Evaluation of segmentation algorithms for generation of patient models in radiofrequency hyperthermia

    International Nuclear Information System (INIS)

    Wust, P.; Gellermann, J.; Beier, J.; Tilly, W.; Troeger, J.; Felix, R.; Wegner, S.; Oswald, H.; Stalling, D.; Hege, H.C.; Deuflhard, P.

    1998-01-01

    Time-efficient and easy-to-use segmentation algorithms (contour generation) are a precondition for various applications in radiation oncology, especially for planning purposes in hyperthermia. We have developed the three following algorithms for contour generation and implemented them in an editor of the HyperPlan hyperthermia planning system. Firstly, a manual contour input with numerous correction and editing options. Secondly, a volume growing algorithm with adjustable threshold range and minimal region size. Thirdly, a watershed transformation in two and three dimensions. In addition, the region input function of the Helax commercial radiation therapy planning system was available for comparison. All four approaches were applied under routine conditions to two-dimensional computed tomographic slices of the superior thoracic aperture, mid-chest, upper abdomen, mid-abdomen, pelvis and thigh; they were also applied to a 3D CT sequence of 72 slices using the three-dimensional extension of the algorithms. Time to generate the contours and their quality with respect to a reference model were determined. Manual input for a complete patient model required approximately 5 to 6 h for 72 CT slices (4.5 min/slice). If slight irregularities at object boundaries are accepted, this time can be reduced to 3.5 min/slice using the volume growing algorithm. However, generating a tetrahedron mesh from such a contour sequence for hyperthermia planning (the basis for finite-element algorithms) requires a significant amount of postediting. With the watershed algorithm extended to three dimensions, processing time can be further reduced to 3 min/slice while achieving satisfactory contour quality. Therefore, this method is currently regarded as offering some potential for efficient automated model generation in hyperthermia. In summary, the 3D volume growing algorithm and watershed transformation are both suitable for segmentation of even low-contrast objects. However, they are not

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. Anatomy of the Le Fort I segment: Are arterial variations a potential risk factor for avascular bone necrosis in Le Fort I osteotomies?

    Science.gov (United States)

    Bruneder, Simon; Wallner, Jürgen; Weiglein, Andreas; Kmečová, Ĺudmila; Egger, Jan; Pilsl, Ulrike; Zemann, Wolfgang

    2018-05-02

    Osteotomies of the Le Fort I segment are routine operations with low complication rates. Ischemic complications are rare, but can have severe consequences that may lead to avascular bone necrosis of the Le Fort I segment. Therefore the aim of this study was to investigate the blood supply and special arterial variants of the Le Fort I segment responsible for arterial hypoperfusion or ischemic avascular necrosis after surgery. The arterial anatomy of the Le Fort I segment's blood supply using 30 halved human cadaver head specimens was analyzed after complete dissection until the submicroscopic level. In all specimens the arterial variants of the Le Fort I segment and also the arterial diameters measured at two points were evaluated. The typical known vascularization pattern was apparent in 90% of all specimens, in which the ascending palatine (D1: 1,2 mm ± 0,34 mm; D2: 0,8 mm ± 0,34 mm) and ascending pharyngeal artery (D1: 1,3 mm ± 0,58 mm; D2: avascular segment necrosis after surgery. An individualized operation plan may prevent ischemic complications in at-risk patients. Copyright © 2018 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  12. An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms.

    Science.gov (United States)

    Fischer, Christoph; Domer, Benno; Wibmer, Thomas; Penzel, Thomas

    2017-03-01

    Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.

  13. 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

  14. 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.

  15. 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.

  16. Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing.

    Science.gov (United States)

    Thyreau, Benjamin; Sato, Kazunori; Fukuda, Hiroshi; Taki, Yasuyuki

    2018-01-01

    The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model. Deep convolutional neural networks (ConvNets) have shown great success in recent years, due to their ability to learn meaningful features from a mass of training data. Our method relies on the following key novelties: (i) we use a wide and variable training set coming from multiple cohorts (ii) our training labels come in part from the output of the FreeSurfer algorithm, and (iii) we include synthetic data and use a powerful data augmentation scheme. Our method proves to be robust, and it has fast inference (deep neural-network methods can easily encode, and even improve, existing anatomical knowledge, even when this knowledge exists in algorithmic form. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. 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.

  18. A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images.

    Science.gov (United States)

    Katouzian, Amin; Angelini, Elsa D; Carlier, Stéphane G; Suri, Jasjit S; Navab, Nassir; Laine, Andrew F

    2012-09-01

    Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

  19. Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters

    Science.gov (United States)

    Mueller, Jenna L.; Harmany, Zachary T.; Mito, Jeffrey K.; Kennedy, Stephanie A.; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G.; Willett, Rebecca M.; Brown, J. Quincy; Ramanujam, Nimmi

    2013-02-01

    The combination of fluorescent contrast agents with microscopy is a powerful technique to obtain real time images of tissue histology without the need for fixing, sectioning, and staining. The potential of this technology lies in the identification of robust methods for image segmentation and quantitation, particularly in heterogeneous tissues. Our solution is to apply sparse decomposition (SD) to monochrome images of fluorescently-stained microanatomy to segment and quantify distinct tissue types. The clinical utility of our approach is demonstrated by imaging excised margins in a cohort of mice after surgical resection of a sarcoma. Representative images of excised margins were used to optimize the formulation of SD and tune parameters associated with the algorithm. Our results demonstrate that SD is a robust solution that can advance vital fluorescence microscopy as a clinically significant technology.

  20. An improved K-means clustering algorithm in agricultural image segmentation

    Science.gov (United States)

    Cheng, Huifeng; Peng, Hui; Liu, Shanmei

    Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.

  1. A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation.

    Science.gov (United States)

    Pelet, S; Previte, M J R; Laiho, L H; So, P T C

    2004-10-01

    Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits. Copyright 2004 Biophysical Society

  2. Application of Multiobjective Genetic Algorithms in Anatomy Based Dose Optimization in Brachytherapy and its Comparation with Deterministic Algorithms

    National Research Council Canada - National Science Library

    Milickovic, Natasa

    2001-01-01

    In High Dose Rate (HDR) brachytherapy the conventional dose optimization algorithms consider the multiple objectives in the form of an aggregate function which combines individual objectives into a single utility value...

  3. A quality and efficiency analysis of the IMFASTTM segmentation algorithm in head and neck 'step and shoot' IMRT treatments

    International Nuclear Information System (INIS)

    Potter, Larry D.; Chang, Sha X.; Cullip, Timothy J.; Siochi, Alfredo C.

    2002-01-01

    The performance of segmentation algorithms used in IMFAST for 'step and shoot' IMRT treatment delivery is evaluated for three head and neck clinical treatments of different optimization objectives. The segmentation uses the intensity maps generated by the in-house TPS PLANUNC using the index-dose minimization algorithm. The dose optimization objectives include PTV dose uniformity and dose volume histogram-specified critical structure sparing. The optimized continuous intensity maps were truncated into five and ten intensity levels and exported to IMFAST for MLC segments optimization. The MLC segments were imported back to PLUNC for dose optimization quality calculation. The five basic segmentation algorithms included in IMFAST were evaluated alone and in combination with either tongue and groove/match line correction or fluence correction or both. Two criteria were used in the evaluation: treatment efficiency represented by the total number of MLC segments and optimization quality represented by a clinically relevant optimization quality factor. We found that the treatment efficiency depends first on the number of intensity levels used in the intensity map and second the segmentation technique used. The standard optimal segmentation with fluence correction is a consistent good performer for all treatment plans studied. All segmentation techniques evaluated produced treatments with similar dose optimization quality values, especially when ten-level intensity maps are used

  4. 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).

  5. A Novel Approach for Bi-Level Segmentation of Tuberculosis Bacilli Based on Meta-Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    AYAS, S.

    2018-02-01

    Full Text Available Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.

  6. 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.

  7. Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

    Science.gov (United States)

    de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

    2015-06-01

    Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

  8. Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video.

    Science.gov (United States)

    Guo, Yanhui; Jiang, Shuang-Quan; Sun, Baiqing; Siuly, Siuly; Şengür, Abdulkadir; Tian, Jia-Wei

    2017-12-01

    Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Then the anatomic structure is identified in thyroid ultrasound image. Finally the rendering colors on these anatomic regions are extracted and validated to find the frames which satisfy the selection criteria. To test the performance of the proposed method, a thyroid elastogram dataset is built and totally 33 cases were collected. An experienced radiologist manually evaluates the selection results of the proposed method. Experimental results demonstrate that the proposed method finds the qualified rendering frame with 100% accuracy. The proposed scheme assists the radiologists to diagnose the thyroid diseases using the qualified rendering images.

  9. Principle and realization of segmenting contour series algorithm in reverse engineering based on X-ray computerized tomography

    International Nuclear Information System (INIS)

    Wang Yanfang; Liu Li; Yan Yonglian; Shan Baoci; Tang Xiaowei

    2007-01-01

    A new algorithm of segmenting contour series of images is presented, which can achieve three dimension reconstruction with parametric recognition in Reverse Engineering based on X-ray CT. First, in order to get the nested relationship between contours, a method of a certain angle ray is used. Second, for realizing the contour location in one slice, another approach is presented to generate the contour tree by scanning the relevant vector only once. Last, a judge algorithm is put forward to accomplish the contour match between slices by adopting the qualitative and quantitative properties. The example shows that this algorithm can segment contour series of CT parts rapidly and precisely. (authors)

  10. CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

    Science.gov (United States)

    Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

    2010-03-01

    Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

  11. Shadow Detection from Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

    Science.gov (United States)

    Kadhim, N. M. S. M.; Mourshed, M.; Bray, M. T.

    2015-03-01

    Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of

  12. SHADOW DETECTION FROM VERY HIGH RESOLUTON SATELLITE IMAGE USING GRABCUT SEGMENTATION AND RATIO-BAND ALGORITHMS

    Directory of Open Access Journals (Sweden)

    N. M. S. M. Kadhim

    2015-03-01

    Full Text Available Very-High-Resolution (VHR satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour, the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates

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

    International Nuclear Information System (INIS)

    Hoven, Andor F. van den; Leeuwen, Maarten S. van; Lam, Marnix G. E. H.; Bosch, Maurice A. A. J. van den

    2015-01-01

    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

  14. 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)

  15. 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.

  16. Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model

    Science.gov (United States)

    Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.

    2009-04-01

    The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.

  17. 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.

  18. 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

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

    International Nuclear Information System (INIS)

    Polan, D; Brady, S; Kaufman, R

    2016-01-01

    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

  20. 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.

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

    Science.gov (United States)

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

    2013-09-01

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

  2. 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.

  3. 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.

  4. Functional segmentation of dynamic PET studies: Open source implementation and validation of a leader-follower-based algorithm.

    Science.gov (United States)

    Mateos-Pérez, José María; Soto-Montenegro, María Luisa; Peña-Zalbidea, Santiago; Desco, Manuel; Vaquero, Juan José

    2016-02-01

    We present a novel segmentation algorithm for dynamic PET studies that groups pixels according to the similarity of their time-activity curves. Sixteen mice bearing a human tumor cell line xenograft (CH-157MN) were imaged with three different (68)Ga-DOTA-peptides (DOTANOC, DOTATATE, DOTATOC) using a small animal PET-CT scanner. Regional activities (input function and tumor) were obtained after manual delineation of regions of interest over the image. The algorithm was implemented under the jClustering framework and used to extract the same regional activities as in the manual approach. The volume of distribution in the tumor was computed using the Logan linear method. A Kruskal-Wallis test was used to investigate significant differences between the manually and automatically obtained volumes of distribution. The algorithm successfully segmented all the studies. No significant differences were found for the same tracer across different segmentation methods. Manual delineation revealed significant differences between DOTANOC and the other two tracers (DOTANOC - DOTATATE, p=0.020; DOTANOC - DOTATOC, p=0.033). Similar differences were found using the leader-follower algorithm. An open implementation of a novel segmentation method for dynamic PET studies is presented and validated in rodent studies. It successfully replicated the manual results obtained in small-animal studies, thus making it a reliable substitute for this task and, potentially, for other dynamic segmentation procedures. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. 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.

  6. Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm

    International Nuclear Information System (INIS)

    Muehlenbruch, Georg; Das, Marco; Hohl, Christian; Wildberger, Joachim E.; Guenther, Rolf W.; Mahnken, Andreas H.; Rinck, Daniel; Flohr, Thomas G.; Koos, Ralf; Knackstedt, Christian

    2006-01-01

    The purpose was to evaluate a new semi-automated 3D region-growing segmentation algorithm for functional analysis of the left ventricle in multislice CT (MSCT) of the heart. Twenty patients underwent contrast-enhanced MSCT of the heart (collimation 16 x 0.75 mm; 120 kV; 550 mAseff). Multiphase image reconstructions with 1-mm axial slices and 8-mm short-axis slices were performed. Left ventricular volume measurements (end-diastolic volume, end-systolic volume, ejection fraction and stroke volume) from manually drawn endocardial contours in the short axis slices were compared to semi-automated region-growing segmentation of the left ventricle from the 1-mm axial slices. The post-processing-time for both methods was recorded. Applying the new region-growing algorithm in 13/20 patients (65%), proper segmentation of the left ventricle was feasible. In these patients, the signal-to-noise ratio was higher than in the remaining patients (3.2±1.0 vs. 2.6±0.6). Volume measurements of both segmentation algorithms showed an excellent correlation (all P≤0.0001); the limits of agreement for the ejection fraction were 2.3±8.3 ml. In the patients with proper segmentation the mean post-processing time using the region-growing algorithm was diminished by 44.2%. On the basis of a good contrast-enhanced data set, a left ventricular volume analysis using the new semi-automated region-growing segmentation algorithm is technically feasible, accurate and more time-effective. (orig.)

