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Sample records for anatomy segmentation algorithm

  1. Anatomy-aware measurement of segmentation accuracy

    Science.gov (United States)

    Tizhoosh, H. R.; Othman, A. A.

    2016-03-01

    Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems. One major shortcoming of all accuracy measures is that they neglect the anatomical significance or relevance of different zones within a given segment. Hence, existing accuracy metrics measure the overlap of a given segment with a ground-truth without any anatomical discrimination inside the segment. For instance, if we understand the rectal wall or urethral sphincter as anatomical zones, then current accuracy measures ignore their significance when they are applied to assess the quality of the prostate gland segments. In this paper, we propose an anatomy-aware measurement scheme for segmentation accuracy of medical images. The idea is to create a "master gold" based on a consensus shape containing not just the outline of the segment but also the outlines of the internal zones if existent or relevant. To apply this new approach to accuracy measurement, we introduce the anatomy-aware extensions of both Dice coefficient and Jaccard index and investigate their effect using 500 synthetic prostate ultrasound images with 20 different segments for each image. We show that through anatomy-sensitive calculation of segmentation accuracy, namely by considering relevant anatomical zones, not only the measurement of individual users can change but also the ranking of users' segmentation skills may require reordering.

  2. Performance evaluation of an automatic anatomy segmentation algorithm on repeat or four-dimensional CT images using a deformable image registration method

    Science.gov (United States)

    Wang, He; Garden, Adam S.; Zhang, Lifei; Wei, Xiong; Ahamad, Anesa; Kuban, Deborah A.; Komaki, Ritsuko; O’Daniel, Jennifer; Zhang, Yongbin; Mohan, Radhe; Dong, Lei

    2008-01-01

    Purpose Auto-propagation of anatomical region-of-interests (ROIs) from the planning CT to daily CT is an essential step in image-guided adaptive radiotherapy. The goal of this study was to quantitatively evaluate the performance of the algorithm in typical clinical applications. Method and Materials We previously adopted an image intensity-based deformable registration algorithm to find the correspondence between two images. In this study, the ROIs delineated on the planning CT image were mapped onto daily CT or four-dimentional (4D) CT images using the same transformation. Post-processing methods, such as boundary smoothing and modification, were used to enhance the robustness of the algorithm. Auto-propagated contours for eight head-and-neck patients with a total of 100 repeat CTs, one prostate patient with 24 repeat CTs, and nine lung cancer patients with a total of 90 4D-CT images were evaluated against physician-drawn contours and physician-modified deformed contours using the volume-overlap-index (VOI) and mean absolute surface-to-surface distance (ASSD). Results The deformed contours were reasonably well matched with daily anatomy on repeat CT images. The VOI and mean ASSD were 83% and 1.3 mm when compared to the independently drawn contours. A better agreement (greater than 97% and less than 0.4 mm) was achieved if the physician was only asked to correct the deformed contours. The algorithm was robust in the presence of random noise in the image. Conclusion The deformable algorithm may be an effective method to propagate the planning ROIs to subsequent CT images of changed anatomy, although a final review by physicians is highly recommended. PMID:18722272

  3. The Watershed Algorithm for Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    OU Yan; LIN Nan

    2007-01-01

    This article introduced the watershed algorithm for the segmentation, illustrated the segmation process by implementing this algorithm. By comparing with another three related algorithm, this article revealed both the advantages and drawbacks of the watershed algorithm.

  4. Novel Facial Features Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An efficient algorithm for facial features extractions is proposed. The facial features we segment are the two eyes, nose and mouth. The algorithm is based on an improved Gabor wavelets edge detector, morphological approach to detect the face region and facial features regions, and an improved T-shape face mask to locate the extract location of facial features. The experimental results show that the proposed method is robust against facial expression, illumination, and can be also effective if the person wearing glasses, and so on.

  5. 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.

  6. An algorithm for segmenting range imagery

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    Roberts, R.S.

    1997-03-01

    This report describes the technical accomplishments of the FY96 Cross Cutting and Advanced Technology (CC&AT) project at Los Alamos National Laboratory. The project focused on developing algorithms for segmenting range images. The image segmentation algorithm developed during the project is described here. In addition to segmenting range images, the algorithm can fuse multiple range images thereby providing true 3D scene models. The algorithm has been incorporated into the Rapid World Modelling System at Sandia National Laboratory.

  7. Robust atlas-based segmentation of highly variable anatomy: left atrium segmentation

    OpenAIRE

    Depa, Michal; Sabuncu, Mert R.; Holmvang, Godtfred; Nezafat, Reza; Schmidt, Ehud J.; Golland, Polina

    2010-01-01

    Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We ac...

  8. Image Series Segmentation and Improved MC Algorithm

    Institute of Scientific and Technical Information of China (English)

    WAN Wei-bing; SHI Peng-fei

    2008-01-01

    A semiautomatic segmentation method based on active contour is proposed for computed tomog-raphy (CT) image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial con-tour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes (MC) algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by experimental results.

  9. An efficient algorithm for color image segmentation

    Directory of Open Access Journals (Sweden)

    Shikha Yadav

    2016-09-01

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

  10. Medical anatomy segmentation kit: combining 2D and 3D segmentation methods to enhance functionality

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    Tracton, Gregg S.; Chaney, Edward L.; Rosenman, Julian G.; Pizer, Stephen M.

    1994-07-01

    Image segmentation, in particular, defining normal anatomic structures and diseased or malformed tissue from tomographic images, is common in medical applications. Defining tumors or arterio-venous malformation from computed tomography or magnetic resonance images are typical examples. This paper describes a program, Medical Anatomy Segmentation Kit (MASK), whose design acknowledges that no single segmentation technique has proven to be successful or optimal for all object definition tasks associated with medical images. A practical solution is offered through a suite of complementary user-guided segmentation techniques and extensive manual editing functions to reach the final object definition goal. Manual editing can also be used to define objects which are abstract or otherwise not well represented in the image data and so require direct human definition - e.g., a radiotherapy target volume which requires human knowledge and judgement regarding image interpretation and tumor spread characteristics. Results are either in the form of 2D boundaries or regions of labeled pixels or voxels. MASK currently uses thresholding and edge detection to form contours, and 2D or 3D scale-sensitive fill and region algebra to form regions. In addition to these proven techniques, MASK's architecture anticipates clinically practical automatic 2D and 3D segmentation methods of the future.

  11. 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

  12. Dermoscopic Image Segmentation using Machine Learning Algorithm

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    L. P. Suresh

    2011-01-01

    Full Text Available Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in Dermoscopy images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma. The various intelligent system based clustering techniques used are Fuzzy C Means Algorithm (FCM, Possibilistic C Means Algorithm (PCM, Hierarchical C Means Algorithm (HCM; C-mean based Fuzzy Hopfield Neural Network, Adaline Neural Network and Regression Neural Network. Results: The segmented images are compared with the ground truth image using various parameters such as False Positive Error (FPE, False Negative Error (FNE Coefficient of similarity, spatial overlap and their performance is evaluated. Conclusion: The experimental results show that the Hierarchical C Means algorithm( Fuzzy provides better segmentation than other (Fuzzy C Means, Possibilistic C Means, Adaline Neural Network, FHNN and GRNN clustering algorithms. Thus Hierarchical C Means approach can handle uncertainties that exist in the data efficiently and useful for the lesion segmentation in a computer aided diagnosis system to assist the clinical diagnosis of dermatologists.

  13. Heart region segmentation from low-dose CT scans: an anatomy based approach

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    Reeves, Anthony P.; Biancardi, Alberto M.; Yankelevitz, David F.; Cham, Matthew D.; Henschke, Claudia I.

    2012-02-01

    Cardiovascular disease is a leading cause of death in developed countries. The concurrent detection of heart diseases during low-dose whole-lung CT scans (LDCT), typically performed as part of a screening protocol, hinges on the accurate quantification of coronary calcification. The creation of fully automated methods is ideal as complete manual evaluation is imprecise, operator dependent, time consuming and thus costly. The technical challenges posed by LDCT scans in this context are mainly twofold. First, there is a high level image noise arising from the low radiation dose technique. Additionally, there is a variable amount of cardiac motion blurring due to the lack of electrocardiographic gating and the fact that heart rates differ between human subjects. As a consequence, the reliable segmentation of the heart, the first stage toward the implementation of morphologic heart abnormality detection, is also quite challenging. An automated computer method based on a sequential labeling of major organs and determination of anatomical landmarks has been evaluated on a public database of LDCT images. The novel algorithm builds from a robust segmentation of the bones and airways and embodies a stepwise refinement starting at the top of the lungs where image noise is at its lowest and where the carina provides a good calibration landmark. The segmentation is completed at the inferior wall of the heart where extensive image noise is accommodated. This method is based on the geometry of human anatomy and does not involve training through manual markings. Using visual inspection by an expert reader as a gold standard, the algorithm achieved successful heart and major vessel segmentation in 42 of 45 low-dose CT images. In the 3 remaining cases, the cardiac base was over segmented due to incorrect hemidiaphragm localization.

  14. Uniform wire segmentation algorithm of distributed interconnects

    Institute of Scientific and Technical Information of China (English)

    Yin Guoli; Lin Zhenghui

    2007-01-01

    A uniform wire segmentation algorithm for performance optimization of distributed RLC interconnects was proposed in this paper. The optimal wire length for identical segments and buffer size for buffer insertion are obtained through computation and derivation, based on a 2-pole approximation model of distributed RLC interconnect. For typical inductance value and long wires under 180nm technology, experiments show that the uniform wire segmentation technique proposed in the paper can reduce delay by about 27% ~ 56% , while requires 34%~69% less total buffer usage and thus 29% to 58% less power consumption. It is suitable for long RLC interconnect performance optimization.

  15. Touching Syllable Segmentation using Split Profile Algorithm

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    L.Pratap Reddy

    2010-05-01

    Full Text Available The most challenging task of a character recognition system is associated with segmentation of individual components of the script with maximum efficiency. This process is relatively easy with regard to stroke based and standard scripts. Cursive scripts are more complex possessing a large number of overlapping and touching objects, where in the statistical behavior of the topological properties are to be studied extensively for achieving highest accuracy. Certain amount of similarity exists between unconstrained hand written text as well as South Indian scripts in terms of topology, component combinations, overlapping and merging characteristics. The concept of syllables and their formulations is an additive complexity with regard to Indian scripts. In this paper the statistical behavior of the cursive script, Telugu, is presented. The topological properties in terms of zones, component combinations, behavioural aspects of syllables are studied and adopted in the segmentation process. The statistical behaviour of cursive components are evaluated. Split Profile Algorithm is proposed while handling touching components. The proposed algorithm is evaluated on different fonts and sizes. The performance of the proposed algorithm is compared with two approaches methods viz aspect ratio and syllable width approaches.

  16. Segmentation precision of abdominal anatomy for MRI-based radiotherapy.

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    Noel, Camille E; Zhu, Fan; Lee, Andrew Y; Yanle, Hu; Parikh, Parag J

    2014-01-01

    The limited soft tissue visualization provided by computed tomography, the standard imaging modality for radiotherapy treatment planning and daily localization, has motivated studies on the use of magnetic resonance imaging (MRI) for better characterization of treatment sites, such as the prostate and head and neck. However, no studies have been conducted on MRI-based segmentation for the abdomen, a site that could greatly benefit from enhanced soft tissue targeting. We investigated the interobserver and intraobserver precision in segmentation of abdominal organs on MR images for treatment planning and localization. Manual segmentation of 8 abdominal organs was performed by 3 independent observers on MR images acquired from 14 healthy subjects. Observers repeated segmentation 4 separate times for each image set. Interobserver and intraobserver contouring precision was assessed by computing 3-dimensional overlap (Dice coefficient [DC]) and distance to agreement (Hausdorff distance [HD]) of segmented organs. The mean and standard deviation of intraobserver and interobserver DC and HD values were DC(intraobserver) = 0.89 ± 0.12, HD(intraobserver) = 3.6mm ± 1.5, DC(interobserver) = 0.89 ± 0.15, and HD(interobserver) = 3.2mm ± 1.4. Overall, metrics indicated good interobserver/intraobserver precision (mean DC > 0.7, mean HD segmentation precision for abdominal sites. These findings support the utility of MRI for abdominal planning and localization, as emerging MRI technologies, techniques, and onboard imaging devices are beginning to enable MRI-based radiotherapy.

  17. A new algorithm of brain volume contours segmentation

    Institute of Scientific and Technical Information of China (English)

    吴建明; 施鹏飞

    2003-01-01

    This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.

  18. Segmentation precision of abdominal anatomy for MRI-based radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Noel, Camille E.; Zhu, Fan; Lee, Andrew Y.; Yanle, Hu; Parikh, Parag J., E-mail: pparikh@radonc.wustl.edu

    2014-10-01

    The limited soft tissue visualization provided by computed tomography, the standard imaging modality for radiotherapy treatment planning and daily localization, has motivated studies on the use of magnetic resonance imaging (MRI) for better characterization of treatment sites, such as the prostate and head and neck. However, no studies have been conducted on MRI-based segmentation for the abdomen, a site that could greatly benefit from enhanced soft tissue targeting. We investigated the interobserver and intraobserver precision in segmentation of abdominal organs on MR images for treatment planning and localization. Manual segmentation of 8 abdominal organs was performed by 3 independent observers on MR images acquired from 14 healthy subjects. Observers repeated segmentation 4 separate times for each image set. Interobserver and intraobserver contouring precision was assessed by computing 3-dimensional overlap (Dice coefficient [DC]) and distance to agreement (Hausdorff distance [HD]) of segmented organs. The mean and standard deviation of intraobserver and interobserver DC and HD values were DC{sub intraobserver} = 0.89 ± 0.12, HD{sub intraobserver} = 3.6 mm ± 1.5, DC{sub interobserver} = 0.89 ± 0.15, and HD{sub interobserver} = 3.2 mm ± 1.4. Overall, metrics indicated good interobserver/intraobserver precision (mean DC > 0.7, mean HD < 4 mm). Results suggest that MRI offers good segmentation precision for abdominal sites. These findings support the utility of MRI for abdominal planning and localization, as emerging MRI technologies, techniques, and onboard imaging devices are beginning to enable MRI-based radiotherapy.

  19. Video segmentation using multiple features based on EM algorithm

    Institute of Scientific and Technical Information of China (English)

    张风超; 杨杰; 刘尔琦

    2004-01-01

    Object-based video segmentation is an important issue for many multimedia applications. A video segmentation method based on EM algorithm is proposed. We consider video segmentation as an unsupervised classification problem and apply EM algorithm to obtain the maximum-likelihood estimation of the Gaussian model parameters for model-based segmentation. We simultaneously combine multiple features (motion, color) within a maximum likelihood framework to obtain accurate segment results. We also use the temporal consistency among video frames to improve the speed of EM algorithm. Experimental results on typical MPEG-4 sequences and real scene sequences show that our method has an attractive accuracy and robustness.

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

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    Chen, Xinjian; Bagci, Ulas [Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182 and Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China); Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182 (United States)

    2011-08-15

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

  1. Illumination Compensation Algorithm for Unevenly Lighted Document Segmentation

    Directory of Open Access Journals (Sweden)

    Ju Zhiyong

    2013-07-01

    Full Text Available For the problem of segmenting the unevenly lighted document image, this paper proposes an illumination compensation segmentation algorithm which can effectively segment the unevenly lighted document. The illumination compensation method is proposed to equivalently convert unevenly lighted document image to evenly lighted document image, then segment the evenly lighted document directly. Experimental results show that the proposed method can get the accurate evenly lighted document images so that we can segment the document accurately and it is more efficient to process unevenly lighted document  images than traditional binarization methods. The algorithm effectively overcomes the difficulty in handling uneven lighting and enhances segmentation quality considerably.

  2. CT segmentation of dental shapes by anatomy-driven reformation imaging and B-spline modelling.

    Science.gov (United States)

    Barone, S; Paoli, A; Razionale, A V

    2016-06-01

    Dedicated imaging methods are among the most important tools of modern computer-aided medical applications. In the last few years, cone beam computed tomography (CBCT) has gained popularity in digital dentistry for 3D imaging of jawbones and teeth. However, the anatomy of a maxillofacial region complicates the assessment of tooth geometry and anatomical location when using standard orthogonal views of the CT data set. In particular, a tooth is defined by a sub-region, which cannot be easily separated from surrounding tissues by only considering pixel grey-intensity values. For this reason, an image enhancement is usually necessary in order to properly segment tooth geometries. In this paper, an anatomy-driven methodology to reconstruct individual 3D tooth anatomies by processing CBCT data is presented. The main concept is to generate a small set of multi-planar reformation images along significant views for each target tooth, driven by the individual anatomical geometry of a specific patient. The reformation images greatly enhance the clearness of the target tooth contours. A set of meaningful 2D tooth contours is extracted and used to automatically model the overall 3D tooth shape through a B-spline representation. The effectiveness of the methodology has been verified by comparing some anatomy-driven reconstructions of anterior and premolar teeth with those obtained by using standard tooth segmentation tools. Copyright © 2015 John Wiley & Sons, Ltd.

  3. Automatic lobar segmentation for diseased lungs using an anatomy-based priority knowledge in low-dose CT images

    Science.gov (United States)

    Park, Sang Joon; Kim, Jung Im; Goo, Jin Mo; Lee, Doohee

    2014-03-01

    Lung lobar segmentation in CT images is a challenging tasks because of the limitations in image quality inherent to CT image acquisition, especially low-dose CT for clinical routine environment. Besides, complex anatomy and abnormal lesions in the lung parenchyma makes segmentation difficult because contrast in CT images are determined by the differential absorption of X-rays by neighboring structures, such as tissue, vessel or several pathological conditions. Thus, we attempted to develop a robust segmentation technique for normal and diseased lung parenchyma. The images were obtained with low-dose chest CT using soft reconstruction kernel (Sensation 16, Siemens, Germany). Our PC-based in-house software segmented bronchial trees and lungs with intensity adaptive region-growing technique. Then the horizontal and oblique fissures were detected by using eigenvalues-ratio of the Hessian matrix in the lung regions which were excluded from airways and vessels. To enhance and recover the faithful 3-D fissure plane, our proposed fissure enhancing scheme were applied to the images. After finishing above steps, for careful smoothening of fissure planes, 3-D rolling-ball algorithm in xyz planes were performed. Results show that success rate of our proposed scheme was achieved up to 89.5% in the diseased lung parenchyma.

  4. IMPROVED RANDOMIZED ALGORITHM FOR THE EQUIVALENT 2-CATALOG SEGMENTATION PROBLEM

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    An improved randomized algorithm of the equivalent 2-catalog segmentation problem is presented. The result obtained in this paper makes some progress to answer the open problem by analyze this algorithm with performance guarantee. A 0.6378-approximation for the equivalent 2-catalog segmentation problem is obtained.

  5. Comparative testing of DNA segmentation algorithms using benchmark simulations.

    Science.gov (United States)

    Elhaik, Eran; Graur, Dan; Josic, Kresimir

    2010-05-01

    Numerous segmentation methods for the detection of compositionally homogeneous domains within genomic sequences have been proposed. Unfortunately, these methods yield inconsistent results. Here, we present a benchmark consisting of two sets of simulated genomic sequences for testing the performances of segmentation algorithms. Sequences in the first set are composed of fixed-sized homogeneous domains, distinct in their between-domain guanine and cytosine (GC) content variability. The sequences in the second set are composed of a mosaic of many short domains and a few long ones, distinguished by sharp GC content boundaries between neighboring domains. We use these sets to test the performance of seven segmentation algorithms in the literature. Our results show that recursive segmentation algorithms based on the Jensen-Shannon divergence outperform all other algorithms. However, even these algorithms perform poorly in certain instances because of the arbitrary choice of a segmentation-stopping criterion.