  7. 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

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

    International Nuclear Information System (INIS)

    Feng, Y; Olsen, J.; Parikh, P.; Noel, C; Wooten, H; Du, D; Mutic, S; Hu, Y; Kawrakow, I; Dempsey, J

    2014-01-01

    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

  9. 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

  10. Correction of oral contrast artifacts in CT-based attenuation correction of PET images using an automated segmentation algorithm

    International Nuclear Information System (INIS)

    Ahmadian, Alireza; Ay, Mohammad R.; Sarkar, Saeed; Bidgoli, Javad H.; Zaidi, Habib

    2008-01-01

    Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high-density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map (μmap), thus resulting in overcorrection for attenuation of positron emission tomography (PET) images. In this study, we developed an automated algorithm for segmentation and classification of regions containing oral contrast medium to correct for artifacts in CT-attenuation-corrected PET images using the segmented contrast correction (SCC) algorithm. The proposed algorithm consists of two steps: first, high CT number object segmentation using combined region- and boundary-based segmentation and second, object classification to bone and contrast agent using a knowledge-based nonlinear fuzzy classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers using the SCC algorithm. The generated CT images are then down-sampled followed by Gaussian smoothing to match the resolution of PET images. A piecewise calibration curve was then used to convert CT pixel values to linear attenuation coefficients at 511 keV. The visual assessment of segmented regions performed by an experienced radiologist confirmed the accuracy of the segmentation and classification algorithms for delineation of contrast-enhanced regions in clinical CT images. The quantitative analysis of generated μmaps of 21 clinical CT colonoscopy datasets showed an overestimation ranging between 24.4% and 37.3% in the 3D-classified regions depending on their volume and the concentration of contrast medium. Two PET/CT studies known to be problematic demonstrated the applicability of the technique in

  11. Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

    Science.gov (United States)

    Gao, Bin; Li, Xiaoqing; Woo, Wai Lok; Tian, Gui Yun

    2018-05-01

    Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

  12. A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

    Science.gov (United States)

    Li, Jing; Xie, Weixin; Pei, Jihong

    2018-03-01

    Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

  13. Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.

    Science.gov (United States)

    Thomson, David; Boylan, Chris; Liptrot, Tom; Aitkenhead, Adam; Lee, Lip; Yap, Beng; Sykes, Andrew; Rowbottom, Carl; Slevin, Nicholas

    2014-08-03

    The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT. Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA). For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency. Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

  14. Fast track segment finding in the Monitored Drift Tubes of the ATLAS Muon Spectrometer using a Legendre transform algorithm

    CERN Document Server

    Ntekas, Konstantinos; The ATLAS collaboration

    2018-01-01

    The upgrade of the ATLAS first-level muon trigger for High- Luminosity LHC foresees incorporating the precise tracking of the Monitored Drift Tubes in the current system based on Resistive Plate Chambers and Thin Gap Chambers to improve the accuracy in the transverse momentum measurement and control the single muon trigger rate by suppressing low quality fake triggers. The core of the MDT trigger algorithm is the segment identification and reconstruction which is performed per MDT chamber. The reconstructed segment positions and directions are then combined to extract the muon candidate’s transverse momentum. A fast pattern recognition segment finding algorithm, called the Legendre transform, is proposed to be used for the MDT trigger, implemented in a FPGA housed on a ATCA blade.

  15. Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation

    Science.gov (United States)

    Wang, Xuejuan; Wu, Shuhang; Liu, Yunpeng

    2018-04-01

    This paper presents a new method for wood defect detection. It can solve the over-segmentation problem existing in local threshold segmentation methods. This method effectively takes advantages of visual saliency and local threshold segmentation. Firstly, defect areas are coarsely located by using spectral residual method to calculate global visual saliency of them. Then, the threshold segmentation of maximum inter-class variance method is adopted for positioning and segmenting the wood surface defects precisely around the coarse located areas. Lastly, we use mathematical morphology to process the binary images after segmentation, which reduces the noise and small false objects. Experiments on test images of insect hole, dead knot and sound knot show that the method we proposed obtains ideal segmentation results and is superior to the existing segmentation methods based on edge detection, OSTU and threshold segmentation.

  16. 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.

  17. Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm.

    Science.gov (United States)

    Kamali, Tahereh; Stashuk, Daniel

    2016-10-01

    Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity. Copyright

  18. A region segmentation based algorithm for building a crystal position lookup table in a scintillation detector

    International Nuclear Information System (INIS)

    Wang Haipeng; Fan Xin; Yun Mingkai; Liu Shuangquan; Cao Xuexiang; Chai Pei; Shan Baoci

    2015-01-01

    In a scintillation detector, scintillation crystals are typically made into a 2-dimensional modular array. The location of incident gamma-ray needs be calibrated due to spatial response nonlinearity. Generally, position histograms-the characteristic flood response of scintillation detectors-are used for position calibration. In this paper, a position calibration method based on a crystal position lookup table which maps the inaccurate location calculated by Anger logic to the exact hitting crystal position has been proposed. Firstly, the position histogram is preprocessed, such as noise reduction and image enhancement. Then the processed position histogram is segmented into disconnected regions, and crystal marking points are labeled by finding the centroids of regions. Finally, crystal boundaries are determined and the crystal position lookup table is generated. The scheme is evaluated by the whole-body positron emission tomography (PET) scanner and breast dedicated single photon emission computed tomography scanner developed by the Institute of High Energy Physics, Chinese Academy of Sciences. The results demonstrate that the algorithm is accurate, efficient, robust and applicable to any configurations of scintillation detector. (authors)

  19. 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.

  20. 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.

  1. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography.

    Science.gov (United States)

    Kirişli, H A; Schaap, M; Metz, C T; Dharampal, A S; Meijboom, W B; Papadopoulou, S L; Dedic, A; Nieman, K; de Graaf, M A; Meijs, M F L; Cramer, M J; Broersen, A; Cetin, S; Eslami, A; Flórez-Valencia, L; Lor, K L; Matuszewski, B; Melki, I; Mohr, B; Oksüz, I; Shahzad, R; Wang, C; Kitslaar, P H; Unal, G; Katouzian, A; Örkisz, M; Chen, C M; Precioso, F; Najman, L; Masood, S; Ünay, D; van Vliet, L; Moreno, R; Goldenberg, R; Vuçini, E; Krestin, G P; Niessen, W J; van Walsum, T

    2013-12-01

    Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. 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.

  3. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images.

    Science.gov (United States)

    Karim, Rashed; Bhagirath, Pranav; Claus, Piet; James Housden, R; Chen, Zhong; Karimaghaloo, Zahra; Sohn, Hyon-Mok; Lara Rodríguez, Laura; Vera, Sergio; Albà, Xènia; Hennemuth, Anja; Peitgen, Heinz-Otto; Arbel, Tal; Gonzàlez Ballester, Miguel A; Frangi, Alejandro F; Götte, Marco; Razavi, Reza; Schaeffter, Tobias; Rhode, Kawal

    2016-05-01

    Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  4. 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.

  5. 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.

  6. 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.

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

    African Journals Online (AJOL)

    Segmentation (or partitioning) of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main streams of segmentation and examples exist where the application of these techniques improved the performance of ...

  8. Inverse planning anatomy-based dose optimization for HDR-brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function

    International Nuclear Information System (INIS)

    Lessard, Etienne; Pouliot, Jean

    2001-01-01

    An anatomy-based dose optimization algorithm is developed to automatically and rapidly produce a highly conformal dose coverage of the target volume while minimizing urethra, bladder, and rectal doses in the delivery of an high dose-rate (HDR) brachytherapy boost for the treatment of prostate cancer. The dwell times are optimized using an inverse planning simulated annealing algorithm (IPSA) governed entirely from the anatomy extracted from a CT and by a dedicated objective function (cost function) reflecting clinical prescription and constraints. With this inverse planning approach, the focus is on the physician's prescription and constraint instead of on the technical limitations. Consequently, the physician's control on the treatment is improved. The capacity of this algorithm to represent the physician's prescription is presented for a clinical prostate case. The computation time (CPU) for IPSA optimization is less than 1 min (41 s for 142 915 iterations) for a typical clinical case, allowing fast and practical dose optimization. The achievement of highly conformal dose coverage to the target volume opens the possibility to deliver a higher dose to the prostate without inducing overdosage of urethra and normal tissues surrounding the prostate. Moreover, using the same concept, it will be possible to deliver a boost dose to a delimited tumor volume within the prostate. Finally, this method can be easily extended to other anatomical sites

  9. [Plaque segmentation of intracoronary optical coherence tomography images based on K-means and improved random walk algorithm].

    Science.gov (United States)

    Wang, Guanglei; Wang, Pengyu; Han, Yechen; Liu, Xiuling; Li, Yan; Lu, Qian

    2017-06-01

    In recent years, optical coherence tomography (OCT) has developed into a popular coronary imaging technology at home and abroad. The segmentation of plaque regions in coronary OCT images has great significance for vulnerable plaque recognition and research. In this paper, a new algorithm based on K -means clustering and improved random walk is proposed and Semi-automated segmentation of calcified plaque, fibrotic plaque and lipid pool was achieved. And the weight function of random walk is improved. The distance between the edges of pixels in the image and the seed points is added to the definition of the weight function. It increases the weak edge weights and prevent over-segmentation. Based on the above methods, the OCT images of 9 coronary atherosclerotic patients were selected for plaque segmentation. By contrasting the doctor's manual segmentation results with this method, it was proved that this method had good robustness and accuracy. It is hoped that this method can be helpful for the clinical diagnosis of coronary heart disease.

  10. Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably

    DEFF Research Database (Denmark)

    Ashraf, Haseem; de Hoop, B; Shaker, S B

    2010-01-01

    We examined the reproducibility of lung nodule volumetry software that offers three different volumetry algorithms.......We examined the reproducibility of lung nodule volumetry software that offers three different volumetry algorithms....

  11. 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.

  12. 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.)

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

    International Nuclear Information System (INIS)

    Kurata, Akira; Kono, Atsushi; Coenen, Adriaan; Saru-Chelu, Raluca G.; Krestin, Gabriel P.; Sakamoto, Tsuyoshi; Kido, Teruhito; Mochizuki, Teruhito; Higashino, Hiroshi; Abe, Mitsunori; Feyter, Pim J. de; Nieman, Koen

    2015-01-01

    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.)

  14. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiao Ling

    2016-08-01

    Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

  15. 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 ...

  16. 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 ...

  17. 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 ...

  18. 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.

  19. Hemodynamic segmentation of brain perfusion images with delay and dispersion effects using an expectation-maximization algorithm.

    Directory of Open Access Journals (Sweden)

    Chia-Feng Lu

    Full Text Available Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.

  20. 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...

  1. Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA).

    Science.gov (United States)

    Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A; Hammel, Naama; Yang, Zhiyong; Weinreb, Robert N; Zangwill, Linda M

    2016-02-01

    We determined if the Bruch's membrane opening (BMO) location changes over time in healthy eyes and eyes with progressing glaucoma, and validated an automated segmentation algorithm for identifying the BMO in Cirrus high-definition coherence tomography (HD-OCT) images. We followed 95 eyes (35 progressing glaucoma and 60 healthy) for an average of 3.7 ± 1.1 years. A stable group of 50 eyes had repeated tests over a short period. In each B-scan of the stable group, the BMO points were delineated manually and automatically to assess the reproducibility of both segmentation methods. Moreover, the BMO location variation over time was assessed longitudinally on the aligned images in 3D space point by point in x, y, and z directions. Mean visual field mean deviation at baseline of the progressing glaucoma group was -7.7 dB. Mixed-effects models revealed small nonsignificant changes in BMO location over time for all directions in healthy eyes (the smallest P value was 0.39) and in the progressing glaucoma eyes (the smallest P value was 0.30). In the stable group, the overall intervisit-intraclass correlation coefficient (ICC) and coefficient of variation (CV) were 98.4% and 2.1%, respectively, for the manual segmentation and 98.1% and 1.9%, respectively, for the automated algorithm. Bruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression. The BMO location estimation with Cirrus HD-OCT using manual and automated segmentation showed excellent reproducibility.

  2. 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

  3. New segmentation-based tone mapping algorithm for high dynamic range image

    Science.gov (United States)

    Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

    2017-07-01

    The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

  4. 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

  5. A Fast Global Fitting Algorithm for Fluorescence Lifetime Imaging Microscopy Based on Image Segmentation

    OpenAIRE

    Pelet, S.; Previte, M.J.R.; Laiho, L.H.; So, P.T. C.

    2004-01-01

    Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained ana...

  6. 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.

  7. 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.

  8. 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.

  9. Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

    Science.gov (United States)

    Kurç, Tahsin M.; Taveira, Luís F. R.; Melo, Alba C. M. A.; Gao, Yi; Kong, Jun; Saltz, Joel H.

    2017-01-01

    Abstract Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Availability and Implementation: Source code: https://github.com/SBU-BMI/region-templates/. Contact: teodoro@unb.br Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28062445

  10. 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

  11. Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably.

    NARCIS (Netherlands)

    Ashraf, H.; Hoop, B.J. de; Shaker, S.B.; Dirksen, A.; Bach, K.S.; Hansen, H.; Prokop, M.; Pedersen, J.H.

    2010-01-01

    OBJECTIVE: We examined the reproducibility of lung nodule volumetry software that offers three different volumetry algorithms. METHODS: In a lung cancer screening trial, 188 baseline nodules >5 mm were identified. Including follow-ups, these nodules formed a study-set of 545 nodules. Nodules were

  12. A Quality-Aware Relay Node Placement Algorithm to Connect Disjoint WSN Segments with Topology Reorganized

    Directory of Open Access Journals (Sweden)

    Zheng Zimu

    2014-05-01

    Full Text Available Connecting numerous separate parts can be indispensable to simply WSNs that operate autonomously or partitions of a single segmented WSN. Linking them is subject to different intersegment quality of communication and service (QoC and QoS while relay nodes are deployed with their exact number and positions left to define, which is the problem that we focus on. Finding the optimal number and position of relays is NP-hard and therefore heuristics and approximation are pursued. This paper presents an effective approach to deploy appropriate relays to satisfy both the QoC and QoS requirements by introducing probabilistic topology control. The key idea is to regulate a new parallel rule and cost function for segments, which eventually maximizes the utilization of deployed node and avoids the deployment of additional relays as much as possible. The optimization problem is then mapped to finding the path that fulfills the both requirements together with least overheads. The experimental result is validated that although the number of relays is slightly increased to meet extra requirements, the network connectivity can be improved by up to 289.5 % with reorganized topology.

  13. 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

  14. Eye Anatomy

    Science.gov (United States)

    ... News About Us Donate In This Section Eye Anatomy en Español email Send this article to a ... You at Risk For Glaucoma? Childhood Glaucoma Eye Anatomy Five Common Glaucoma Tests Glaucoma Facts and Stats ...