  6. COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES

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    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.

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

    OpenAIRE

    Saadia Zahid; Fawad Hussain; Muhammad Rashid; Muhammad Haroon Yousaf; Hafiz Adnan Habib

    2015-01-01

    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 o...

  8. The PCNN adaptive segmentation algorithm based on visual perception

    Science.gov (United States)

    Zhao, Yanming

    To solve network adaptive parameter determination problem of the pulse coupled neural network (PCNN), and improve the image segmentation results in image segmentation. The PCNN adaptive segmentation algorithm based on visual perception of information is proposed. Based on the image information of visual perception and Gabor mathematical model of Optic nerve cells receptive field, the algorithm determines adaptively the receptive field of each pixel of the image. And determines adaptively the network parameters W, M, and β of PCNN by the Gabor mathematical model, which can overcome the problem of traditional PCNN parameter determination in the field of image segmentation. Experimental results show that the proposed algorithm can improve the region connectivity and edge regularity of segmentation image. And also show the PCNN of visual perception information for segmentation image of advantage.

  9. 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.

  10. Track segment association algorithm based on statistical binary thresholds

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    Qi Lin

    2015-05-01

    Full Text Available The classical Track Segment Association (TSA algorithm suffers from low accuracy and is impractical to use in concentrated targets, branching, and cross-tracking environment. Thus, a new statistical binary track segment association algorithm is proposed. The new algorithm is more appropriate as it increases the sample size for the χ2 distribution threshold detection. Simulation results show that in air cross tracking and for ballistic targets, the global correct association rate and the average correct association rate of the proposed algorithm are remarkably improved, which proves the good performance of the proposed algorithm.

  11. Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

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    Oludayo O. Olugbara

    2015-01-01

    Full Text Available Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

  12. 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.

  13. Automated lung segmentation algorithm for CAD system of thoracic CT

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert's manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.

  14. An Improved Image Segmentation Based on Mean Shift Algorithm

    Institute of Scientific and Technical Information of China (English)

    CHENHanfeng; QIFeihu

    2003-01-01

    Gray image segmentation is to segment an image into some homogeneous regions and only one gray level is defined for each region as the result. These grayl evels are called major gray levels. Mean shift algorithm(MSA) has shown its efficiency in image segmentation. An improved gray image segmentation method based on MSAis proposed in this paper since usual image segmentation methods based on MSA often fail in segmenting imageswith weak edges. Corrupted block and its J-value are defined firstly in the proposed method. Then, J-matrix gotten from corrupted blocks are proposed to measure whether weak edges appear in the image. According to the J-matrix, major gray levels gotten with usual segmen-tation methods based on MSA are augmented and corre-sponding allocation windows are modified to detect weak edges. Experimental results demonstrate the effectiveness of the proposed method in gray image segmentation.

  15. A novel iris segmentation algorithm based on small eigenvalue analysis

    Science.gov (United States)

    Harish, B. S.; Aruna Kumar, S. V.; Guru, D. S.; Ngo, Minh Ngoc

    2015-12-01

    In this paper, a simple and robust algorithm is proposed for iris segmentation. The proposed method consists of two steps. In first step, iris and pupil is segmented using Robust Spatial Kernel FCM (RSKFCM) algorithm. RSKFCM is based on traditional Fuzzy-c-Means (FCM) algorithm, which incorporates spatial information and uses kernel metric as distance measure. In second step, small eigenvalue transformation is applied to localize iris boundary. The transformation is based on statistical and geometrical properties of the small eigenvalue of the covariance matrix of a set of edge pixels. Extensive experimentations are carried out on standard benchmark iris dataset (viz. CASIA-IrisV4 and UBIRIS.v2). We compared our proposed method with existing iris segmentation methods. Our proposed method has the least time complexity of O(n(i+p)) . The result of the experiments emphasizes that the proposed algorithm outperforms the existing iris segmentation methods.

  16. Image segmentation by using the localized subspace iteration algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An image segmentation algorithm called"segmentation based on the localized subspace iterations"(SLSI)is proposed in this paper.The basic idea is to combine the strategies in Ncut algorithm by Shi and Malik in 2000 and the LSI by E,Li and Lu in 2007.The LSI is applied to solve an eigenvalue problem associated with the affinity matrix of an image,which makes the overall algorithm linearly scaled.The choices of the partition number,the supports and weight functions in SLSI are discussed.Numerical experiments for real images show the applicability of the algorithm.

  17. A CT Image Segmentation Algorithm Based on Level Set Method

    Institute of Scientific and Technical Information of China (English)

    QU Jing-yi; SHI Hao-shan

    2006-01-01

    Level Set methods are robust and efficient numerical tools for resolving curve evolution in image segmentation. This paper proposes a new image segmentation algorithm based on Mumford-Shah module. The method is used to CT images and the experiment results demonstrate its efficiency and veracity.

  18. Color Image Segmentation via Improved K-Means Algorithm

    Directory of Open Access Journals (Sweden)

    Ajay Kumar

    2016-03-01

    Full Text Available Data clustering techniques are often used to segment the real world images. Unsupervised image segmentation algorithms that are based on the clustering suffer from random initialization. There is a need for efficient and effective image segmentation algorithm, which can be used in the computer vision, object recognition, image recognition, or compression. To address these problems, the authors present a density-based initialization scheme to segment the color images. In the kernel density based clustering technique, the data sample is mapped to a high-dimensional space for the effective data classification. The Gaussian kernel is used for the density estimation and for the mapping of sample image into a high- dimensional color space. The proposed initialization scheme for the k-means clustering algorithm can homogenously segment an image into the regions of interest with the capability of avoiding the dead centre and the trapped centre by local minima phenomena. The performance of the experimental result indicates that the proposed approach is more effective, compared to the other existing clustering-based image segmentation algorithms. In the proposed approach, the Berkeley image database has been used for the comparison analysis with the recent clustering-based image segmentation algorithms like k-means++, k-medoids and k-mode.

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

    Directory of Open Access Journals (Sweden)

    Saadia Zahid

    2015-01-01

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

  20. Segmentation algorithm for non-stationary compound Poisson processes

    CERN Document Server

    Toth, Bence; Farmer, J Doyne

    2010-01-01

    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 the time series. The process is composed of consecutive patches of variable length, each patch being described by a stationary compound Poisson process, i.e. a Poisson process where each count is associated to 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-Galvan, et al., Phys. Rev. Lett., 87, 168105 (2001). We show that the new algorithm outperforms the original one for regime switching 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.

  1. 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.

  2. Segmentation algorithms for ear image data towards biomechanical studies.

    Science.gov (United States)

    Ferreira, Ana; Gentil, Fernanda; Tavares, João Manuel R S

    2014-01-01

    In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.

  3. PCNN document segmentation method based on bacterial foraging optimization algorithm

    Science.gov (United States)

    Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian

    2014-04-01

    Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.

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

    Directory of Open Access Journals (Sweden)

    V.G. Biju

    2015-11-01

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

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

    Science.gov (United States)

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

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

  6. Comparison of algorithms for ultrasound image segmentation without ground truth

    Science.gov (United States)

    Sikka, Karan; Deserno, Thomas M.

    2010-02-01

    Image segmentation is a pre-requisite to medical image analysis. A variety of segmentation algorithms have been proposed, and most are evaluated on a small dataset or based on classification of a single feature. The lack of a gold standard (ground truth) further adds to the discrepancy in these comparisons. This work proposes a new methodology for comparing image segmentation algorithms without ground truth by building a matrix called region-correlation matrix. Subsequently, suitable distance measures are proposed for quantitative assessment of similarity. The first measure takes into account the degree of region overlap or identical match. The second considers the degree of splitting or misclassification by using an appropriate penalty term. These measures are shown to satisfy the axioms of a quasi-metric. They are applied for a comparative analysis of synthetic segmentation maps to show their direct correlation with human intuition of similar segmentation. Since ultrasound images are difficult to segment and usually lack a ground truth, the measures are further used to compare the recently proposed spectral clustering algorithm (encoding spatial and edge information) with standard k-means over abdominal ultrasound images. Improving the parameterization and enlarging the feature space for k-means steadily increased segmentation quality to that of spectral clustering.

  7. A statistical learning algorithm for word segmentation

    CERN Document Server

    Van Aken, Jerry R

    2011-01-01

    In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recurring sequences. This paper describes a computer algorithm that is designed to solve the problem of locating word boundaries in blocks of English text from which the spaces have been removed. This problem avoids the complexities of processing speech but requires similar capabilities for detecting recurring sequences. The algorithm that is described in this paper relies entirely on statistical relationships between letters in the input stream to infer the locations of word boundaries. The source code for a C++ version of this algorithm is presented in an appendix.

  8. An Efficient Character Segmentation Based on VNP Algorithm

    Directory of Open Access Journals (Sweden)

    S. Chitrakala

    2012-12-01

    Full Text Available Character segmentation is an important preprocessing stage in image processing applications such as OCR, License Plate Recognition, electronic processing of checks in banks, form processing and, label and barcode recognition. It is essential to have an efficient character segmentation technique because it affects the performance of all the processes that follow and hence, the overall system accuracy. Vertical projection profile is the most common segmentation technique. However, the segmentation results are not always correct in cases where pixels of adjacent characters fall on the same scan line and a minimum threshold is not observed in the histogram to segment the respective adjacent characters. In this study, a character segmentation technique based on Visited Neighbor Pixel (VNP Algorithm is proposed, which is an improvement to the vertical projection profile technique. VNP Algorithm performs segmentation based on the connectedness of the pixels on the scan line with that of the previously visited pixels. Therefore, a clear line of separation is found even when the threshold between two adjacent characters is not minimal. The segmentation results of the traditional vertical projection profile and the proposed method are compared with respect to a few selected fonts and the latter, with an average accuracy of approximately 94%, has shown encouraging results.