  15. 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 ...

  16. Evolutionary Cellular Automata for Image Segmentation and Noise Filtering Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Sihem SLATNIA

    2011-01-01

    Full Text Available We use an evolutionary process to seek a specialized set of rules among a wide range of rules to be used by Cellular Automata (CA for a range of tasks,extracting edges in a given gray or colour image, noise filtering applied to black-white image. This is the best set of local rules determine the future state of CA in an asynchronous way. The Genetic Algorithm (GA is applied to search the best CA rules that can realize the best edge detection and noise filtering.

  17. Evolutionary Cellular Automata for Image Segmentation and Noise Filtering Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Okba Kazar

    2011-01-01

    Full Text Available We use an evolutionary process to seek a specialized set of rules among a wide range of rules to be used by Cellular Automata (CA for a range of tasks, extracting edges in a given gray or colour image, noise filtering applied to black-white image. This is the best set of local rules determine the future state of CA in an asynchronous way. The Genetic Algorithm (GA is applied to search the best CA rules that can realize the best edge detection and noise filtering.

  18. 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

  19. Hand Anatomy

    Science.gov (United States)

    ... All Topics A-Z Videos Infographics Symptom Picker Anatomy Bones Joints Muscles Nerves Vessels Tendons About Hand Surgery What is ... Hand Therapist? Media Find a Hand Surgeon Home Anatomy Bones Joints Muscles Nerves Vessels Tendons Anatomy The upper extremity is ...

  20. Comb-like optical transmission spectra generated from one-dimensional two-segment-connected two-material waveguide networks optimized by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yu [MOE Key Laboratory of Laser Life Science and Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631 (China); Yang, Xiangbo, E-mail: xbyang@scnu.edu.cn [MOE Key Laboratory of Laser Life Science and Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631 (China); School of Physical Education and Sports Science, South China Normal University, Guangzhou 510006 (China); Lu, Jian; Zhang, Guogang [MOE Key Laboratory of Laser Life Science and Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631 (China); Liu, Chengyi Timon [School of Physical Education and Sports Science, South China Normal University, Guangzhou 510006 (China)

    2014-03-01

    In this Letter, a one-dimensional (1D) two-segment-connected two-material waveguide network (TSCTMWN) is designed to produce comb-like frequency passbands, where each waveguide segment is composed of normal and anomalous dispersion materials and the length ratio of sub-waveguide segments is optimized by genetic algorithm (GA). It is found that 66 comb-like frequency passbands are created in the second frequency unit, maximal relative width difference of which is less than 2×10{sup −5}. It may be useful for the designing of dense wavelength division multiplexings (DWDMs) and multi-channel filters, etc., and provide new applications for GA.

  1. 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.

  2. 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

  3. 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

  4. 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

  5. Algorithms

    Indian Academy of Sciences (India)

    polynomial) division 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.

  6. Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms.

    Science.gov (United States)

    Rashno, Abdolreza; Koozekanani, Dara D; Drayna, Paul M; Nazari, Behzad; Sadri, Saeed; Rabbani, Hossein; Parhi, Keshab K

    2018-05-01

    This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.

  7. Segmentation algorithm for non-stationary compound Poisson processes. With an application to inventory time series of market members in a financial market

    Science.gov (United States)

    Tóth, B.; Lillo, F.; Farmer, J. D.

    2010-11-01

    We introduce an algorithm for the segmentation of a class of regime switching processes. The segmentation algorithm is a non parametric statistical method able to identify the regimes (patches) of a time series. The process is composed of consecutive patches of variable length. In each patch the process is described by a stationary compound Poisson process, i.e. a Poisson process where each count is associated with a fluctuating signal. The parameters of the process are different in each patch and therefore the time series is non-stationary. Our method is a generalization of the algorithm introduced by Bernaola-Galván, et al. [Phys. Rev. Lett. 87, 168105 (2001)]. We show that the new algorithm outperforms the original one for regime switching models of compound Poisson processes. As an application we use the algorithm to segment the time series of the inventory of market members of the London Stock Exchange and we observe that our method finds almost three times more patches than the original one.

  8. Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula

    International Nuclear Information System (INIS)

    Mera, David; Cotos, José M.; Varela-Pet, José; Garcia-Pineda, Oscar

    2012-01-01

    Highlights: ► We present an adaptive thresholding algorithm to segment oil spills. ► The segmentation algorithm is based on SAR images and wind field estimations. ► A Database of oil spill confirmations was used for the development of the algorithm. ► Wind field estimations have demonstrated to be useful for filtering look-alikes. ► Parallel programming has been successfully used to minimize processing time. - Abstract: Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the ocean’s surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time.

  9. 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.

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

    Science.gov (United States)

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

    2014-03-01

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

  11. 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

  12. High-resolution CISS MR imaging with and without contrast for evaluation of the upper cranial nerves: segmental anatomy and selected pathologic conditions of the cisternal through extraforaminal segments.

    Science.gov (United States)

    Blitz, Ari M; Macedo, Leonardo L; Chonka, Zachary D; Ilica, Ahmet T; Choudhri, Asim F; Gallia, Gary L; Aygun, Nafi

    2014-02-01

    The authors review the course and appearance of the major segments of the upper cranial nerves from their apparent origin at the brainstem through the proximal extraforaminal region, focusing on the imaging and anatomic features of particular relevance to high-resolution magnetic resonance imaging evaluation. Selected pathologic entities are included in the discussion of the corresponding cranial nerve segments for illustrative purposes. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. 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

  14. 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.

  15. Algorithms

    Indian Academy of Sciences (India)

    to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...

  16. Active Segmentation.

    Science.gov (United States)

    Mishra, Ajay; Aloimonos, Yiannis

    2009-01-01

    The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary.We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases 1 demonstrate the promise of the approach.

  17. 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

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

    International Nuclear Information System (INIS)

    Lu, Y; Chen, I; Kashani, R; Wan, H; Maughan, N; Muccigrosso, D; Parikh, P

    2016-01-01

    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.

  19. 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.

  20. The validation index: a new metric for validation of segmentation algorithms using two or more expert outlines with application to radiotherapy planning.

    Science.gov (United States)

    Juneja, Prabhjot; Evans, Philp M; Harris, Emma J

    2013-08-01

    Validation is required to ensure automated segmentation algorithms are suitable for radiotherapy target definition. In the absence of true segmentation, algorithmic segmentation is validated against expert outlining of the region of interest. Multiple experts are used to overcome inter-expert variability. Several approaches have been studied in the literature, but the most appropriate approach to combine the information from multiple expert outlines, to give a single metric for validation, is unclear. None consider a metric that can be tailored to case-specific requirements in radiotherapy planning. Validation index (VI), a new validation metric which uses experts' level of agreement was developed. A control parameter was introduced for the validation of segmentations required for different radiotherapy scenarios: for targets close to organs-at-risk and for difficult to discern targets, where large variation between experts is expected. VI was evaluated using two simulated idealized cases and data from two clinical studies. VI was compared with the commonly used Dice similarity coefficient (DSCpair - wise) and found to be more sensitive than the DSCpair - wise to the changes in agreement between experts. VI was shown to be adaptable to specific radiotherapy planning scenarios.

  1. New robust algorithm for tracking cells in videos of Drosophila morphogenesis based on finding an ideal path in segmented spatio-temporal cellular structures.

    Science.gov (United States)

    Bellaïche, Yohanns; Bosveld, Floris; Graner, François; Mikula, Karol; Remesíková, Mariana; Smísek, Michal

    2011-01-01

    In this paper, we present a novel algorithm for tracking cells in time lapse confocal microscopy movie of a Drosophila epithelial tissue during pupal morphogenesis. We consider a 2D + time video as a 3D static image, where frames are stacked atop each other, and using a spatio-temporal segmentation algorithm we obtain information about spatio-temporal 3D tubes representing evolutions of cells. The main idea for tracking is the usage of two distance functions--first one from the cells in the initial frame and second one from segmented boundaries. We track the cells backwards in time. The first distance function attracts the subsequently constructed cell trajectories to the cells in the initial frame and the second one forces them to be close to centerlines of the segmented tubular structures. This makes our tracking algorithm robust against noise and missing spatio-temporal boundaries. This approach can be generalized to a 3D + time video analysis, where spatio-temporal tubes are 4D objects.

  2. Facial anatomy.

    Science.gov (United States)

    Marur, Tania; Tuna, Yakup; Demirci, Selman

    2014-01-01

    Dermatologic problems of the face affect both function and aesthetics, which are based on complex anatomical features. Treating dermatologic problems while preserving the aesthetics and functions of the face requires knowledge of normal anatomy. When performing successfully invasive procedures of the face, it is essential to understand its underlying topographic anatomy. This chapter presents the anatomy of the facial musculature and neurovascular structures in a systematic way with some clinically important aspects. We describe the attachments of the mimetic and masticatory muscles and emphasize their functions and nerve supply. We highlight clinically relevant facial topographic anatomy by explaining the course and location of the sensory and motor nerves of the face and facial vasculature with their relations. Additionally, this chapter reviews the recent nomenclature of the branching pattern of the facial artery. © 2013 Elsevier Inc. All rights reserved.

  3. Transforming Anatomy

    OpenAIRE

    Hall, Anndee

    2017-01-01

    Abstract: Transforming Anatomy Studying historic books allows people to witness the transformation of the world right before their very eyes. The Bruxellensis Icones Anatomicae[1] by Andreas Vesalius is a vital piece of evidence in the movement from a more rudimentary understanding of the human body into the more complex and accurate development of modern anatomy. Vesalius’ research worked to both refute and confirm findings of his predecessor, the great historical Greek philosopher, Galen...

  4. 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.

  5. A New License Plate Segmentation Algorithm of Freight Train%一种新的货运列车车号分割算法

    Institute of Scientific and Technical Information of China (English)

    牛智慧; 赵歆波; 葛莉

    2014-01-01

    在对现有的货运列车车号分割算法及相关字符分割算法对比研究的基础上,文中提出并实现了一种新的货运列车车号分割算法。根据上下轮廓特征初步确定车号字符串图像的候选分割位置,然后根据字符尺寸比例和数字的弧特征,对断裂字符进行合并和对粘连字符进行再分割。该方法巧妙地避免了传统的投影分析分割法中处理粘连字符的难题,也避免了噪声对连通域的影响。与传统方法相比,具有较好的鲁棒性,达到了较高的精度和运行效率,为整个车号识别系统的精确性和稳定性提供了保障。%Through the comparative research of the existing segmentation algorithms of license plate and the related segmentation algo-rithms,a new segmentation algorithm of license plate of the freight train is proposed and implemented. According to the upper and lower contours of the license plate numbers,the algorithm yields a list of candidate segmentation locations,then merges the break characters and re-segments the touching characters depending on the character size ratio and arc features of numbers. This method neatly avoids the nightmare of segmentation of the touching characters in the traditional projection analysis,and also avoids the impact of noise on the con-nected fields. Compared with traditional methods,this method has better robustness,achieving a higher accuracy and efficiency and provi-ding a guarantee of accuracy and stability of license plate recognition system.

  6. Algorithms

    Indian Academy of Sciences (India)

    ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...

  7. Improving cerebellar segmentation with statistical fusion

    Science.gov (United States)

    Plassard, Andrew J.; Yang, Zhen; Prince, Jerry L.; Claassen, Daniel O.; Landman, Bennett A.

    2016-03-01

    The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multiatlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non- Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

  8. Regulatory Anatomy

    DEFF Research Database (Denmark)

    Hoeyer, Klaus

    2015-01-01

    This article proposes the term “safety logics” to understand attempts within the European Union (EU) to harmonize member state legislation to ensure a safe and stable supply of human biological material for transplants and transfusions. With safety logics, I refer to assemblages of discourses, le...... they arise. In short, I expose the regulatory anatomy of the policy landscape....

  9. Stedets Anatomi

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    Titlen på denne ph.d.-afhandling, Stedets Anatomi – en teoretisk undersøgelse af stedets og rumlighedens betydning for leg, computerspil og læring, skitserer ikke kun afhandlingens teoretiske dimensionering, men også dens analytiske bliks tematik i forbindelse med undersøgelsen af fænomenerne leg...

  10. Volumetric visualization of anatomy for treatment planning

    International Nuclear Information System (INIS)

    Pelizzari, Charles A.; Grzeszczuk, Robert; Chen, George T. Y.; Heimann, Ruth; Haraf, Daniel J.; Vijayakumar, Srinivasan; Ryan, Martin J.

    1996-01-01

    Purpose: Delineation of volumes of interest for three-dimensional (3D) treatment planning is usually performed by contouring on two-dimensional sections. We explore the usage of segmentation-free volumetric rendering of the three-dimensional image data set for tumor and normal tissue visualization. Methods and Materials: Standard treatment planning computed tomography (CT) studies, with typically 5 to 10 mm slice thickness, and spiral CT studies with 3 mm slice thickness were used. The data were visualized using locally developed volume-rendering software. Similar to the method of Drebin et al., CT voxels are automatically assigned an opacity and other visual properties (e.g., color) based on a probabilistic classification into tissue types. Using volumetric compositing, a projection into the opacity-weighted volume is produced. Depth cueing, perspective, and gradient-based shading are incorporated to achieve realistic images. Unlike surface-rendered displays, no hand segmentation is required to produce detailed renditions of skin, muscle, or bony anatomy. By suitable manipulation of the opacity map, tissue classes can be made transparent, revealing muscle, vessels, or bone, for example. Manually supervised tissue masking allows irrelevant tissues overlying tumors or other structures of interest to be removed. Results: Very high-quality renditions are produced in from 5 s to 1 min on midrange computer workstations. In the pelvis, an anteroposterior (AP) volume rendered view from a typical planning CT scan clearly shows the skin and bony anatomy. A muscle opacity map permits clear visualization of the superficial thigh muscles, femoral veins, and arteries. Lymph nodes are seen in the femoral triangle. When overlying muscle and bone are cut away, the prostate, seminal vessels, bladder, and rectum are seen in 3D perspective. Similar results are obtained for thorax and for head and neck scans. Conclusion: Volumetric visualization of anatomy is useful in treatment

  11. Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

    OpenAIRE

    Allam, Ali; Youssif, Aliaa; Ghalwash, Atef

    2015-01-01

    Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently,...

  12. 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-Ripa, Maria; Schoos, Mikkel Malby

    2017-01-01

    BACKGROUND: The acuteness score (based on the modified Anderson-Wilkins score) estimates the acuteness of ischemia based on ST-segment, Q-wave and T-wave measurements obtained from the electrocardiogram (ECG) in patients with ST Elevation Myocardial Infarction (STEMI). The score (range 1 (least...... the acuteness score. METHODS: We scored 50 pre-hospital ECGs from STEMI patients, manually and by the automated algorithm. We assessed the reliability test between the manual and automated algorithm by interclass correlation coefficient (ICC) and Bland-Altman plot. RESULTS: The ICC was 0.84 (95% CI 0.......72-0.91), PECGs, all within the upper (1.46) and lower (-1.12) limits...

  13. Algorithms

    Indian Academy of Sciences (India)

    algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).

  14. Algorithms

    Indian Academy of Sciences (India)

    algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...

  15. 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.

  16. 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.…

  17. Algorithms

    Indian Academy of Sciences (India)

    will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...

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

    Directory of Open Access Journals (Sweden)

    Jose Martin Carreira

    2013-04-01

    Full Text Available 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.

  19. Thymus Gland Anatomy

    Science.gov (United States)

    ... historical Searches are case-insensitive Thymus Gland, Adult, Anatomy Add to My Pictures View /Download : Small: 720x576 ... Large: 3000x2400 View Download Title: Thymus Gland, Adult, Anatomy Description: Anatomy of the thymus gland; drawing shows ...

  20. Normal Female Reproductive Anatomy

    Science.gov (United States)

    ... historical Searches are case-insensitive Reproductive System, Female, Anatomy Add to My Pictures View /Download : Small: 720x756 ... Large: 3000x3150 View Download Title: Reproductive System, Female, Anatomy Description: Anatomy of the female reproductive system; drawing ...

  1. Normal Pancreas Anatomy

    Science.gov (United States)

    ... hyphen, e.g. -historical Searches are case-insensitive Pancreas Anatomy Add to My Pictures View /Download : Small: ... 1586x1534 View Download Large: 3172x3068 View Download Title: Pancreas Anatomy Description: Anatomy of the pancreas; drawing shows ...

  2. Speaker segmentation and clustering

    OpenAIRE

    Kotti, M; Moschou, V; Kotropoulos, C

    2008-01-01

    07.08.13 KB. Ok to add the accepted version to Spiral, Elsevier says ok whlile mandate not enforced. This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker...

  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. 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.

  5. 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.

  6. GPU-based relative fuzzy connectedness image segmentation

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-01-15

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

  7. GPU-based relative fuzzy connectedness image segmentation.

    Science.gov (United States)

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

    2013-01-01

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

  8. 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

  9. 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

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

    International Nuclear Information System (INIS)

    Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu Jianwu; Hori, Masatoshi

    2010-01-01

    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 completion time

  11. FPGA Implementation of Gaussian Mixture Model Algorithm for 47 fps Segmentation of 1080p Video

    Directory of Open Access Journals (Sweden)

    Mariangela Genovese

    2013-01-01

    Full Text Available Circuits and systems able to process high quality video in real time are fundamental in nowadays imaging systems. The circuit proposed in the paper, aimed at the robust identification of the background in video streams, implements the improved formulation of the Gaussian Mixture Model (GMM algorithm that is included in the OpenCV library. An innovative, hardware oriented, formulation of the GMM equations, the use of truncated binary multipliers, and ROM compression techniques allow reduced hardware complexity and increased processing capability. The proposed circuit has been designed having commercial FPGA devices as target and provides speed and logic resources occupation that overcome previously proposed implementations. The circuit, when implemented on Virtex6 or StratixIV, processes more than 45 frame per second in 1080p format and uses few percent of FPGA logic resources.

  12. 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...

  13. 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)

  14. 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...

  15. Variability and reliability of the vastus lateralis muscle anatomy.

    Science.gov (United States)

    D'Arpa, Salvatore; Toia, Francesca; Brenner, Erich; Melloni, Carlo; Moschella, Francesco; Cordova, Adriana

    2016-08-01

    The aims of this study are to investigate the variability of the morphological and neurovascular anatomy of the vastus lateralis (VL) muscle and to describe the relationships among its intramuscular partitions and with the other muscles of the quadriceps femoris. Clinical implications in its reliability as a flap donor are also discussed. In 2012, the extra- and intramuscular neurovascular anatomy of the VL was investigated in 10 cadaveric lower limbs. In three specimens, the segmental arterial pedicles were injected with latex of different colors to point out their anastomotic connections. The morphological anatomy was investigated with regard to the mutual relationship of the three muscular partitions and the relation of the VL with the other muscles of the quadriceps femoris. The VL has a segmental morphological anatomy. However, the fibers of its three partitions interconnect individually and with the other bellies of the quadriceps femoris, particularly, in several variable portions with the vastus intermedius and mainly in the posterior part of the VL. The lateral circumflex femoral artery and its branches have variable origin, but demonstrate constant segmental distribution. Intramuscular dissection and colored latex injections show a rich anastomotic vascular network among the three partitions. Moderate variability exists in both the myological and the neurovascular anatomy of the VL. Despite this variability, the anatomy of the VL always has a constant segmental pattern, which makes the VL a reliable flap donor. Detailed knowledge of the VL anatomy could have useful applications in a broad clinical field.

  16. Multi-atlas segmentation for abdominal organs with Gaussian mixture models

    Science.gov (United States)

    Burke, Ryan P.; Xu, Zhoubing; Lee, Christopher P.; Baucom, Rebeccah B.; Poulose, Benjamin K.; Abramson, Richard G.; Landman, Bennett A.

    2015-03-01

    Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid / gray matter / white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an a posteriori framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.

  17. Anatomy Journal of Africa

    African Journals Online (AJOL)

    PROMOTING ACCESS TO AFRICAN RESEARCH. AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search · USING AJOL · RESOURCES ... Anatomy Journal of Africa is the Official Journal for the Association of Anatomical Societies of Africa. ... Applied anatomy - Clinical anatomy - Morphology, - Embryology ...

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

    Science.gov (United States)

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

    2010-09-01

    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.

  19. 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.)

  20. 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.)

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

    Science.gov (United States)

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

    2014-03-01

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

  2. Application of hybrid techniques (self-organizing map and fuzzy algorithm) using backscatter data for segmentation and fine-scale roughness characterization of seepage-related seafloor along the western continental margin of India

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Menezes, A.A.A.; Dandapath, S.; Fernandes, W.A.; Karisiddaiah, S.M.; Haris, K.; Gokul, G.S.

    density. 2 I. INTRODUCTION Echo-sounding systems, single beam (SBES) and multi-beam (MBES), allow coincident acquisition of high-resolution seafloor backscatter and bathymetric data [1], [2], which enormously sustains the marine exploration..., the SOM can be utilized to formulate a decision regarding the number of data classes during the online data acquisition, that are then used as an input to the fuzzy C-means (FCM) algorithms for data segmentation [12]. The FCM will require initial...

  3. Pancreas and cyst segmentation

    Science.gov (United States)

    Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

    2016-03-01

    Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

  4. 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.

  5. 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...

  6. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.

    Directory of Open Access Journals (Sweden)

    Yong He

    Full Text Available Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1 concave points clustering to determine the seed points of touching cells; and 2 random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.

  7. Fluence map segmentation

    International Nuclear Information System (INIS)

    Rosenwald, J.-C.

    2008-01-01

    The lecture addressed the following topics: 'Interpreting' the fluence map; The sequencer; Reasons for difference between desired and actual fluence map; Principle of 'Step and Shoot' segmentation; Large number of solutions for given fluence map; Optimizing 'step and shoot' segmentation; The interdigitation constraint; Main algorithms; Conclusions on segmentation algorithms (static mode); Optimizing intensity levels and monitor units; Sliding window sequencing; Synchronization to avoid the tongue-and-groove effect; Accounting for physical characteristics of MLC; Importance of corrections for leaf transmission and offset; Accounting for MLC mechanical constraints; The 'complexity' factor; Incorporating the sequencing into optimization algorithm; Data transfer to the treatment machine; Interface between R and V and accelerator; and Conclusions on fluence map segmentation (Segmentation is part of the overall inverse planning procedure; 'Step and Shoot' and 'Dynamic' options are available for most TPS (depending on accelerator model; The segmentation phase tends to come into the optimization loop; The physical characteristics of the MLC have a large influence on final dose distribution; The IMRT plans (MU and relative dose distribution) must be carefully validated). (P.A.)

  8. An Interval Type-2 Fuzzy C-Means algorithm for image segmentation%区间二型模糊C均值聚类在图像分割中的应用

    Institute of Scientific and Technical Information of China (English)

    邱存勇; 肖建

    2011-01-01

    Cluster analysis is an important branch of non-supervision pattern recognition, and Fuzzy C-Means(FCM) algorithm is a classic algorithm in cluster analysis. However, FCM is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in data and algorithm itself. This paper introduces the Interval Type-2 Fuzzy C-Means(IT2FCM) algorithm, whose core is type-2 fuzzy set that has better performance on handling uncertainties than Type-1 fuzzy set. IT2FCM and FCM are used for image segmentation to compare their segmentation results. The experiment shows that IT2FCM has better performance on suppressing noise and better effects on segmenting images compared with FCM.%聚类分析是非监督模式识别的重要分支,模糊C均值聚类算法(FCM)是其中的一类经典算法,然而该算法以一型模糊集为基础,无法处理数据集以及算法中的不确定性,为此引入区间二型模糊C均值聚类算法(IT2FCM).二型模糊集处理不确定性的能力强于一型模糊集,基于二型模糊集的IT2FCM在处理不确定性时效果优于FCM算法.文章以图像分割为应用对象,比较IT2FCM和FCM算法的分割效果,实验证明IT2FCM较传统FCM有更好的抗噪性.

  9. Teaching Anatomy: need or taste?

    OpenAIRE

    Farrokhi, Ahmad; Nejad, Masoume Soleymani

    2017-01-01

    Abstract Background: Anatomy is one of the core sections of Basic Medical Sciences. Given the central role of anatomy, the development of medical knowledge and reach new horizons in science is not possible without relying on anatomy. Since in the anatomy science, students are familiar with the basic terms of medical language, the anatomy's hard to know and have a negative attitude towards this course. With these conditions, anatomy professors have an important role in providing incentives...

  10. 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.

  11. A comparison of several cluster algorithms on artificial binary data [Part 2]. Scenarios from travel market segmentation. Part 2 (Addition to Working Paper No. 7).

    OpenAIRE

    Dolnicar, Sara; Leisch, Friedrich; Steiner, Gottfried; Weingessel, Andreas

    1998-01-01

    The search for clusters in empirical data is an important and often encountered research problem. Numerous algorithms exist that are able to render groups of objects or individuals. Of course each algorithm has its strengths and weaknesses. In order to identify these crucial points artificial data was generated - based primarily on experience with structures of empirical data - and used as benchmark for evaluating the results of numerous cluster algorithms. This work is an addition to SFB Wor...

  12. 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…

  13. 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...

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. Applied peritoneal anatomy

    International Nuclear Information System (INIS)

    Patel, R.R.; Planche, K.

    2013-01-01

    The peritoneal cavity is a complex anatomical structure with multiple attachments and connections. These are better understood with reference to the embryological development of this region. Armed with this knowledge, the diagnosis and assessment of a wide range of common intra-abdominal diseases becomes straightforward. This article will review and simplify the terminology, complex embryological development, and anatomy of the peritoneum, peritoneal attachments, and the reflections forming the peritoneal boundaries. Normal anatomy will be described using schematic diagrams with corresponding computed tomography (CT) and magnetic resonance imaging (MRI) images, including CT peritoneograms. The relevance of intra- and extra-peritoneal anatomy to common pathological processes will be demonstrated

  19. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation

    International Nuclear Information System (INIS)

    Daisne, Jean-François; Blumhofer, Andreas

    2013-01-01

    Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for “manual to automatic” and “manual to corrected” volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert

  20. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

    Science.gov (United States)

    Daisne, Jean-François; Blumhofer, Andreas

    2013-06-26

    Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

  1. 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…

  2. Anatomy of Memory

    OpenAIRE

    J Gordon Millichap

    1991-01-01

    Studies of the anatomy and function of the brain system for memory in humans and animal models are reviewed from the Veterans Affairs Medical Center, San Diego and the Department of Psychiatry, University of California, San Diego, La Jolla, CA.

  3. 3D segmentation of kidney tumors from freehand 2D ultrasound

    Science.gov (United States)

    Ahmad, Anis; Cool, Derek; Chew, Ben H.; Pautler, Stephen E.; Peters, Terry M.

    2006-03-01

    To completely remove a tumor from a diseased kidney, while minimizing the resection of healthy tissue, the surgeon must be able to accurately determine its location, size and shape. Currently, the surgeon mentally estimates these parameters by examining pre-operative Computed Tomography (CT) images of the patient's anatomy. However, these images do not reflect the state of the abdomen or organ during surgery. Furthermore, these images can be difficult to place in proper clinical context. We propose using Ultrasound (US) to acquire images of the tumor and the surrounding tissues in real-time, then segmenting these US images to present the tumor as a three dimensional (3D) surface. Given the common use of laparoscopic procedures that inhibit the range of motion of the operator, we propose segmenting arbitrarily placed and oriented US slices individually using a tracked US probe. Given the known location and orientation of the US probe, we can assign 3D coordinates to the segmented slices and use them as input to a 3D surface reconstruction algorithm. We have implemented two approaches for 3D segmentation from freehand 2D ultrasound. Each approach was evaluated on a tissue-mimicking phantom of a kidney tumor. The performance of our approach was determined by measuring RMS surface error between the segmentation and the known gold standard and was found to be below 0.8 mm.

  4. [Laurentius on anatomy].