  9. FCM Clustering Algorithms for Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    Yogita K. Dubey

    2016-01-01

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

  10. A hybrid algorithm for the segmentation of books in libraries

    Science.gov (United States)

    Hu, Zilong; Tang, Jinshan; Lei, Liang

    2016-05-01

    This paper proposes an algorithm for book segmentation based on bookshelves images. The algorithm can be separated into three parts. The first part is pre-processing, aiming at eliminating or decreasing the effect of image noise and illumination conditions. The second part is near-horizontal line detection based on Canny edge detector, and separating a bookshelves image into multiple sub-images so that each sub-image contains an individual shelf. The last part is book segmentation. In each shelf image, near-vertical line is detected, and obtained lines are used for book segmentation. The proposed algorithm was tested with the bookshelf images taken from OPIE library in MTU, and the experimental results demonstrate good performance.

  11. Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology

    Directory of Open Access Journals (Sweden)

    Guanghua Zhang

    2016-09-01

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

  12. Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm

    OpenAIRE

    Dong Qin

    2014-01-01

    Contraposing to the features of image data with high sparsity of and the problems on determination of clustering numbers, we try to put forward an color image segmentation algorithm, combined with semi-supervised machine learning technology and spectral graph theory. By the research of related theories and methods of spectral clustering algorithms, we introduce information entropy conception to design a method which can automatically optimize the scale parameter value. So it avoids the unstab...

  13. 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.

  14. An algorithm of image segmentation for overlapping grain image

    Institute of Scientific and Technical Information of China (English)

    WANG Zhi; JIN Guang; SUN Xiao-wei

    2005-01-01

    Aiming at measurement of granularity size of nonmetal grain, an algorithm of image segmentation and parameter calculation for microscopic overlapping grain image was studied. This algorithm presents some new attributes of graph sequence from discrete attribute of graph,and consequently achieves the geometrical characteristics from input graph, and the new graph sequence in favor of image segmentation is recombined. The conception that image edge denoted with "twin-point" is put forward, base on geometrical characters of point, image edge is transformed into serial edge, and on recombined serial image edge, based on direction vector definition of line and some additional restricted conditions, the segmentation twin-points are searched with, thus image segmentation is accomplished. Serial image edge is transformed into twin-point pattern, to realize calculation of area and granularity size of nonmetal grain. The inkling and uncertainty on selection of structure element which base on mathematical morphology are avoided in this algorithm, and image segmentation and parameter calculation are realized without changing grain's self statistical characters.

  15. Segment-based traffic smoothing algorithm for VBR video stream

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the VBR video stream by transmitting data in a series of fixed rates. We propose in this paper a novel segment-based bandwidth allocation algorithm which dynamically adjusts the segmentation boundary and changes the transmission rate at the latest possible point so that the video segment will be extended as long as possible and the number of rate changes can be as small as possible while keeping the peak rate low. Simulation results showed that our approach has small bandwidth requirement, high bandwidth utilization and low computation cost.

  16. Split Bregman's algorithm for three-dimensional mesh segmentation

    Science.gov (United States)

    Habiba, Nabi; Ali, Douik

    2016-05-01

    Variational methods have attracted a lot of attention in the literature, especially for image and mesh segmentation. The methods aim at minimizing the energy to optimize both edge and region detections. We propose a spectral mesh decomposition algorithm to obtain disjoint but meaningful regions of an input mesh. The related optimization problem is nonconvex, and it is very difficult to find a good approximation or global optimum, which represents a challenge in computer vision. We propose an alternating split Bregman algorithm for mesh segmentation, where we extended the image-dedicated model to a three-dimensional (3-D) mesh one. By applying our scheme to 3-D mesh segmentation, we obtain fast solvers that can outperform various conventional ones, such as graph-cut and primal dual methods. A consistent evaluation of the proposed method on various public domain 3-D databases for different metrics is elaborated, and a comparison with the state-of-the-art is performed.

  17. Video Segmentation Using Fast Marching and Region Growing Algorithms

    Directory of Open Access Journals (Sweden)

    Eftychis Sifakis

    2002-04-01

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

  18. Object Recognition Algorithm Utilizing Graph Cuts Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Zhaofeng Li

    2014-02-01

    Full Text Available This paper concentrates on designing an object recognition algorithm utilizing image segmentation. The main innovations of this paper lie in that we convert the image segmentation problem into graph cut problem, and then the graph cut results can be obtained by calculating the probability of intensity for a given pixel which is belonged to the object and the background intensity. After the graph cut process, the pixels in a same component are similar, and the pixels in different components are dissimilar. To detect the objects in the test image, the visual similarity between the segments of the testing images and the object types deduced from the training images is estimated. Finally, a series of experiments are conducted to make performance evaluation. Experimental results illustrate that compared with existing methods, the proposed scheme can effectively detect the salient objects. Particularly, we testify that, in our scheme, the precision of object recognition is proportional to image segmentation accuracy

  19. Approximation Algorithm for Line Segment Coverage for Wireless Sensor Network

    CERN Document Server

    Dash, Dinesh; Gupta, Arobinda; Nandy, Subhas C

    2010-01-01

    The coverage problem in wireless sensor networks deals with the problem of covering a region or parts of it with sensors. In this paper, we address the problem of covering a set of line segments in sensor networks. A line segment ` is said to be covered if it intersects the sensing regions of at least one sensor distributed in that region. We show that the problem of ?nding the minimum number of sensors needed to cover each member in a given set of line segments in a rectangular area is NP-hard. Next, we propose a constant factor approximation algorithm for the problem of covering a set of axis-parallel line segments. We also show that a PTAS exists for this problem.

  20. A New Algorithm for Interactive Structural Image Segmentation

    CERN Document Server

    Noma, Alexandre; Consularo, Luis Augusto; Cesar, Roberto M Jr; Bloch, Isabelle

    2008-01-01

    This paper proposes a novel algorithm for the problem of structural image segmentation through an interactive model-based approach. Interaction is expressed in the model creation, which is done according to user traces drawn over a given input image. Both model and input are then represented by means of attributed relational graphs derived on the fly. Appearance features are taken into account as object attributes and structural properties are expressed as relational attributes. To cope with possible topological differences between both graphs, a new structure called the deformation graph is introduced. The segmentation process corresponds to finding a labelling of the input graph that minimizes the deformations introduced in the model when it is updated with input information. This approach has shown to be faster than other segmentation methods, with competitive output quality. Therefore, the method solves the problem of multiple label segmentation in an efficient way. Encouraging results on both natural and...

  1. A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION

    Directory of Open Access Journals (Sweden)

    Mutasem K. Alsmadi

    2014-01-01

    Full Text Available Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI. A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA with Fuzzy C-Means algorithm (FCM. In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

  2. 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.

  3. A comparative study of Image Region-Based Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Lahouaoui LALAOUI

    2013-07-01

    Full Text Available Image segmentation has recently become an essential step in image processing as it mainly conditions the interpretation which is done afterwards. It is still difficult to justify the accuracy of a segmentation algorithm, regardless of the nature of the treated image. In this paper we perform an objective comparison of region-based segmentation techniques such as supervised and unsupervised deterministic classification, non-parametric and parametric probabilistic classification. Eight methods among the well-known and used in the scientific community have been selected and compared. The Martin’s(GCE, LCE, probabilistic Rand Index (RI, Variation of Information (VI and Boundary Displacement Error (BDE criteria are used to evaluate the performance of these algorithms on Magnetic Resonance (MR brain images, synthetic MR image, and synthetic images. MR brain image are composed of the gray matter (GM, white matter (WM and cerebrospinal fluid (CSF and others, and the synthetic MR image composed of the same for real image and the plus edema, and the tumor. Results show that segmentation is an image dependent process and that some of the evaluated methods are well suited for a better segmentation.

  4. Optimization-Based Image Segmentation by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    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.

  5. Optimization-Based Image Segmentation by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    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.

  6. Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

    Science.gov (United States)

    Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K

    2013-12-01

    We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC.

  7. WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    P. Mathivanan

    2014-02-01

    Full Text Available In this paper we develop a system for writer identification which involves four processing steps like preprocessing, segmentation, feature extraction and writer identification using neural network. In the preprocessing phase the handwritten text is subjected to slant removal process for segmentation and feature extraction. After this step the text image enters into the process of noise removal and gray level conversion. The preprocessed image is further segmented by using morphological watershed algorithm, where the text lines are segmented into single words and then into single letters. The segmented image is feature extracted by Daubechies’5/3 integer wavelet transform to reduce training complexity [1, 6]. This process is lossless and reversible [10], [14]. These extracted features are given as input to our neural network for writer identification process and a target image is selected for each training process in the 2-layer neural network. With the several trained output data obtained from different target help in text identification. It is a multilingual text analysis which provides simple and efficient text segmentation.

  8. An Improved Image Segmentation Algorithm Based on MET Method

    Directory of Open Access Journals (Sweden)

    Z. A. Abo-Eleneen

    2012-09-01

    Full Text Available Image segmentation is a basic component of many computer vision systems and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, Kittler and Illingworth's minimum error thresholding (MET, improves the image segmentation effect obviously. Its simpler and easier to implement. However, it fails in the presence of skew and heavy-tailed class-conditional distributions or if the histogram is unimodal or close to unimodal. The Fisher information (FI measure is an important concept in statistical estimation theory and information theory. Employing the FI measure, an improved threshold image segmentation algorithm FI-based extension of MET is developed. Comparing with the MET method, the improved method in general can achieve more robust performance when the data for either class is skew and heavy-tailed.

  9. Medical Image Segmentation through Bat-Active Contour Algorithm

    Directory of Open Access Journals (Sweden)

    Rabiu O. Isah

    2017-01-01

    Full Text Available In this research work, an improved active contour method called Bat-Active Contour Method (BAACM using bat algorithm has been developed. The bat algorithm is incorporated in order to escape local minima entrapped into by the classical active contour method, stabilize contour (snake movement and accurately, reach boundary concavity. Then, the developed Bat-Active Contour Method was applied to a dataset of medical images of the human heart, bone of knee and vertebra which were obtained from Auckland MRI Research Group (Cardiac Atlas Website, University of Auckland. Set of similarity metrics, including Jaccard index and Dice similarity measures were adopted to evaluate the performance of the developed algorithm. Jaccard index values of 0.9310, 0.9234 and 0.8947 and Dice similarity values of 0.8341, 0.8616 and 0.9138 were obtained from the human heart, vertebra and bone of knee images respectively. The results obtained show high similarity measures between BA-ACM algorithm and expert segmented images. Moreso, traditional ACM produced Jaccard index values 0.5873, 0.5601, 0.6009 and Dice similarity values of 0.5974, 0.6079, 0.6102 in the human heart, vertebra and bone of knee images respectively. The results obtained for traditional ACM show low similarity measures between it and expertly segmented images. It is evident from the results obtained that the developed algorithm performed better compared to the traditional ACM

  10. A Genetic Algorithm for the Segmentation of Known Touching Objects

    Directory of Open Access Journals (Sweden)

    Edgar Scavino

    2009-01-01

    Full Text Available Problem statement: Segmentation is the first and fundamental step in the process of computer vision and object classification. However, complicate or similar colour pattern add complexity to the segmentation of touching objects. The objective of this study was to develop a robust technique for the automatic segmentation and classification of touching plastic bottles, whose features were previously stored in a database. Approach: Our technique was based on the possibility to separate the two objects by means of a segment of straight line, whose position was determined by a genetic approach. The initial population of the genetic algorithm was heuristically determined among a large set of cutting lines, while further generations were selected based on the likelihood of the two objects with the images stored in the database. Results: Extensive testing, which was performed on random couples out of a population of 50 bottles, showed that the correct segmentation could be achieved in success rates above 90% with only a limited number of both chromosomes and iterations, thus reducing the computing time. Conclusion: These findings proved the effectiveness of our method as far as touching plastic bottles are concerned. This technique, being absolutely general, can be extended to any situation in which the properties of single objects were previously stored in a database.

  11. Simulated annealing spectral clustering algorithm for image segmentation

    Institute of Scientific and Technical Information of China (English)

    Yifang Yang; and Yuping Wang

    2014-01-01

    The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity mea-sure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering (SASC) algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm (SAGA), characterized by its rapid conver-gence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently ap-ply the algorithm to image segmentation, the Nystr¨om method is used to reduce the computation complexity. Experimental re-sults show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.

  12. 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.

  13. Extended Approach to Water Flow Algorithm for Text Line Segmentation

    Institute of Scientific and Technical Information of China (English)

    Darko Brodi(c)

    2012-01-01

    This paper proposes a new approach to the water flow algorithm for text line segmentation.In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as parameter.It is applied to the document image frame from left to right and vice versa.As a result,the unwetted and wetted areas are established.Thesc areas separate text from non-text elements in each text line,respectively.Hence,they represent the control areas that are of major importance for text line segmentation.Primarily,an extended approach means extraction of the connected-components by bounding boxes ovcr text.By this way,each connected component is mutually separated.Hence,the water flow angle,which defines the unwetted areas,is determined adaptively.By choosing appropriate water flow angle,the unwetted areas are lengthening which leads to the better text line segmentation.Results of this approach are encouraging due to the text line segmentation improvement which is the most challenging step in document image processing.

  14. A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm

    CERN Document Server

    Gomathi, M

    2010-01-01

    Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy CMeans Clustering (FCM) ...

  15. Crowdsourcing the creation of image segmentation algorithms for connectomics

    Directory of Open Access Journals (Sweden)

    Ignacio eArganda-Carreras

    2015-11-01

    Full Text Available To stimulate progress in automating the reconstruction of neural circuits,we organized the first international challenge on 2D segmentationof electron microscopic (EM images of the brain. Participants submittedboundary maps predicted for a test set of images, and were scoredbased on their agreement with ground truth from human experts. Thewinning team had no prior experience with EM images, and employeda convolutional network. This ``deep learning'' approach has sincebecome accepted as a standard for segmentation of EM images. The challengehas continued to accept submissions, and the best so far has resultedfrom cooperation between two teams. The challenge has probably saturated,as algorithms cannot progress beyond limits set by ambiguities inherentin 2D scoring. Retrospective evaluation of the challenge scoring systemreveals that it was not sufficiently robust to variations in the widthsof neurite borders. We propose a solution to this problem, which shouldbe useful for a future 3D segmentation challenge.

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

    Directory of Open Access Journals (Sweden)

    Carlos Hernández Medel

    2008-01-01

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

  17. 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 ...

  18. 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 ...

  19. Formal Photograph Compression Algorithm Based on Object Segmentation

    Institute of Scientific and Technical Information of China (English)

    Li Zhu; Guo-You Wang; Chen Wang

    2008-01-01

    Small storage space for photographs in formal documents is increasingly necessary in today's needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees (SPIHT) algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel (bpp)is equal to 0.078, peak signal-to-noise ratio (PSNR) of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.

  20. A time-consistent video segmentation algorithm designed for real-time implementation

    OpenAIRE

    Elhassani, Mohammed; Rivasseau, Delphine; Jehan-Besson, Stéphanie; Revenu, Marinette; Tschumperlé, David; Brun, Luc; Duranton, Marc

    2006-01-01

    International audience; In this paper, we propose a time consistent video segmentation algorithm designed for real-time implementation. Our segmentation algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images (flat or textured regions) with few parameters to be set. Temporal c...

  1. A modified region growing algorithm for multi-colored image object segmentation

    Institute of Scientific and Technical Information of China (English)

    Yuxi Chen; Chongzhao Han

    2007-01-01

    A hybrid algorithm based on seeded region growing and k-means clustering was proposed to improve image object segmentation result. A user friendly segmentation tool was provided for the definition of objects,then k-means algorithm was utilized to cluster the selected points into k seeds-clusters, finally the seeded region growing algorithm was used for object segmentation. Experimental results show that the proposed method is suitable for segmentation of multi-colored object, while conventional seeded region growing methods can only segment uniform-colored object.

  2. CSHURI - Modified HURI algorithm for Customer Segmentation and Transaction Profitability

    CERN Document Server

    Pillai, Jyothi

    2012-01-01

    Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead to a probable loss of profitable rules. The advantage of wealth of the customers' needs information and rules aids the retailer in designing his store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the customers based on some criteria; for example, a retail business may need to identify valuable customers who are major contributors to a company's overall profit. For a potential customer arriving in the store, which customer group one should belong ...

  3. New two-dimensional fuzzy C-means clustering algorithm for image segmentation

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation,a novel two-dimensional FCM clustering algorithm for image segmentation was proposed.In this method,the image segmentation was converted into an optimization problem.The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixcls described by the improved two-dimensional histogram.By making use of the global searching ability of the predator-prey particle swarm optimization,the optimal cluster center could be obtained by iterative optimization,and the image segmentation could be accomplished.The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%.The proposed algorithm has strong anti-noise capability,high clustering accuracy and good segment effect,indicating that it is an effective algorithm for image segmentation.

  4. An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm

    CERN Document Server

    Halder, Amiya

    2012-01-01

    This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.

  5. Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

    Institute of Scientific and Technical Information of China (English)

    Tian Chunna; Gao Xinbo

    2005-01-01

    A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex background and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

  6. 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.

  7. The new segment building algorithm for the cathode strip chambers in the CMS experiment

    CERN Document Server

    Golutvin, Igor; Pal'Chik, Vladimir; Voytishin, Nikolay; Zarubin, Anatoly

    2015-01-01

    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 standard algorithm. The new segment builder was tested with different Monte-Carlo data samples. The new algorithm is meant to be robust and effective fot the higher luminosity and hard muons that are expected in the future at LHC.

  8. Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

    NARCIS (Netherlands)

    Malan, D.F.; Botha, C.P.; Valstar, E.R.

    2012-01-01

    Purpose Automated patient-specific image-based segmentation of tissues surrounding aseptically loose hip prostheses is desired. For this we present an automated segmentation pipeline that labels periprosthetic tissues in computed tomography (CT). The intended application of this pipeline is in pre-o

  9. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

    Science.gov (United States)

    Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

  10. A Novel Face Segmentation Algorithm from a Video Sequence for Real-Time Face Recognition

    Directory of Open Access Journals (Sweden)

    Sudhaker Samuel RD

    2007-01-01

    Full Text Available The first step in an automatic face recognition system is to localize the face region in a cluttered background and carefully segment the face from each frame of a video sequence. In this paper, we propose a fast and efficient algorithm for segmenting a face suitable for recognition from a video sequence. The cluttered background is first subtracted from each frame, in the foreground regions, a coarse face region is found using skin colour. Then using a dynamic template matching approach the face is efficiently segmented. The proposed algorithm is fast and suitable for real-time video sequence. The algorithm is invariant to large scale and pose variation. The segmented face is then handed over to a recognition algorithm based on principal component analysis and linear discriminant analysis. The online face detection, segmentation, and recognition algorithms take an average of 0.06 second on a 3.2 GHz P4 machine.

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

    Science.gov (United States)

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

    2016-03-01

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

  12. 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.

  13. 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.

  14. Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jing; TANG Jilong; LIU Jibin; REN Chunying; LIU Xiangnan; FENG Jiang

    2009-01-01

    Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

  15. Integrating Real-Time Analysis With The Dendritic Cell Algorithm Through Segmentation

    CERN Document Server

    Gu, Feng; Aickelin, Uwe

    2010-01-01

    As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, to continuously detect misuses as soon as they occur. Consequently, the analysis process performed by an intrusion detection system must operate in real-time or near-to real-time. The analysis process of the DCA is currently performed offline, therefore to improve the algorithm's performance we suggest the development of a real-time analysis component. The initial step of the development is to apply segmentation to the DCA. This involves segmenting the current output of the DCA into slices and performing the analysis in various ways. Two segmentation approaches are introduced and tested in this paper, namely antigen based segmentation (ABS) and time based segmentation (TBS). The results of the corresponding experiments suggest that applying segmentation produces different and significantl...