    Science.gov (United States)

    Sawai, Tadashi; Sakai, Tatsuo

    2005-03-01

    Andreas Laurentius wrote Opera anatomica (1593) and Historia anatomica (1600). These books were composed of two types of chapters; 'historia' and 'quaestio'. His description is not original, but take from other anatomists. 'Historia' describes the structure, action and usefulness of the body parts clarified after dissection. 'Quaestio' treats those questions which could not be solved only by dissection. Laurentius cited many previous contradicting interpretations to these questions and choose a best interpretation for the individual questions. In most cases, Laurentius preferred Galen's view. Historia anatomica retained almost all the 'historia' and 'quaestio' from Opera anatomica, and added some new 'historia' and 'quaestio', especially in regard to the components of the body, such as ligaments, membranes, vessels, nerves and glands. Other new 'historia' and 'quaestio' in Historia anatomica concerned several topics on anatomy in general to comprehensively analyze the history of anatomy, methods of anatomy, and usefulness of anatomy. Historia anatomica reviewed what was anatomy by describing in 'historia' what was known and in 'quaestio' what was unresolved. Till now Laurentius's anatomical works have attracted little attention because his description contained few original findings and depended on previous books. However, the important fact that Historia anatomica was very popular in the 17th century tells us that people needed non-original and handbook style of this textbook. Historia anatomica is important for further research on the propagation of anatomical knowledge from professional anatomists to non-professionals in the 17th century.

  5. 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.

  6. 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

  7. 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%.

  8. 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

  9. Henry Gray's Anatomy.

    Science.gov (United States)

    Pearce, J M S

    2009-04-01

    Little is generally known of Henry Gray, the author of Gray's Anatomy, and even less of his colleague Henry Vandyke Carter, who played a vital role in the dissections and illustrations leading to the production of the first volume in 1859. This essay attempts to sketch briefly the salient, know aspects of these two men and their divergent careers. It traces succinctly the subsequent fate of the unique anatomy book that has influenced and instructed almost every student of medicine. (c) 2009 Wiley-Liss, Inc.

  10. Anatomy of the clitoris.

    Science.gov (United States)

    O'Connell, Helen E; Sanjeevan, Kalavampara V; Hutson, John M

    2005-10-01

    We present a comprehensive account of clitoral anatomy, including its component structures, neurovascular supply, relationship to adjacent structures (the urethra, vagina and vestibular glands, and connective tissue supports), histology and immunohistochemistry. We related recent anatomical findings to the historical literature to determine when data on accurate anatomy became available. An extensive review of the current and historical literature was done. The studies reviewed included dissection and microdissection, magnetic resonance imaging (MRI), 3-dimensional sectional anatomy reconstruction, histology and immunohistochemical studies. The clitoris is a multiplanar structure with a broad attachment to the pubic arch and via extensive supporting tissue to the mons pubis and labia. Centrally it is attached to the urethra and vagina. Its components include the erectile bodies (paired bulbs and paired corpora, which are continuous with the crura) and the glans clitoris. The glans is a midline, densely neural, non-erectile structure that is the only external manifestation of the clitoris. All other components are composed of erectile tissue with the composition of the bulbar erectile tissue differing from that of the corpora. The clitoral and perineal neurovascular bundles are large, paired terminations of the pudendal neurovascular bundles. The clitoral neurovascular bundles ascend along the ischiopubic rami to meet each other and pass along the superior surface of the clitoral body supplying the clitoris. The neural trunks pass largely intact into the glans. These nerves are at least 2 mm in diameter even in infancy. The cavernous or autonomic neural anatomy is microscopic and difficult to define consistently. MRI complements dissection studies and clarifies the anatomy. Clitoral pharmacology and histology appears to parallel those of penile tissue, although the clinical impact is vastly different. Typical textbook descriptions of the clitoris lack detail and

  11. Detailed Vascular Anatomy of the Human Retina by Projection-Resolved Optical Coherence Tomography Angiography

    Science.gov (United States)

    Campbell, J. P.; Zhang, M.; Hwang, T. S.; Bailey, S. T.; Wilson, D. J.; Jia, Y.; Huang, D.

    2017-02-01

    Optical coherence tomography angiography (OCTA) is a noninvasive method of 3D imaging of the retinal and choroidal circulations. However, vascular depth discrimination is limited by superficial vessels projecting flow signal artifact onto deeper layers. The projection-resolved (PR) OCTA algorithm improves depth resolution by removing projection artifact while retaining in-situ flow signal from real blood vessels in deeper layers. This novel technology allowed us to study the normal retinal vasculature in vivo with better depth resolution than previously possible. Our investigation in normal human volunteers revealed the presence of 2 to 4 distinct vascular plexuses in the retina, depending on location relative to the optic disc and fovea. The vascular pattern in these retinal plexuses and interconnecting layers are consistent with previous histologic studies. Based on these data, we propose an improved system of nomenclature and segmentation boundaries for detailed 3-dimensional retinal vascular anatomy by OCTA. This could serve as a basis for future investigation of both normal retinal anatomy, as well as vascular malformations, nonperfusion, and neovascularization.

  12. 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.

  13. Polarimetric Segmentation Using Wishart Test Statistic

    DEFF Research Database (Denmark)

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

    2002-01-01

    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......) 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...

  14. 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

  15. Fully automated reconstruction of three-dimensional vascular tree structures from two orthogonal views using computational algorithms and productionrules

    Science.gov (United States)

    Liu, Iching; Sun, Ying

    1992-10-01

    A system for reconstructing 3-D vascular structure from two orthogonally projected images is presented. The formidable problem of matching segments between two views is solved using knowledge of the epipolar constraint and the similarity of segment geometry and connectivity. The knowledge is represented in a rule-based system, which also controls the operation of several computational algorithms for tracking segments in each image, representing 2-D segments with directed graphs, and reconstructing 3-D segments from matching 2-D segment pairs. Uncertain reasoning governs the interaction between segmentation and matching; it also provides a framework for resolving the matching ambiguities in an iterative way. The system was implemented in the C language and the C Language Integrated Production System (CLIPS) expert system shell. Using video images of a tree model, the standard deviation of reconstructed centerlines was estimated to be 0.8 mm (1.7 mm) when the view direction was parallel (perpendicular) to the epipolar plane. Feasibility of clinical use was shown using x-ray angiograms of a human chest phantom. The correspondence of vessel segments between two views was accurate. Computational time for the entire reconstruction process was under 30 s on a workstation. A fully automated system for two-view reconstruction that does not require the a priori knowledge of vascular anatomy is demonstrated.

  16. 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...

  17. 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…

  18. 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.

  19. Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance.

    Science.gov (United States)

    Sjögren, Jane; Ubachs, Joey F A; Engblom, Henrik; Carlsson, Marcus; Arheden, Håkan; Heiberg, Einar

    2012-01-31

    T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p Segment MaR, -2.3 ± 4.9%, R = 0.91 (p Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2

  20. 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.

  1. 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…

  2. 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…

  3. 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…

  4. 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.

  5. Three-dimensional segmentation of the tumor mass in computed tomographic images of neuroblastoma

    Science.gov (United States)

    Deglint, Hanford J.; Rangayyan, Rangaraj M.; Boag, Graham S.

    2004-05-01

    Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, fibrosis, and normal tissue are often intermixed. Rather than attempt to separate these tissue types into distinct regions, we propose to explore methods to delineate the normal structures expected in abdominal CT images, remove them from further consideration, and examine the remaining parts of the images for the tumor mass. We explore the use of fuzzy connectivity for this purpose. Expert knowledge provided by the radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are also incorporated in the segmentation algorithm. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to chemotherapy and in the planning of delayed surgery for resection of the tumor. The performance of the algorithm is evaluated using cases acquired from the Alberta Children's Hospital.

  6. 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

  7. 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.

  8. 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.

  9. Hyperspectral image segmentation of the common bile duct

    Science.gov (United States)

    Samarov, Daniel; Wehner, Eleanor; Schwarz, Roderich; Zuzak, Karel; Livingston, Edward

    2013-03-01

    Over the course of the last several years hyperspectral imaging (HSI) has seen increased usage in biomedicine. Within the medical field in particular HSI has been recognized as having the potential to make an immediate impact by reducing the risks and complications associated with laparotomies (surgical procedures involving large incisions into the abdominal wall) and related procedures. There are several ongoing studies focused on such applications. Hyperspectral images were acquired during pancreatoduodenectomies (commonly referred to as Whipple procedures), a surgical procedure done to remove cancerous tumors involving the pancreas and gallbladder. As a result of the complexity of the local anatomy, identifying where the common bile duct (CBD) is can be difficult, resulting in comparatively high incidents of injury to the CBD and associated complications. It is here that HSI has the potential to help reduce the risk of such events from happening. Because the bile contained within the CBD exhibits a unique spectral signature, we are able to utilize HSI segmentation algorithms to help in identifying where the CBD is. In the work presented here we discuss approaches to this segmentation problem and present the results.

  10. 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.

  11. Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance

    Directory of Open Access Journals (Sweden)

    Sjögren Jane

    2012-01-01

    Full Text Available Abstract Background T2-weighted cardiovascular magnetic resonance (CMR has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR, after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD, full width half maximum intensity (FWHM or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Methods Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. Results MaR was 32.9 ± 10.9% of left ventricular mass (LVM when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p Conclusions There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a

  12. Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures.

    Science.gov (United States)

    Manzke, Robert; Meyer, Carsten; Ecabert, Olivier; Peters, Jochen; Noordhoek, Niels J; Thiagalingam, Aravinda; Reddy, Vivek Y; Chan, Raymond C; Weese, Jürgen

    2010-02-01

    Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3

  13. Document segmentation via oblique cuts

    Science.gov (United States)

    Svendsen, Jeremy; Branzan-Albu, Alexandra

    2013-01-01

    This paper presents a novel solution for the layout segmentation of graphical elements in Business Intelligence documents. We propose a generalization of the recursive X-Y cut algorithm, which allows for cutting along arbitrary oblique directions. An intermediate processing step consisting of line and solid region removal is also necessary due to presence of decorative elements. The output of the proposed segmentation is a hierarchical structure which allows for the identification of primitives in pie and bar charts. The algorithm was tested on a database composed of charts from business documents. Results are very promising.

  14. The anatomy workbook

    International Nuclear Information System (INIS)

    Hagen-Ansert, S.L.

    1986-01-01

    This is an atlas of human anatomy presented in the form of line drawings, many of which correspond to imaging planes used in ultrasound (US), computed tomography (CT), and magnetic resonance (MR). The book is organized into 17 sections, each covering a specific structure or organ system. Large, uncluttered drawings are labeled for identification of structures of interest. Many illustrations include captions consisting of comments explaining major divisions within organs, specific anatomic relationships and landmarks, and pertinent vascular anatomy. Most organs are first depicted in isolation or in relation to important adjacent organs or blood vessels and are rendered as if viewed from anterior, posterior, inferior, or superior perspectives. The organs are demonstrated again in serial transverse, saggital, and coronal sections, each accompanied by a drawing of a body in anatomic position denoting the plane of the section

  15. 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.

  16. Anatomy of the cerebellopontine angle; Anatomie des Kleinhirnbrueckenwinkels

    Energy Technology Data Exchange (ETDEWEB)

    Grunwald, I.Q.; Papanagiotou, P.; Politi, M.; Reith, W. [Universitaetsklinikum des Saarlandes, Homburg/Saar (Germany). Klinik fuer Diagnostische und Interventionelle Neuroradiologie; Nabhan, A. [Universitaetsklinikum des Saarlandes, Homburg/Saar (Germany). Neurochirurgische Klinik

    2006-03-15

    The cerebellopontine angle (CPA) is an anatomically complex region of the brain. In this article we describe the anatomy of the CPA cisterns, of the internal auditory canal, the topography of the cerebellum and brainstem, and the neurovascular structures of this area. (orig.) [German] Der Kleinhirnbrueckenwinkel ist eine umschriebene anatomische Region. Im diesem Artikel werden die Subarachnoidalraeume im Kleinhirnbrueckenwinkel, die Anatomie der Felsenbeinflaeche, Anatomie und Topographie des Kleinhirns und des Hirnstamms, die arteriellen Beziehungen und venoese Drainage des Kleinhirnbrueckenwinkels besprochen. (orig.)

  17. Human ocular anatomy.

    Science.gov (United States)

    Kels, Barry D; Grzybowski, Andrzej; Grant-Kels, Jane M

    2015-01-01

    We review the normal anatomy of the human globe, eyelids, and lacrimal system. This contribution explores both the form and function of numerous anatomic features of the human ocular system, which are vital to a comprehensive understanding of the pathophysiology of many oculocutaneous diseases. The review concludes with a reference glossary of selective ophthalmologic terms that are relevant to a thorough understanding of many oculocutaneous disease processes. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Mixed segmentation

    DEFF Research Database (Denmark)

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

    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...

  19. 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.

  20. Correlation of computed tomography, sonography, and gross anatomy of the liver

    International Nuclear Information System (INIS)

    Sexton, C.F.; Zeman, R.K.

    1983-01-01

    Although the vascular and segmental anatomy of the liver is well defined in the sonographic and computed tomographic (CT) literature, the three-dimensional relations of hepatic structures remain conceptually complex. As an aid to understanding and teaching this anatomy, axially and sagitally sectioned gross liver specimens were correlated with appropriate sonographic and CT scans. Major hepatic landmarks and vascular structures are used to identify four distinct transverse planes and four sagittal planes

  1. 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.

  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...... is not available.We will illustrate the results for magnet design problems from different areas, such as electric motors/generators (as the example in the picture), beam focusing for particle accelerators and magnetic refrigeration devices.......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...... magnets[1][2]. However, the powerful rare-earth magnets are generally expensive, so both the scientific and industrial communities have devoted a lot of effort into developing suitable design methods. Even so, many magnet optimization algorithms either are based on heuristic approaches[3...