  16. 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.

  17. 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%.

  18. Objective Performance Evaluation of Video Segmentation Algorithms with Ground-Truth

    Institute of Scientific and Technical Information of China (English)

    杨高波; 张兆扬

    2004-01-01

    While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation. Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy. Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames. The experimental results show the feasibility of our approach. Moreover, it is computationally more efficient than previous methods. It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.

  19. A moving object segmentation algorithm for static camera via active contours and GMM

    Institute of Scientific and Technical Information of China (English)

    WAN ChengKai; YUAN BaoZong; MIAO ZhenJiang

    2009-01-01

    Moving object segmentation is one of the most challenging Issues In computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mix-ture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.

  20. Robust protein microarray image segmentation using improved seeded region growing algorithm

    Institute of Scientific and Technical Information of China (English)

    Liqiang Wang(王立强); Xuxiang Ni(倪旭翔); Zukang Lu(陆祖康)

    2003-01-01

    Protein microarray technology has recently emerged as a powerful tool for biomedical research. Before automatic analysis the protein microarray images, protein spots in the images must be determined appropriately by spot segmentation algorithm. In this paper, an improved seeded region growing (ISRG)algorithm for protein microarray segmentation is presented, the seeds are obtained by finding the positions of the printed spots, and the protein spot regions are grown through these seeds. The experiment results show that the presented algorithm is accurate for adaptive shape segmentation and robust for protein microarray images contaminated by noise.

  1. Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms.

    Science.gov (United States)

    Yang, Miin-Shen; Lin, Karen Chia-Ren; Liu, Hsiu-Chih; Lirng, Jiing-Feng

    2007-02-01

    In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.

  2. The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

    Science.gov (United States)

    Chen, Xi; Zhou, Liqing

    2015-12-01

    With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

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

    Directory of Open Access Journals (Sweden)

    Xiaolin Zhang

    2015-01-01

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

  4. 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.

  5. 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.

  6. Fast algorithm for optimal graph-Laplacian based 3D image segmentation

    Science.gov (United States)

    Harizanov, S.; Georgiev, I.

    2016-10-01

    In this paper we propose an iterative steepest-descent-type algorithm that is observed to converge towards the exact solution of the ℓ0 discrete optimization problem, related to graph-Laplacian based image segmentation. Such an algorithm allows for significant additional improvements on the segmentation quality once the minimizer of the associated relaxed ℓ1 continuous optimization problem is computed, unlike the standard strategy of simply hard-thresholding the latter. Convergence analysis of the algorithm is not a subject of this work. Instead, various numerical experiments, confirming the practical value of the algorithm, are documented.

  7. Topology Correction of Segmented Medical Images using a Fast Marching Algorithm

    OpenAIRE

    2007-01-01

    We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that ...

  8. 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.

  9. 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.

  10. Applied anatomy of the atlantal segment of the vertebral artery%椎动脉寰椎部的应用解剖

    Institute of Scientific and Technical Information of China (English)

    郭徐华; 高宜录; 金国华

    2012-01-01

    Objective: To study the anatomy of the atlantal segment of the vertebral artery and to provide the basis of micro-vascular anatomy for clinical treatment. Methods: Adult cadaver heads were dissected in the study, and the morphology of the atlantal segment of the vertebral arteries were observed; The dimensions of the atlantal segment of vertebral arteries and their adjacent structures were measured. Results: The atlantal segments of the vertebral artery was divided into occipo-atlan-tal segment and atlanto-axoidean segment. The atlantal segment of vertebral artery had four permanent and continuous blood vessel loops. There were abundant paravertebral venous plexus around the atlantal segment of the vertebral artery. The anterior root of C2 permanent sticking to dorsal of occipo-atlantal segment of atlantal segment of the vertebral artery. Conclusion: The anatomy of the atlantal segment of vertebral artery and their adjacent structures have great clinical significance to avoid damaging the vertebral artery in a far lateral operative approach.%目的:研究椎动脉寰椎部的解剖,为临床应用提供解剖学依据.方法:成人尸头标本,解剖观察椎动脉寰椎部的形态结构,测量椎动脉寰椎部及其毗邻结构.结果:椎动脉寰椎部可分为寰枕段及寰枢椎段2部分;椎动脉寰椎部有恒定而连续的4个血管襻;周围有丰富的椎旁静脉丛,第2颈椎神经前根恒定地紧贴于椎动脉寰椎部寰枕段背侧.结论:对椎动脉寰椎部的解剖及其毗邻结构的研究,对于远外侧手术入路中避免椎动脉的损伤有重大临床意义.

  11. 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

  12. 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.

  13. Study on Control Algorithm for Continuous Segments Trajectory Interpolation

    Institute of Scientific and Technical Information of China (English)

    SHI Chuan; YE Peiqing; LV Qiang

    2006-01-01

    In CNC machining, the complexity of the part contour causes a series of problems including the repeated start-stop of the motor, low machining efficiency, and poor machining quality. To relieve those problems, a new interpolation algorithm was put forward to realize the interpolation control of continuous sections trajectory. The relevant error analysis of the algorithm was also studied. The feasibility of the algorithm was proved by machining experiment using a laser machine to carve the interpolation trajectory in the CNC system GT100. This algorithm effectively improved the machining efficiency and the contour quality.

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

    Science.gov (United States)

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

    2016-09-01

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

  15. Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions

    Science.gov (United States)

    Mazzaferri, Javier; Beaton, Luke; Hounye, Gisèle; Sayah, Diane N.; Costantino, Santiago

    2017-02-01

    The use of optical coherence tomography (OCT) to study ocular diseases associated with choroidal physiology is sharply limited by the lack of available automated segmentation tools. Current research largely relies on hand-traced, single B-Scan segmentations because commercially available programs require high quality images, and the existing implementations are closed, scarce and not freely available. We developed and implemented a robust algorithm for segmenting and quantifying the choroidal layer from 3-dimensional OCT reconstructions. Here, we describe the algorithm, validate and benchmark the results, and provide an open-source implementation under the General Public License for any researcher to use (https://www.mathworks.com/matlabcentral/fileexchange/61275-choroidsegmentation).

  16. 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.

  17. Coupling Regular Tessellation with Rjmcmc Algorithm to Segment SAR Image with Unknown Number of Classes

    Science.gov (United States)

    Wang, Y.; Li, Y.; Zhao, Q. H.

    2016-06-01

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

  18. 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

  19. Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation

    Directory of Open Access Journals (Sweden)

    P.Vasuda,

    2010-08-01

    Full Text Available Segmentation is an important aspect of medical image processing, where Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal Magnetic Resonance Images (MRI. Fuzzy clustering using Fuzzy C- Means (FCM algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. Theeffectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, convergence rate is compared between the conventional FCM and the Improved FCM.

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

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2014-01-01

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

  1. IMPROVED ALGORITHM FOR ROAD REGION SEGMENTATION BASED ON SEQUENTIAL MONTE-CARLO ESTIMATION

    Directory of Open Access Journals (Sweden)

    Zdenek Prochazka

    2014-12-01

    Full Text Available In recent years, many researchers and car makers put a lot of intensive effort into development of autonomous driving systems. Since visual information is the main modality used by human driver, a camera mounted on moving platform is very important kind of sensor, and various computer vision algorithms to handle vehicle surrounding situation are under intensive research. Our final goal is to develop a vision based lane detection system with ability to handle various types of road shapes, working on both structured and unstructured roads, ideally under presence of shadows. This paper presents a modified road region segmentation algorithm based on sequential Monte-Carlo estimation. Detailed description of the algorithm is given, and evaluation results show that the proposed algorithm outperforms the segmentation algorithm developed as a part of our previous work, as well as an conventional algorithm based on colour histogram.

  2. SEGMENTATION ALGORITHM BASED ON EDGE-SEARCHING FOR MUlTI-LINEAR STRUCTURED LIGHT IMAGES

    Institute of Scientific and Technical Information of China (English)

    LIU Baohua; LI Bing; JIANG Zhuangde

    2006-01-01

    Aiming at the problem that the existence of disturbances on the edges of light-stripe makes the segmentation of the light-stripes images difficult, a new segmentation algorithm based on edge-searching is presented. It firstly calculates every edge pixel's horizontal coordinate grads to produce the corresponding grads-edge, then uses a designed length-variable 1D template to scan the light-stripes' grads-edges. The template is able to fmd the disturbances with different width utilizing the distributing character of the edge disturbances. The found disturbances are eliminated finally. The algorithm not only can smoothly segment the light-stripes images, but also eliminate most disturbances on the light-stripes' edges without damaging the light-stripes images' 3D information. A practical example of using the proposed algorithm is given in the end. It is proved that the efficiency of the algorithm has been improved obviously by comparison.

  3. An approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms.

    Science.gov (United States)

    Brodic, Darko; Milivojevic, Dragan R; Milivojevic, Zoran N

    2011-01-01

    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.

  4. Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation

    Directory of Open Access Journals (Sweden)

    Xiao Sun

    2015-01-01

    Full Text Available 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.

  5. 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.

  6. 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

    2016-03-08

    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 con-tours 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

  7. Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm

    Science.gov (United States)

    Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin

    2016-09-01

    To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.

  8. A spatially constrained generative model and an EM algorithm for image segmentation.

    Science.gov (United States)

    Diplaros, Aristeidis; Vlassis, Nikos; Gevers, Theo

    2007-05-01

    In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model parameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data log-likelihood, where the penalty term is data-dependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are "smoothed" over pixels between each E- and M-step by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markov-based methods, and is in general faster.

  9. 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.)