  3. The edge detection algorithm combining smallest univalue segment assimilating nucleus and quaternion%结合四元数与最小核值相似区的边缘检测

    Institute of Scientific and Technical Information of China (English)

    李姗姗; 陈莉; 张永新; 尹化荣; 袁娅婷

    2017-01-01

    received the same attention.Up to now,most of the existing color image edge detection algorithms are monochromatic-based methods,which produce a superior effect than traditional gray-value methods.Both methods do not completely utilize the chromatic information.Meanwhile,vector-valued techniques treat the color information as color vectors in a vector space provided with a vector norm,thus solving such a problem.However,the vector-valued methods have high complexity and large computation.A color image with three components can be represented in quaternion form as pure quaternions,which can well preserve the vector features of the image pixels.Consequently,the edge detection algorithm combining the smallest univalue segment assimilating nucleus and quatemion in RGB space was proposed to deal with several problems in the traditional color image edge detection methods,such as the insufficient use of chromatic information in color images,the large amount of time and space consumption in the process of nonlinear transformation between color models,and the complex algorithm implementation.Method For a preferable color image edge detection result,we consider the algebraic operation and spatial characteristics regarding the quaternion,as well as the simple and effective edge detection performance of the SUSAN algorithm in our method.The steps of this approach can be summarized as follows.First,the color images are represented with pure quaternions and normalize each pixel.Second,edge detection is performed using the SUSAN operator,which generates a thick edge because of the constraint of the fixed geometry threshold g;hence,the Otsu algorithm is applied to adaptively capture the double geometry thresholds.Third,we make the edge growth on the weak edge set and determine the local edge direction according to the center of gravity and the longest axis of symmetry of SUSAN.Finally,we perform the local non-maximum suppression operation to obtain the final thinned edge image

  4. MOVING WINDOW SEGMENTATION FRAMEWORK FOR POINT CLOUDS

    Directory of Open Access Journals (Sweden)

    G. Sithole

    2012-07-01

    Full Text Available As lidar point clouds become larger streamed processing becomes more attractive. This paper presents a framework for the streamed segmentation of point clouds with the intention of segmenting unstructured point clouds in real-time. The framework is composed of two main components. The first component segments points within a window shifting over the point cloud. The second component stitches the segments within the windows together. In this fashion a point cloud can be streamed through these two components in sequence, thus producing a segmentation. The algorithm has been tested on airborne lidar point cloud and some results of the performance of the framework are presented.

  5. COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES

    OpenAIRE

    Hetangi D. Mehta*, Daxa Vekariya, Pratixa Badelia

    2017-01-01

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

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

    African Journals Online (AJOL)

    Petioles of D. hirtiflora and D. dumetorum were profusely surrounded with stellate and simple unicellular trichomes. Parenchyma cells in wild D. dumetorum were beaded, while they were not in the edible cultivar. Generally, scattered vascular bundles, layers of collenchymas cells, and dilated parenchyma cells filled with ...

  7. 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)

  8. Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion SPECT interpretation.

    Science.gov (United States)

    Berman, Daniel S; Abidov, Aiden; Kang, Xingping; Hayes, Sean W; Friedman, John D; Sciammarella, Maria G; Cohen, Ishac; Gerlach, James; Waechter, Parker B; Germano, Guido; Hachamovitch, Rory

    2004-01-01

    Recently, a 17-segment model of the left ventricle has been recommended as an optimally weighted approach for interpreting myocardial perfusion single photon emission computed tomography (SPECT). Methods to convert databases from previous 20- to new 17-segment data and criteria for abnormality for the 17-segment scores are needed. Initially, for derivation of the conversion algorithm, 65 patients were studied (algorithm population) (pilot group, n = 28; validation group, n = 37). Three conversion algorithms were derived: algorithm 1, which used mid, distal, and apical scores; algorithm 2, which used distal and apical scores alone; and algorithm 3, which used maximal scores of the distal septal, lateral, and apical segments in the 20-segment model for 3 corresponding segments of the 17-segment model. The prognosis population comprised 16,020 consecutive patients (mean age, 65 +/- 12 years; 41% women) who had exercise or vasodilator stress technetium 99m sestamibi myocardial perfusion SPECT and were followed up for 2.1 +/- 0.8 years. In this population, 17-segment scores were derived from 20-segment scores by use of algorithm 2, which demonstrated the best agreement with expert 17-segment reading in the algorithm population. The prognostic value of the 20- and 17-segment scores was compared by converting the respective summed scores into percent myocardium abnormal. Conversion algorithm 2 was found to be highly concordant with expert visual analysis by the 17-segment model (r = 0.982; kappa = 0.866) in the algorithm population. In the prognosis population, 456 cardiac deaths occurred during follow-up. When the conversion algorithm was applied, extent and severity of perfusion defects were nearly identical by 20- and derived 17-segment scores. The receiver operating characteristic curve areas by 20- and 17-segment perfusion scores were identical for predicting cardiac death (both 0.77 +/- 0.02, P = not significant). The optimal prognostic cutoff value for either 20

  9. 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,…

  10. 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.

  11. 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

  12. Normal cranial CT anatomy

    International Nuclear Information System (INIS)

    Gado, M.H.; Rao, K.C.V.G.

    1987-01-01

    The human brain consists of well-known anatomical components. Some parts of these components have been shown to be concerned with certain functions. A complete cranial CT examination consists of a series of several slices obtained in a sequence usually from the base to the vertex of the cranial vault, in the axial mode. The ultimate goal of this chapter is to pinpoint those slices that depict a given anatomical structure or several structures that deal with a given function. To achieve this goal, the discussion of CT cranial anatomy is presented in three sections

  13. 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 Disease......, 200 randomly selected CT scans were manually evaluated by medical experts, and only negligible or minor errors were found in nine scans. The proposed algorithm has been used to study how changes in smoking behavior affect CT based emphysema quantification. The algorithms for segmenting the airway...

  14. 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...

  15. 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

  16. 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.

  17. 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

  18. Orbita - Anatomy, development and deformities

    International Nuclear Information System (INIS)

    Hartmann, K.M.; Reith, W.; Golinski, M.; Schroeder, A.C.

    2008-01-01

    The development of the structures of the human orbita is very complex, but understanding the development makes it easier to understand normal anatomy and dysplasia. The following article first discusses the embryonic development of the eye structures and then presents the ''normal'' radiological anatomy using different investigation techniques and the most common deformities. (orig.) [de

  19. 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…

  20. Distance measures for image segmentation evaluation

    OpenAIRE

    Monteiro, Fernando C.; Campilho, Aurélio

    2012-01-01

    In this paper we present a study of evaluation measures that enable the quantification of the quality of an image segmentation result. Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Such an evaluation criterion can be useful for differ...

  1. MRI Brain Tumor Segmentation Methods- A Review

    OpenAIRE

    Gursangeet, Kaur; Jyoti, Rani

    2016-01-01

    Medical image processing and its segmentation is an active and interesting area for researchers. It has reached at the tremendous place in diagnosing tumors after the discovery of CT and MRI. MRI is an useful tool to detect the brain tumor and segmentation is performed to carry out the useful portion from an image. The purpose of this paper is to provide an overview of different image segmentation methods like watershed algorithm, morphological operations, neutrosophic sets, thresholding, K-...

  2. Bayesian segmentation of brainstem structures in MRI

    DEFF Research Database (Denmark)

    Iglesias, Juan Eugenio; Van Leemput, Koen; Bhatt, Priyanka

    2015-01-01

    the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy...

  3. A NEW APPROACH TO SEGMENT HANDWRITTEN DIGITS

    NARCIS (Netherlands)

    Oliveira, L.S.; Lethelier, E.; Bortolozzi, F.; Sabourin, R.

    2004-01-01

    This article presents a new segmentation approach applied to unconstrained handwritten digits. The novelty of the proposed algorithm is based on the combination of two types of structural features in order to provide the best segmentation path between connected entities. In this article, we first

  4. 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.

  5. heuristically improved bayesian segmentation of brain mr images

    African Journals Online (AJOL)

    Brainweb as a simulated brain MRI dataset is used in evaluating the proposed algorithm. ..... neighboring system can improve the segmentation power of the algorithm. ... tuning and learning of fuzzy knowledge bases, World Scientific. Pub Co ...

  6. Brookhaven segment interconnect

    International Nuclear Information System (INIS)

    Morse, W.M.; Benenson, G.; Leipuner, L.B.

    1983-01-01

    We have performed a high energy physics experiment using a multisegment Brookhaven FASTBUS system. The system was composed of three crate segments and two cable segments. We discuss the segment interconnect module which permits communication between the various segments

  7. Video segmentation using keywords

    Science.gov (United States)

    Ton-That, Vinh; Vong, Chi-Tai; Nguyen-Dao, Xuan-Truong; Tran, Minh-Triet

    2018-04-01

    At DAVIS-2016 Challenge, many state-of-art video segmentation methods achieve potential results, but they still much depend on annotated frames to distinguish between background and foreground. It takes a lot of time and efforts to create these frames exactly. In this paper, we introduce a method to segment objects from video based on keywords given by user. First, we use a real-time object detection system - YOLOv2 to identify regions containing objects that have labels match with the given keywords in the first frame. Then, for each region identified from the previous step, we use Pyramid Scene Parsing Network to assign each pixel as foreground or background. These frames can be used as input frames for Object Flow algorithm to perform segmentation on entire video. We conduct experiments on a subset of DAVIS-2016 dataset in half the size of its original size, which shows that our method can handle many popular classes in PASCAL VOC 2012 dataset with acceptable accuracy, about 75.03%. We suggest widely testing by combining other methods to improve this result in the future.

  8. 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....

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

    International Nuclear Information System (INIS)

    Zhen, X; Chen, H; Zhou, L; Yan, H; Jiang, S; Jia, X; Gu, X; Mell, L; Yashar, C; Cervino, L

    2014-01-01

    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

  10. 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...

  11. 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.

  12. The peritoneum - anatomy and imaging

    International Nuclear Information System (INIS)

    Ribarova, V.

    2017-01-01

    The peritoneum is a large and complex serous membrane The peritoneal spaces and the natural flow of peritoneal fluid determine the route of spread of the disease processes within the abdominal cavity. The goal of this article is to review the normal peritoneal anatomy and the role of the imaging in the diagnostic of the disease processes. Among the imaging methods, the computed tomography at greater extent allows the accurate examination of the complex anatomy of the peritoneal cavity and to assess the extent of the pathological processes affecting it. Key words: Peritoneal Anatomy. Imaging. CT [bg

  13. Accounting for segment correlations in segmented gamma-ray scans

    International Nuclear Information System (INIS)

    Sheppard, G.A.; Prettyman, T.H.; Piquette, E.C.

    1994-01-01

    In a typical segmented gamma-ray scanner (SGS), the detector's field of view is collimated so that a complete horizontal slice or segment of the desired thickness is visible. Ordinarily, the collimator is not deep enough to exclude gamma rays emitted from sample volumes above and below the segment aligned with the collimator. This can lead to assay biases, particularly for certain radioactive-material distributions. Another consequence of the collimator's low aspect ratio is that segment assays at the top and bottom of the sample are biased low because the detector's field of view is not filled. This effect is ordinarily countered by placing the sample on a low-Z pedestal and scanning one or more segment thicknesses below and above the sample. This takes extra time, however, We have investigated a number of techniques that both account for correlated segments and correct for end effects in SGS assays. Also, we have developed an algorithm that facilitates estimates of assay precision. Six calculation methods have been compared by evaluating the results of thousands of simulated, assays for three types of gamma-ray source distribution and ten masses. We will report on these computational studies and their experimental verification

  14. 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....

  15. 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.

  16. 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.

  17. 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.

  18. 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)

  19. Active mask segmentation of fluorescence microscope images.

    Science.gov (United States)

    Srinivasa, Gowri; Fickus, Matthew C; Guo, Yusong; Linstedt, Adam D; Kovacević, Jelena

    2009-08-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.

  20. 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

  1. Penile embryology and anatomy.

    Science.gov (United States)

    Yiee, Jenny H; Baskin, Laurence S

    2010-06-29

    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 7th and 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. 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.

  3. 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

  4. Compositional-prior-guided image reconstruction algorithm for multi-modality imaging

    Science.gov (United States)

    Fang, Qianqian; Moore, Richard H.; Kopans, Daniel B.; Boas, David A.

    2010-01-01

    The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information. We propose a composition-based image segmentation method for X-ray digital breast tomosynthesis (DBT) and a structural-prior-guided image reconstruction for a combined DBT and diffuse optical tomography (DOT) breast imaging system. Using the 3D DBT images from 31 clinically measured healthy breasts, we create an empirical relationship between the X-ray intensities for adipose and fibroglandular tissue. We use this relationship to then segment another 58 healthy breast DBT images from 29 subjects into compositional maps of different tissue types. For each breast, we build a weighted-graph in the compositional space and construct a regularization matrix to incorporate the structural priors into a finite-element-based DOT image reconstruction. Use of the compositional priors enables us to fuse tissue anatomy into optical images with less restriction than when using a binary segmentation. This allows us to recover the image contrast captured by DOT but not by DBT. We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter. By estimating the optical properties for adipose and fibroglandular tissue using the proposed algorithm, we found the results are comparable or superior to those estimated with expert-segmentations, but does not involve the time-consuming manual selection of regions-of-interest. PMID:21258460

  5. Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

    International Nuclear Information System (INIS)

    Shekhar, Raj; Lei, Peng; Castro-Pareja, Carlos R.; Plishker, William L.; D'Souza, Warren D.

    2007-01-01

    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

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

    Science.gov (United States)

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

    2009-04-01

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

  7. 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.

  8. The place of surface anatomy in the medical literature and undergraduate anatomy textbooks.

    Science.gov (United States)

    Azer, Samy A

    2013-01-01

    The aims of this review were to examine the place of surface anatomy in the medical literature, particularly the methods and approaches used in teaching surface and living anatomy and assess commonly used anatomy textbooks in regard to their surface anatomy contents. PubMed and MEDLINE databases were searched using the following keywords "surface anatomy," "living anatomy," "teaching surface anatomy," "bony landmarks," "peer examination" and "dermatomes". The percentage of pages covering surface anatomy in each textbook was calculated as well as the number of images covering surface anatomy. Clarity, quality and adequacy of surface anatomy contents was also examined. The search identified 22 research papers addressing methods used in teaching surface anatomy, 31 papers that can help in the improvement of surface anatomy curriculum, and 12 anatomy textbooks. These teaching methods included: body painting, peer volunteer surface anatomy, use of a living anatomy model, real time ultrasound, virtual (visible) human dissector (VHD), full body digital x-ray of cadavers (Lodox(®) Statscan(®) images) combined with palpating landmarks on peers and the cadaver, as well as the use of collaborative, contextual and self-directed learning. Nineteen of these studies were published in the period from 2006 to 2013. The 31 papers covered evidence-based and clinically-applied surface anatomy. The percentage of surface anatomy in textbooks' contents ranged from 0 to 6.2 with an average of 3.4%. The number of medical illustrations on surface anatomy varied from 0 to 135. In conclusion, although there has been a progressive increase in publications addressing methods used in teaching surface anatomy over the last six to seven years, most anatomy textbooks do not provide students with adequate information about surface anatomy. Only three textbooks provided a solid explanation and foundation of understanding surface anatomy. © 2013 American Association of Anatomists.