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

    OpenAIRE

    Marar Liviu Ioan; Radulescu Adrian; Bacila Mihai-Florin

    2012-01-01

    The scope of relationship marketing is to retain customers and win their loyalty. This can be achieved if the companiesâ€(tm) products and services are developed and sold considering customersâ€(tm) demands. Fulfilling customersâ€(tm) demands, taken as the starting point of relationship marketing, can be obtained by acknowledging that the customersâ€(tm) needs and wishes are heterogeneous. The segmentation of the customersâ€(tm) base allows operators to overcome this because it illustrates th...

  11. Knowledge Automatic Indexing Based on Concept Lexicon and Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Lan-cheng; JIANG Dan; LE Jia-jin

    2005-01-01

    This paper is based on two existing theories about automatic indexing of thematic knowledge concept. The prohibit-word table with position information has been designed. The improved Maximum Matching-Minimum Backtracking method has been researched. Moreover it has been studied on improved indexing algorithm and application technology based on rules and thematic concept word table.

  12. On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms.

    Science.gov (United States)

    Rodrigues, Érick Oliveira; Cordeiro de Morais, Felipe Fernandes; Conci, Aura

    2015-01-01

    The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.

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

    Science.gov (United States)

    Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

    2016-12-01

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

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

    Science.gov (United States)

    He, Baochun; Ma, Zhiyuan; Zong, Mao; Zhou, Xiangrong; Fujita, Hiroshi

    2013-01-01

    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. PMID:24066017

  15. A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation

    Directory of Open Access Journals (Sweden)

    M. El Hassani

    2008-01-01

    Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination differences between frames and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.

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

    Science.gov (United States)

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

    2012-07-07

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

  17. Multi-level Threshold Image Segmentation Based on PSNR using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Cao Yun-Fei

    2012-01-01

    Full Text Available Image segmentation is still a crucial problem in image processing. It hasn yet been solved very well. In this study, we propose a novel multi-level thresholding image segmentation method based on PSNR using artificial bee colony algorithm (ABCA. PSNR is considered as an objective function of ABCA. The multi-level thresholds (t*1, t*2 ,...., t*n-1, t*n are those maximizing the PSNR. We compare entropy and PSNR in segmenting gray-level images. The experiments results demonstrate proposed method is effective and efficient.

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

    Science.gov (United States)

    Hatipoglu, Nuh; Bilgin, Gokhan

    2017-02-28

    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.

  19. An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes

    Science.gov (United States)

    2002-01-01

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

  20. OCT Segmentation Survey and Summary Reviews and a Novel 3D Segmentation Algorithm and a Proof of Concept Implementation

    CERN Document Server

    Mokhov, Serguei A

    2012-01-01

    We overview the existing OCT work, especially the practical aspects of it. We create a novel algorithm for 3D OCT segmentation with the goals of speed and/or accuracy while remaining flexible in the design and implementation for future extensions and improvements. The document at this point is a running draft being iteratively "developed" as a progress report as the work and survey advance. It contains the review and summarization of select OCT works, the design and implementation of the OCTMARF experimentation application and some results.

  1. 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

  2. Performance evaluation of a contextual news story segmentation algorithm

    Science.gov (United States)

    Janvier, Bruno; Bruno, Eric; Marchand-Maillet, Stephane; Pun, Thierry

    2006-01-01

    The problem of semantic video structuring is vital for automated management of large video collections. The goal is to automatically extract from the raw data the inner structure of a video collection; so that a whole new range of applications to browse and search video collections can be derived out of this high-level segmentation. To reach this goal, we exploit techniques that consider the full spectrum of video content; it is fundamental to properly integrate technologies from the fields of computer vision, audio analysis, natural language processing and machine learning. In this paper, a multimodal feature vector providing a rich description of the audio, visual and text modalities is first constructed. Boosted Random Fields are then used to learn two types of relationships: between features and labels and between labels associated with various modalities for improved consistency of the results. The parameters of this enhanced model are found iteratively by using two successive stages of Boosting. We experimented using the TRECvid corpus and show results that validate the approach over existing studies.

  3. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.

    Science.gov (United States)

    Litjens, Geert; Toth, Robert; van de Ven, Wendy; Hoeks, Caroline; Kerkstra, Sjoerd; van Ginneken, Bram; Vincent, Graham; Guillard, Gwenael; Birbeck, Neil; Zhang, Jindang; Strand, Robin; Malmberg, Filip; Ou, Yangming; Davatzikos, Christos; Kirschner, Matthias; Jung, Florian; Yuan, Jing; Qiu, Wu; Gao, Qinquan; Edwards, Philip Eddie; Maan, Bianca; van der Heijden, Ferdinand; Ghose, Soumya; Mitra, Jhimli; Dowling, Jason; Barratt, Dean; Huisman, Henkjan; Madabhushi, Anant

    2014-02-01

    Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (palgorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating

  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. Statistical Analysis for Performance Evaluation of Image Segmentation Quality Using Edge Detection Algorithms

    Directory of Open Access Journals (Sweden)

    T. Venkat Narayana Rao

    2011-11-01

    Full Text Available Edge detection is the most important feature of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms/operators. Computer vision is rapidly expanding field that depends on the capability to perform faster segments and thus to classify and infer images. Segmentation is central to the successful extraction of image features and their ensuing classification. Powerful segmentation techniques are available; however each technique is ad hoc. In this paper, the computer vision investigates the sub regions of the composite image, brings out commonly used and most important edge detection algorithms/operators with a wide-ranging comparative along with the statistical approach. This paper implements popular algorithms such as Sobel, Roberts, Prewitt, Laplacian of Gaussian and canny. A standard metric is used for evaluating the performance degradation of edge detection algorithms as a function of Peak Signal to Noise Ratio (PSNR along with the elapsed time for generating the segmented output image. A statistical approach to evaluate the variance among the PSNR and the time elapsed in output image is also incorporated. This paper provides a basis for objectively comparing the performance of different techniques and quantifies relative noise tolerance. Results shown allow selection of the most optimum method for application to image.

  6. 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.

  7. Minimum Error Thresholding Segmentation Algorithm Based on 3D Grayscale Histogram

    Directory of Open Access Journals (Sweden)

    Jin Liu

    2014-01-01

    Full Text Available Threshold segmentation is a very important technique. The existing threshold algorithms do not work efficiently for noisy grayscale images. This paper proposes a novel algorithm called three-dimensional minimum error thresholding (3D-MET, which is used to solve the problem. The proposed approach is implemented by an optimal threshold discriminant based on the relative entropy theory and the 3D histogram. The histogram is comprised of gray distribution information of pixels and relevant information of neighboring pixels in an image. Moreover, a fast recursive method is proposed to reduce the time complexity of 3D-MET from O(L6 to O(L3, where L stands for gray levels. Experimental results demonstrate that the proposed approach can provide superior segmentation performance compared to other methods for gray image segmentation.

  8. Surgical wound segmentation based on adaptive threshold edge detection and genetic algorithm

    Science.gov (United States)

    Shih, Hsueh-Fu; Ho, Te-Wei; Hsu, Jui-Tse; Chang, Chun-Che; Lai, Feipei; Wu, Jin-Ming

    2017-02-01

    Postsurgical wound care has a great impact on patients' prognosis. It often takes few days, even few weeks, for the wound to stabilize, which incurs a great cost of health care and nursing resources. To assess the wound condition and diagnosis, it is important to segment out the wound region for further analysis. However, the scenario of this strategy often consists of complicated background and noise. In this study, we propose a wound segmentation algorithm based on Canny edge detector and genetic algorithm with an unsupervised evaluation function. The results were evaluated by the 112 clinical images, and 94.3% of images were correctly segmented. The judgment was based on the evaluation of experimented medical doctors. This capability to extract complete wound regions, makes it possible to conduct further image analysis such as intelligent recovery evaluation and automatic infection requirements.

  9. An Adaptive Frame Skipping and VOP Interpolation Algorithm for Video Object Segmentation

    Institute of Scientific and Technical Information of China (English)

    YANGGaobo; ZHANGZhaoyang

    2004-01-01

    Video object segmentation is a key step for the successful use of MPEG-4. However, most of the current available segmentation algorithms are still far away from real-time performance. In order to improve the processing speed, an adaptive frame skipping and VOP interpolation algorithm is proposed in this paper. It adaptively determines the number of skipped frames based on the rigidity and motion complexity of video object. To interpolate the VOPs for skipped frames, a hi-directional projection scheme is adopted. Its principle is to perform a classification of those regions obtained by spatial segmentation for every frame in the sequence. It is valid for both rigid object and non-rigid object and can get good localization of object boundaries. Experimental results show that the proposed approach can improve the processing speed greatly while maintaining visually pleasant results.

  10. 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.

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

    Science.gov (United States)

    Lahanas, M; Baltas, D; Zamboglou, N

    2003-02-07

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Lahanas, M [Department of Medical Physics and Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach (Germany); Baltas, D [Department of Medical Physics and Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach (Germany); Zamboglou, N [Department of Medical Physics and Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach (Germany)

    2003-02-07

    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.

  13. 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...

  14. 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)

  15. A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm.

    Science.gov (United States)

    Farzinfar, Mahshid; Teoh, Eam Khwang; Xue, Zhong

    2011-11-01

    This study proposes an expectation-maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.

  16. GPU-based acceleration of an automatic white matter segmentation algorithm using CUDA.

    Science.gov (United States)

    Labra, Nicole; Figueroa, Miguel; Guevara, Pamela; Duclap, Delphine; Hoeunou, Josselin; Poupon, Cyril; Mangin, Jean-Francois

    2013-01-01

    This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics Processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the memory hierarchy available on the GPU, we obtain a speedup in execution time of 33.6 with respect to an optimized sequential version of the algorithm written in C, and of 240 with respect to the original Python/C++ implementation. The execution time is reduced from more than two hours to only 35 seconds for a subject dataset of 800,000 fibers, thus enabling applications that use interactive segmentation and visualization of small to medium-sized tractography datasets.