  9. Brain Tumor Image Segmentation in MRI Image

    Science.gov (United States)

    Peni Agustin Tjahyaningtijas, Hapsari

    2018-04-01

    Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

  10. An anatomy precourse enhances student learning in veterinary anatomy.

    Science.gov (United States)

    McNulty, Margaret A; Stevens-Sparks, Cathryn; Taboada, Joseph; Daniel, Annie; Lazarus, Michelle D

    2016-07-08

    Veterinary anatomy is often a source of trepidation for many students. Currently professional veterinary programs, similar to medical curricula, within the United States have no admission requirements for anatomy as a prerequisite course. The purpose of the current study was to evaluate the impact of a week-long precourse in veterinary anatomy on both objective student performance and subjective student perceptions of the precourse educational methods. Incoming first year veterinary students in the Louisiana State University School of Veterinary Medicine professional curriculum were asked to participate in a free precourse before the start of the semester, covering the musculoskeletal structures of the canine thoracic limb. Students learned the material either via dissection only, instructor-led demonstrations only, or a combination of both techniques. Outcome measures included student performance on examinations throughout the first anatomy course of the professional curriculum as compared with those who did not participate in the precourse. This study found that those who participated in the precourse did significantly better on examinations within the professional anatomy course compared with those who did not participate. Notably, this significant improvement was also identified on the examination where both groups were exposed to the material for the first time together, indicating that exposure to a small portion of veterinary anatomy can impact learning of anatomical structures beyond the immediate scope of the material previously learned. Subjective data evaluation indicated that the precourse was well received and students preferred guided learning via demonstrations in addition to dissection as opposed to either method alone. Anat Sci Educ 9: 344-356. © 2015 American Association of Anatomists. © 2015 American Association of Anatomists.

  11. 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.

  12. Pyramidal approach to license plate segmentation

    Science.gov (United States)

    Postolache, Alexandru; Trecat, Jacques C.

    1996-07-01

    Car identification is a goal in traffic control, transport planning, travel time measurement, managing parking lot traffic and so on. Most car identification algorithms contain a standalone plate segmentation process followed by a plate contents reading. A pyramidal algorithm for license plate segmentation, looking for textured regions, has been developed on a PC based system running Unix. It can be used directly in applications not requiring real time. When input images are relatively small, real-time performance is in fact accomplished by the algorithm. When using large images, porting the algorithm to special digital signal processors can easily lead to preserving real-time performance. Experimental results, for stationary and moving cars in outdoor scenes, showed high accuracy and high scores in detecting the plate. The algorithm also deals with cases where many character strings are present in the image, and not only the one corresponding to the plate. This is done by the means of a constrained texture regions classification.

  13. Soft tissue segmentation and 3D display from computerized tomography and magnetic resonance imaging

    International Nuclear Information System (INIS)

    Fan, R.T.; Trivedi, S.S.; Fellingham, L.L.; Gamboa-Aldeco, A.; Hedgcock, M.W.

    1987-01-01

    Volume calculation and 3D display of human anatomy facilitate a physician's diagnosis, treatment, and evaluation. Accurate segmentation of soft tissue structures is a prerequisite for such volume calculations and 3D displays, but segmentation by hand-outlining structures is often tedious and time-consuming. In this paper, methods based on analysis of statistics of image gray level are applied to segmentation of soft tissue in medical images, with the goal of making segmentation automatic or semi-automatic. The resulting segmented images, volume calculations, and 3D displays are analyzed and compared with results based on physician-drawn outlines as well as actual volume measurements

  14. Fully automatic segmentation of arbitrarily shaped fiducial markers in cone-beam CT projections

    DEFF Research Database (Denmark)

    Bertholet, Jenny; Wan, Hanlin; Toftegaard, Jakob

    2017-01-01

    segmentation, the DPTB algorithm generates and uses a 3D marker model to create 2D templates at any projection angle. The 2D templates are used to segment the marker position as the position with highest normalized cross-correlation in a search area centered at the DP segmented position. The accuracy of the DP...... algorithm and the new DPTB algorithm was quantified as the 2D segmentation error (pixels) compared to a manual ground truth segmentation for 97 markers in the projection images of CBCT scans of 40 patients. Also the fraction of wrong segmentations, defined as 2D errors larger than 5 pixels, was calculated...

  15. Coronary artery anatomy and variants

    Energy Technology Data Exchange (ETDEWEB)

    Malago, Roberto; Pezzato, Andrea; Barbiani, Camilla; Alfonsi, Ugolino; Nicoli, Lisa; Caliari, Giuliana; Pozzi Mucelli, Roberto [Policlinico G.B. Rossi, University of Verona, Department of Radiology, Verona (Italy)

    2011-12-15

    Variants and congenital anomalies of the coronary arteries are usually asymptomatic, but may present with severe chest pain or cardiac arrest. The introduction of multidetector CT coronary angiography (MDCT-CA) allows the detection of significant coronary artery stenosis. Improved performance with isotropic spatial resolution and higher temporal resolution provides a valid alternative to conventional coronary angiography (CCA) in many patients. MDCT-CA is now considered the ideal tool for three-dimensional visualization of the complex and tortuous anatomy of the coronary arteries. With multiplanar and volume-rendered reconstructions, MDCT-CA may even outperform CCA in determining the relative position of vessels, thus providing a better view of the coronary vascular anatomy. The purpose of this review is to describe the normal anatomy of the coronary arteries and their main variants based on MDCT-CA with appropriate reconstructions. (orig.)

  16. Skin Segmentation Based on Graph Cuts

    Institute of Scientific and Technical Information of China (English)

    HU Zhilan; WANG Guijin; LIN Xinggang; YAN Hong

    2009-01-01

    Skin segmentation is widely used in many computer vision tasks to improve automated visualiza-tion. This paper presents a graph cuts algorithm to segment arbitrary skin regions from images. The detected face is used to determine the foreground skin seeds and the background non-skin seeds with the color probability distributions for the foreground represented by a single Gaussian model and for the background by a Gaussian mixture model. The probability distribution of the image is used for noise suppression to alle-viate the influence of the background regions having skin-like colors. Finally, the skin is segmented by graph cuts, with the regional parameter y optimally selected to adapt to different images. Tests of the algorithm on many real wodd photographs show that the scheme accurately segments skin regions and is robust against illumination variations, individual skin variations, and cluttered backgrounds.

  17. [Imaging anatomy of cranial nerves].

    Science.gov (United States)

    Hermier, M; Leal, P R L; Salaris, S F; Froment, J-C; Sindou, M

    2009-04-01

    Knowledge of the anatomy of the cranial nerves is mandatory for optimal radiological exploration and interpretation of the images in normal and pathological conditions. CT is the method of choice for the study of the skull base and its foramina. MRI explores the cranial nerves and their vascular relationships precisely. Because of their small size, it is essential to obtain images with high spatial resolution. The MRI sequences optimize contrast between nerves and surrounding structures (cerebrospinal fluid, fat, bone structures and vessels). This chapter discusses the radiological anatomy of the cranial nerves.

  18. 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

  19. GPU accelerated fuzzy connected image segmentation by using CUDA.

    Science.gov (United States)

    Zhuge, Ying; Cao, Yong; Miller, Robert W

    2009-01-01

    Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

  20. Active Mask Segmentation of Fluorescence Microscope Images

    OpenAIRE

    Srinivasa, Gowri; Fickus, Matthew C.; Guo, Yusong; Linstedt, Adam D.; Kovačević, Jelena

    2009-01-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside”, or, masks, allowing for easy mul...

  1. External and internal anatomy of mandibular molars.

    Science.gov (United States)

    Rocha, L F; Sousa Neto, M D; Fidel, S R; da Costa, W F; Pécora, J D

    1996-01-01

    The external and internal anatomy of 628 extracted, mandibular first and second molars was studied. The external anatomy was studied by measuring each tooth and by observing the direction of the root curvatures from the facial surface. The internal anatomy of the pulp cavity was studied by a method of making the teeth translucent.

  2. 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...

  3. 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.

  4. Brain tumor segmentation based on a hybrid clustering technique

    Directory of Open Access Journals (Sweden)

    Eman Abdel-Maksoud

    2015-03-01

    This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

  5. 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

  6. 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.

  7. 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.

  8. Active contour based segmentation of resected livers in CT images

    Science.gov (United States)

    Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

    2015-03-01

    The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

  9. 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.

  10. CONTRIBUTIONS OF SUSHRUTA TO ANATOMY

    African Journals Online (AJOL)

    2005-08-08

    Aug 8, 2005 ... Learning anatomy through the dissected ... practice. Sushruta was the first person who had established the ... graduate had to obtain the permission of the ... importance to neuroembryology in the Sarira- .... Faculty of the History of Medicine and Pharmacy, and held at the Royal College of Physicians.

  11. 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.

  12. 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.

  13. 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...

  14. 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.

  15. BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

    Science.gov (United States)

    Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

    2006-01-01

    Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

  16. 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.

  17. 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

  18. 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.

  19. Remote sensing image segmentation based on Hadoop cloud platform

    Science.gov (United States)

    Li, Jie; Zhu, Lingling; Cao, Fubin

    2018-01-01

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

  20. 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.

  1. 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.

  2. 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.

  3. Algorithming the Algorithm

    DEFF Research Database (Denmark)

    Mahnke, Martina; Uprichard, Emma

    2014-01-01

    Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... 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...

  4. 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.

  5. 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.

  6. Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

    Directory of Open Access Journals (Sweden)

    Sarah Schlaeger

    Full Text Available Magnetic resonance imaging (MRI can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD. Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA and volume. Proton density fat fraction (PDFF of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

  7. Sulcal and gyral anatomy of the basal occipital-temporal lobe.

    Science.gov (United States)

    Chau, Anthony Minh Tien; Stewart, Fiona; Gragnaniello, Cristian

    2014-12-01

    The sulcal and gyral anatomy of the basal occipital-temporal lobe is highly variable and detailed descriptions of this region are limited and often inconsistent. The aim of this study was to describe the salient features of the sulcal and gyral anatomy of the basal occipital-temporal lobe. We studied the sulcal and gyral patterns of 30 formalin-fixed cerebral hemispheres. The major landmarks are the collateral sulcus (separated into the rhinal, proper, and caudal segments) and occipitotemporal sulcus (often interrupted), which were always present in this study. The bifurcation of the caudal collateral sulcus is a useful landmark. In relation to these sulci, we have described the surface anatomy and nominated landmarks of the medial (parahippocampal and lingual) and lateral (fusiform) occipitotemporal gyri. Understanding of the sulcal and gyral patterns of the basal occipital-temporal lobe may provide valuable information in its radiological and intraoperative interpretation.

  8. Automatic Segmentation of Vessels in In-Vivo Ultrasound Scans

    DEFF Research Database (Denmark)

    Tamimi-Sarnikowski, Philip; Brink-Kjær, Andreas; Moshavegh, Ramin

    2017-01-01

    presents a fully automatic segmentation algorithm, for robustly segmenting the vessel lumen in longitudinal B-mode ultrasound images. The automatic segmentation is performed using a combination of B-mode and power Doppler images. The proposed algorithm includes a series of preprocessing steps, and performs......Ultrasound has become highly popular to monitor atherosclerosis, by scanning the carotid artery. The screening involves measuring the thickness of the vessel wall and diameter of the lumen. An automatic segmentation of the vessel lumen, can enable the determination of lumen diameter. This paper...... a vessel segmentation by use of the marker-controlled watershed transform. The ultrasound images used in the study were acquired using the bk3000 ultrasound scanner (BK Ultrasound, Herlev, Denmark) with two transducers ”8L2 Linear” and ”10L2w Wide Linear” (BK Ultrasound, Herlev, Denmark). The algorithm...

  9. Clinically related anatomy for physicists

    International Nuclear Information System (INIS)

    Wright, A.E.; Boyer, A.L.

    1987-01-01

    With the advent of CT and MR imaging, delineation of malignancies and the shaping of radiation treatment fields have become much more precise. Treatment planning in more than one transverse plane is more widely practiced as the use of sophisticated computers grow. These developments emphasize the need for the physicist to have a basic knowledge of human anatomy. This course is designed to familiarize the clinical physicist with the gross anatomy and topographic landmarks used by the physician in planning three-dimensional radiation treatment volumes. The significance of the various anatomic structures and their related lymphatics in the spread of disease is discussed. Emphasis is placed on disease entities that pose particular problems due to overlying or nearby healthy structures at risk

  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. Pavement management segment consolidation

    Science.gov (United States)

    1998-01-01

    Dividing roads into "homogeneous" segments has been a major problem for all areas of highway engineering. SDDOT uses Deighton Associates Limited software, dTIMS, to analyze life-cycle costs for various rehabilitation strategies on each segment of roa...

  12. 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.

  13. 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...

  14. Roentgenologic anatomy of dog arteries

    International Nuclear Information System (INIS)

    Rehak, J.; Hlava, A.; Bavor, J.

    1984-01-01

    In catheter methods in dogs the knowledge of the roentgenologic anatomy of blood vessels is very important. Because of lacking in such roentgenologic anatomic schemes 5 arterial schemes in relation to the skeleton were elaborated. The system of arteries was divided into five regions: chest, head and neck in submentooccipital and lateral projection, abdomen and pelvis. The schemes comprise 75 of the main arteries of the dog. (author)

  15. 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 secon....... A visual interface displays the registered visualization of the first and second images. The system and method are particularly useful for imaging during minimally invasive surgery, such as robotic surgery....

  16. Spinal segmental dysgenesis

    Directory of Open Access Journals (Sweden)

    N Mahomed

    2009-06-01

    Full Text Available Spinal segmental dysgenesis is a rare congenital spinal abnormality , seen in neonates and infants in which a segment of the spine and spinal cord fails to develop normally . The condition is segmental with normal vertebrae above and below the malformation. This condition is commonly associated with various abnormalities that affect the heart, genitourinary, gastrointestinal tract and skeletal system. We report two cases of spinal segmental dysgenesis and the associated abnormalities.

  17. 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

  18. Skeleton-based Hierarchical Shape Segmentation

    NARCIS (Netherlands)

    Reniers, Dennie; Telea, Alexandru

    2007-01-01

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

  19. 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

  20. 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.

  1. Anatomy of the infant head

    International Nuclear Information System (INIS)

    Bosma, J.F.

    1986-01-01

    This text is mainly an atlas of illustration representing the dissection of the head and upper neck of the infant. It was prepared by the author over a 20-year period. The commentary compares the anatomy of the near-term infant with that of a younger fetus, child, and adult. As the author indicates, the dearth of anatomic information about postnatal anatomic changes represents a considerable handicap to those imaging infants. In part 1 of the book, anatomy is related to physiologic performance involving the pharynx, larynx, and mouth. Sequential topics involve the regional anatomy of the head (excluding the brain), the skeleton of the cranium, the nose, orbit, mouth, larynx, pharynx, and ear. To facilitate use of this text as a reference, the illustrations and text on individual organs are considered separately (i.e., the nose, the orbit, the eye, the mouth, the larynx, the pharynx, and the ear). Each part concerned with a separate organ includes materials from the regional illustrations contained in part 2 and from the skeleton, which is treated in part 3. Also included in a summary of the embryologic and fetal development of the organ

  2. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Jakab, Andras; Bauer, Stefan

    2015-01-01

    a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing...

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

    International Nuclear Information System (INIS)

    Tsitsoulis, Athanasios; Bourbakis, Nikolaos

    2012-01-01

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

  4. 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

  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. Accessory left gastric artery: angiographic anatomy

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kang Soo; Lim, Hyung Guhn; Kim, Hong Soo; Jeon, Doo Sung [Presbyterian Medical Center, Chunju (Korea, Republic of); Chung, Jin Wook; Park, Jae Hyung [College of Medicine and the Institute of Radiation Medicine, Seoul National University, Seoul (Korea, Republic of); Song, Soon Young [Myongji Hospital, College of Medicine, Kwandong University, Seoul (Korea, Republic of)

    2000-09-01

    To evaluate the angiographic anatomy of the accessory left gastric artery (accLGA). We evaluated the angiographic findings of the accLGA in 50 patients (Angiostar; Siemens, Erlangen, Germany). Performing celiac and selective angiography in 50 and 34 patients, respectively. By means of celiac angiography, (1) site of origin, (2) anatomical course, (3) diameter, (4) degree of tortuosity, and (5) distal tapering were evaluated, while selective angiography was used to determine (1) arterial branching, (2) area of blood supply, and (3) patterns of gastric wall stain. Celiac angiography showed that the accLGA arose from the left hepatic artery (LHA) in 45 cases (90%) and from the proper hepatic artery in five (10%). If the accLGA arose from the LHA, its origin entirely depended on the branching pattern of the latter. It always arose from the lateral branch of the LHA furthest to the left and uppermost, and proximal to its umbilical point. The most common anatomical course of the accLGA, seen in 27 cases (54%), was between the S2 and S3 segmental branch. The diameter and degree of tortuosity of the accLGA were similar to those of adjacent intrahepatic branches in 21 (42%) and 33 cases (66%), respectively. The degree of tapering was less than that of adjacent intrahepatic vessel in 28 (56%). Selective angiography demonstrated esophageal branching of the acc LGA in 27 cases (79%), inferior phrenic arterial branching in three (9%), a mediastinal branch in one (3%), and hypervascularity of the lung in one (3%). In 15 cases (44%), bifurcation of the accLGA was recognized. The vascular territory of the accLGA was the gastric fundus together with the distal esophagus in 21 cases (62%), mainly the gastric fundus in six (18%), and mainly the distal esophagus in four (12%). The pattern of gastric mucosal stain was curvilinear wall in 31 cases (91%) and nodular in three (9%). A knowledge of the angiographic anatomy of the accLGA facilitates accurate recognition of this artery on

  7. 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.

  8. 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.

  9. Adapting Mask-RCNN for Automatic Nucleus Segmentation

    OpenAIRE

    Johnson, Jeremiah W.

    2018-01-01

    Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for ...

  10. 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.)

  11. 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.)

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

    International Nuclear Information System (INIS)

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

    2003-01-01

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

  13. 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 ...

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  15. Graph run-length matrices for histopathological image segmentation.

    Science.gov (United States)

    Tosun, Akif Burak; Gunduz-Demir, Cigdem

    2011-03-01

    The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.

  16. 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…

  17. 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.

  18. Variational mesh segmentation via quadric surface fitting

    KAUST Repository

    Yan, Dongming; Wang, Wen Ping; Liu, Yang; Yang, Zhouwang

    2012-01-01

    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.

  19. Segmentation of ribs in digital chest radiographs

    Science.gov (United States)

    Cong, Lin; Guo, Wei; Li, Qiang

    2016-03-01

    Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.

  20. Anatomy of Cyberterrorism: Is America Vulnerable?

    National Research Council Canada - National Science Library

    Ashley, Bradley

    2003-01-01

    ... it means. It also presents a model to understand the anatomy of cyberterrorism, describing some real-world cyber events, assesses cyberterrorist capabilities, and finally makes specific recommendations...

  1. A reappraisal of adult thoracic and abdominal surface anatomy in Iranians in vivo using computed tomography.

    Science.gov (United States)

    Pak, Neda; Patel, Shilpan G; Hashemi Taheri, Amir P; Hashemi, Fariba; Eftekhari Vaghefi, Raana; Naybandi Atashi, Sara; Mirjalili, S Ali

    2016-03-01

    Surface anatomy is a core component of human anatomy in clinical practice. It allows clinicians to assess patients accurately and quickly; however, recent studies have revealed variability among individuals and ethnicities. The aim of this study is to investigate possible variations in adult thoracic and abdominal surface anatomy landmarks in an Iranian population. This study used 100 thoracoabdominal CT scans (mean age: 47 ± 17 years, age range: 20-77 years, 47% females), noted the most common locations of clinically relevant surface markings, and analyzed correlations between these variables and age or gender. While many common surface markings in Iranians were consistent with the evidence-based literature, there were some differences. In relation to the corresponding segments of the vertebral column, the superior vena cava formation and the lower border of the pleura adjacent to the vertebral column and right kidney tended to be at higher levels in adult Iranians than a Caucasian population. There were also discrepancies between the Iranian population and commonly-referenced medical textbooks and recent evidence-based literature concerning the vertebral levels of the diaphragmatic openings of the esophagus, aorta, and inferior vena cava. This study emphasizes the need to consider evidence-based reappraisals of surface anatomy to guide clinical practice. Much of our current knowledge of surface anatomy is based on older studies of cadavers rather than living people, and does not take ethnic and individual variations into consideration. © 2015 Wiley Periodicals, Inc.

  2. 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%).

  3. Remediation Trends in an Undergraduate Anatomy Course and Assessment of an Anatomy Supplemental Study Skills Course

    Science.gov (United States)

    Schutte, Audra Faye

    2013-01-01

    Anatomy A215: Basic Human Anatomy (Anat A215) is an undergraduate human anatomy course at Indiana University Bloomington (IUB) that serves as a requirement for many degree programs at IUB. The difficulty of the course, coupled with pressure to achieve grades for admittance into specific programs, has resulted in high remediation rates. In an…

  4. Chinese handwriting recognition an algorithmic perspective

    CERN Document Server

    Su, Tonghua

    2013-01-01

    This book provides an algorithmic perspective on the recent development of Chinese handwriting recognition. Two technically sound strategies, the segmentation-free and integrated segmentation-recognition strategy, are investigated and algorithms that have worked well in practice are primarily focused on. Baseline systems are initially presented for these strategies and are subsequently expanded on and incrementally improved. The sophisticated algorithms covered include: 1) string sample expansion algorithms which synthesize string samples from isolated characters or distort realistic string samples; 2) enhanced feature representation algorithms, e.g. enhanced four-plane features and Delta features; 3) novel learning algorithms, such as Perceptron learning with dynamic margin, MPE training and distributed training; and lastly 4) ensemble algorithms, that is, combining the two strategies using both parallel structure and serial structure. All the while, the book moves from basic to advanced algorithms, helping ...

  5. Image segmentation evaluation for very-large datasets

    Science.gov (United States)

    Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

    2016-03-01

    With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

  6. Three-Dimensional Segmentation of the Tumor in Computed Tomographic Images of Neuroblastoma

    OpenAIRE

    Deglint, Hanford J.; Rangayyan, Rangaraj M.; Ayres, Fábio J.; Boag, Graham S.; Zuffo, Marcelo K.

    2006-01-01

    Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative ...

  7. 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.

  8. Multi scales based sparse matrix spectral clustering image segmentation

    Science.gov (United States)

    Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

    2018-04-01

    In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

  9. An unsupervised strategy for biomedical image segmentation

    Directory of Open Access Journals (Sweden)

    Roberto Rodríguez

    2010-09-01

    Full Text Available Roberto Rodríguez1, Rubén Hernández21Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, MexicoAbstract: Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.Keywords: segmentation, mean shift, unsupervised segmentation, entropy

  10. Contact-impact algorithms on parallel computers

    International Nuclear Information System (INIS)

    Zhong Zhihua; Nilsson, Larsgunnar

    1994-01-01

    Contact-impact algorithms on parallel computers are discussed within the context of explicit finite element analysis. The algorithms concerned include a contact searching algorithm and an algorithm for contact force calculations. The contact searching algorithm is based on the territory concept of the general HITA algorithm. However, no distinction is made between different contact bodies, or between different contact surfaces. All contact segments from contact boundaries are taken as a single set. Hierarchy territories and contact territories are expanded. A three-dimensional bucket sort algorithm is used to sort contact nodes. The defence node algorithm is used in the calculation of contact forces. Both the contact searching algorithm and the defence node algorithm are implemented on the connection machine CM-200. The performance of the algorithms is examined under different circumstances, and numerical results are presented. ((orig.))

  11. William, a voxel model of child anatomy from tomographic images for Monte Carlo dosimetry calculations

    International Nuclear Information System (INIS)

    Caon, M.

    2010-01-01

    Full text: Medical imaging provides two-dimensional pictures of the human internal anatomy from which may be constructed a three-dimensional model of organs and tissues suitable for calculation of dose from radiation. Diagnostic CT provides the greatest exposure to radiation per examination and the frequency of CT examination is high. Esti mates of dose from diagnostic radiography are still determined from data derived from geometric models (rather than anatomical models), models scaled from adult bodies (rather than bodies of children) and CT scanner hardware that is no longer used. The aim of anatomical modelling is to produce a mathematical representation of internal anatomy that has organs of realistic size, shape and positioning. The organs and tissues are represented by a great many cuboidal volumes (voxels). The conversion of medical images to voxels is called segmentation and on completion every pixel in an image is assigned to a tissue or organ. Segmentation is time consuming. An image processing pack age is used to identify organ boundaries in each image. Thirty to forty tomographic voxel models of anatomy have been reported in the literature. Each model is of an individual, or a composite from several individuals. Images of children are particularly scarce. So there remains a need for more paediatric anatomical models. I am working on segmenting ''William'' who is 368 PET-CT images from head to toe of a seven year old boy. William will be used for Monte Carlo dose calculations of dose from CT examination using a simulated modern CT scanner.

  12. Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes.

    Science.gov (United States)

    Irimia, Andrei; Chambers, Micah C; Alger, Jeffry R; Filippou, Maria; Prastawa, Marcel W; Wang, Bo; Hovda, David A; Gerig, Guido; Toga, Arthur W; Kikinis, Ron; Vespa, Paul M; Van Horn, John D

    2011-11-01

    Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra- and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.

  13. Sound algorithms

    OpenAIRE

    De Götzen , Amalia; Mion , Luca; Tache , Olivier

    2007-01-01

    International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.

  14. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  15. 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.)

  16. 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.

  17. SnapAnatomy, a computer-based interactive tool for independent learning of human anatomy.

    Science.gov (United States)

    Yip, George W; Rajendran, Kanagasuntheram

    2008-06-01

    Computer-aided instruction materials are becoming increasing popular in medical education and particularly in the teaching of human anatomy. This paper describes SnapAnatomy, a new interactive program that the authors designed for independent learning of anatomy. SnapAnatomy is primarily tailored for the beginner student to encourage the learning of anatomy by developing a three-dimensional visualization of human structure that is essential to applications in clinical practice and the understanding of function. The program allows the student to take apart and to accurately put together body components in an interactive, self-paced and variable manner to achieve the learning outcome.

  18. 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.

  19. 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.

  20. Anatomy of psoas muscle innervation: Cadaveric study.

    Science.gov (United States)

    Mahan, Mark A; Sanders, Luke E; Guan, Jian; Dailey, Andrew T; Taylor, William; Morton, David A

    2017-05-01

    Hip flexion weakness is relatively common after lateral transpsoas surgery. Persistent weakness may result from injury to the innervation of the psoas major muscles (PMMs); however, anatomical texts have conflicting descriptions of this innervation, and the branching pattern of the nerves within the psoas major, particularly relative to vertebral anatomy, has not been described. The authors dissected human cadavers to describe the branching pattern of nerves supplying the PMMs. Sixteen embalmed cadavers were dissected, and the fine branching pattern of the innervation to the PMM was studied in 24 specimens. The number of branches and width and length of each branch of nerves to the PMMs were quantified. Nerve branches innervating the PMMs arose from spinal nerve levels L1-L4, with an average of 6.3 ± 1.1 branches per muscle. The L1 nerve branch was the least consistently present, whereas L2 and L3 branches were the most robust, the most numerous, and always present. The nerve branches to the psoas major commonly crossed the intervertebral (IV) disc obliquely prior to ramification within the muscle; 76%, 80%, and 40% of specimens had a branch to the PMM cross the midportion of the L2-3, L3-4, and L4-5 IV discs, respectively. The PMMs are segmentally innervated from the L2-L4 ventral rami branches, where these branches course obliquely across the L2-3, L3-4, and L4-5 IV discs. Knowledge of the mapping of nerve branches to the PMMs may reduce injury and the incidence of persistent weak hip flexion during lateral transpsoas surgery. Clin. Anat. 30:479-486, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.