  17. Vessel Segmentation in Medical Imaging Using a Tight-Frame Based Algorithm

    CERN Document Server

    Cai, Xiaohao; Morigi, Serena; Sgallari, Fiorella

    2011-01-01

    Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the variational approach and the partial differential equation (PDE) modeling. In this paper, we propose to apply the tight-frame approach to automatically identify tube-like structures such as blood vessels in Magnetic Resonance Angiography (MRA) images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the tight-frame algorithm to denoise and smooth the possible boundary and sharpen the region. We prove the convergence of our algorithm. Numerical experiments on real 2D/3D MRA images demonstrate that our method is very efficient with convergence usually within a few iterations, ...

  18. Malleable Fuzzy Local Median C Means Algorithm for Effective Biomedical Image Segmentation

    Science.gov (United States)

    Rajendran, Arunkumar; Balakrishnan, Nagaraj; Varatharaj, Mithya

    2016-12-01

    The traditional way of clustering plays an effective role in the field of segmentation which was developed to be more effective and also in the recent development the extraction of contextual information can be processed with ease. This paper presents a modified Fuzzy C-Means (FCM) algorithm that provides the better segmentation in the contour grayscale regions of the biomedical images where effective cluster is needed. Malleable Fuzzy Local Median C-Means (M-FLMCM) is the proposed algorithm, proposed to overcome the disadvantage of the traditional FCM method in which the convergence time requirement is more, lack of ability to remove the noise, and the inability to cluster the contour region such as images. M-FLMCM shows promising results in the experiment with real-world biomedical images. The experiment results, with 96 % accuracy compared to the other algorithms.

  19. The Research of ECG Signal Automatic Segmentation Algorithm Based on Fractal Dimension Trajectory

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    <正>In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.

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

    Science.gov (United States)

    Kumar, Papendra; Kumar, Suresh

    2012-01-01

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

  1. A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set

    Directory of Open Access Journals (Sweden)

    Yanhui Guo

    2016-03-01

    Full Text Available Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory. At first, the image is transformed into NS domain, which is described by three membership sets (T, I and F. Then, a filter is newly defined and employed to reduce the indeterminacy of the image. Finally, a level set algorithm is used in the image after filtering operation for image segmentation. Experiments have been conducted using different images. The results demonstrate that the proposed method can segment the images effectively and accurately. It is especially able to remove the noise effect and extract the correct regions on both the noise-free images and the images with different levels of noise.

  2. Numerical arc segmentation algorithm for a radio conference - A software tool for communication satellite systems planning

    Science.gov (United States)

    Whyte, W. A.; Heyward, A. O.; Ponchak, D. S.; Spence, R. L.; Zuzek, J. E.

    1988-01-01

    A detailed description of a Numerical Arc Segmentation Algorithm for a Radio Conference (NASARC) software package for communication satellite systems planning is presented. This software provides a method of generating predetermined arc segments for use in the development of an allotment planning procedure to be carried out at the 1988 World Administrative Radio Conference (WARC - 88) on the use of the GEO and the planning of space services utilizing GEO. The features of the NASARC software package are described, and detailed information is given about the function of each of the four NASARC program modules. The results of a sample world scenario are presented and discussed.

  3. A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map

    Directory of Open Access Journals (Sweden)

    Bor-Woei Kuo

    2011-01-01

    Full Text Available Simultaneous Localization and Mapping (SLAM is an important technique for robotic system navigation. Due to the high complexity of the algorithm, SLAM usually needs long computational time or large amount of memory to achieve accurate results. In this paper, we present a lightweight Rao-Blackwellized particle filter- (RBPF- based SLAM algorithm for indoor environments, which uses line segments extracted from the laser range finder as the fundamental map structure so as to reduce the memory usage. Since most major structures of indoor environments are usually orthogonal to each other, we can also efficiently increase the accuracy and reduce the complexity of our algorithm by exploiting this orthogonal property of line segments, that is, we treat line segments that are parallel or perpendicular to each other in a special way when calculating the importance weight of each particle. Experimental results shows that our work is capable of drawing maps in complex indoor environments, needing only very low amount of memory and much less computational time as compared to other grid map-based RBPF SLAM algorithms.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-02-15

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

  5. A fast SVM training algorithm based on the set segmentation and k-means clustering

    Institute of Scientific and Technical Information of China (English)

    YANG Xiaowei; LIN Daying; HAO Zhifeng; LIANG Yanchun; LIU Guirong; HAN Xu

    2003-01-01

    At present, studies on training algorithms for support vector machines (SVM) are important issues in the field of machine learning. It is a challenging task to improve the efficiency of the algorithm without reducing the generalization performance of SVM. To face this challenge, a new SVM training algorithm based on the set segmentation and k-means clustering is presented in this paper. The new idea is to divide all the original training data into many subsets, followed by clustering each subset using k-means clustering and finally train SVM using the new data set obtained from clustering centroids. Considering that the decomposition algorithm such as SVMlight is one of the major methods for solving support vector machines, the SVMlight is used in our experiments. Simulations on different types of problems show that the proposed method can solve efficiently not only large linear classification problems but also large nonlinear ones.

  6. Code Synchronization Algorithm Based on Segment Correlation in Spread Spectrum Communication

    Directory of Open Access Journals (Sweden)

    Aohan Li

    2015-10-01

    Full Text Available Spread Spectrum (SPSP Communication is the theoretical basis of Direct Sequence Spread Spectrum (DSSS transceiver technology. Spreading code, modulation, demodulation, carrier synchronization and code synchronization in SPSP communications are the core parts of DSSS transceivers. This paper focuses on the code synchronization problem in SPSP communications. A novel code synchronization algorithm based on segment correlation is proposed. The proposed algorithm can effectively deal with the informational misjudgment caused by the unreasonable data acquisition times. This misjudgment may lead to an inability of DSSS receivers to restore transmitted signals. Simulation results show the feasibility of a DSSS transceiver design based on the proposed code synchronization algorithm. Finally, the communication functions of the DSSS transceiver based on the proposed code synchronization algorithm are implemented on Field Programmable Gate Array (FPGA.

  7. An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

    Directory of Open Access Journals (Sweden)

    Mengling Zhao

    2015-01-01

    Full Text Available As a computational intelligence method, artificial immune network (AIN algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN. The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC, and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

  8. 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.

  9. Automatic brain tumor segmentation with a fast Mumford-Shah algorithm

    Science.gov (United States)

    Müller, Sabine; Weickert, Joachim; Graf, Norbert

    2016-03-01

    We propose a fully-automatic method for brain tumor segmentation that does not require any training phase. Our approach is based on a sequence of segmentations using the Mumford-Shah cartoon model with varying parameters. In order to come up with a very fast implementation, we extend the recent primal-dual algorithm of Strekalovskiy et al. (2014) from the 2D to the medically relevant 3D setting. Moreover, we suggest a new confidence refinement and show that it can increase the precision of our segmentations substantially. Our method is evaluated on 188 data sets with high-grade gliomas and 25 with low-grade gliomas from the BraTS14 database. Within a computation time of only three minutes, we achieve Dice scores that are comparable to state-of-the-art methods.

  10. Research on algorithm about content-based segmentation and spatial transformation for stereo panorama

    Science.gov (United States)

    Li, Zili; Xia, Xuezhi; Zhu, Guangxi; Zhu, Yaoting

    2004-03-01

    The principle to construct G&IBMR virtual scene based on stereo panorama with binocular stereovision was put forward. Closed cubic B-splines have been used for content-based segmentation to virtual objects of stereo panorama and all objects in current viewing frustum would be ordered in current object linked list (COLL) by their depth information. The formula has been educed to calculate the depth information of a point in virtual scene by the parallax based on a parallel binocular vision model. A bilinear interpolation algorithm has been submitted to deform the segmentation template and take image splicing between three key positions. We also use the positional and directional transformation of binocular virtual camera bound to user avatar to drive the transformation of stereo panorama so as to achieve real-time consistency about perspective relationship and image masking. The experimental result has shown that the algorithm in this paper is effective and feasible.

  11. Multispectral image segmentation using parallel mean shift algorithm and CUDA technology

    Science.gov (United States)

    Zghidi, Hafedh; Walczak, Maksym; Świtoński, Adam

    2016-06-01

    We present a parallel mean shift algorithm running on CUDA and its possible application in segmentation of multispectral images. The aim of this paper is to present a method of analyzing highly noised multispectral images of various objects, so that important features are enhanced and easier to identify. The algorithm finds applications in analysis of multispectral images of eyes so that certain features visible only in specific wavelengths are made clearly visible despite high level of noise, for which processing time is very long.

  12. AUTOMATED DIGITAL MAMMOGRAM SEGMENTATION FOR DETECTION OF ABNORMAL MASSES USING BINARY HOMOGENEITY ENHANCEMENT ALGORITHM

    Directory of Open Access Journals (Sweden)

    Indra Kanta Maitra

    2011-06-01

    Full Text Available Many image processing techniques have been developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breastcancer can increase the survival rate, thus making screening programmes a mandatory step for females.Radiologists have to examine a large number of images. Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD systems that will improve detection of abnormalities in mammogram images. Extraction of the breast region by delineation of the breast contour and pectoral muscle allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. We need to performessential pre-processing steps to suppress artifacts, enhance the breast region and then extract breast region by the process of segmentation. In this paper we present a fully automated scheme for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI. We are using medio-lateral oblique (MLO view of mammograms. We have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA, followed by an innovative approach for edge detection (EDA. Then obtain the breast boundary by using our proposed Breast Boundary Detection Algorithm (BBDA. After we use our proposed Pectoral Muscle Detection Algorithm (PMDA to suppress the pectoral muscle thus obtaining the breast ROI, we use our proposed Anatomical Segmentation of Breast ROI (ASB algorithm to differentiate various regions within the breast. After segregating the different breast regions we use our proposed Seeded Region Growing Algorithm (SRGA to isolate normal and abnormal regions in the breast tissue. If any

  13. 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.

  14. Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation

    OpenAIRE

    segmentation, Outlier rejection fuzzy c-means (ORFCM)

    2013-01-01

    This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qua...

  15. AUTOMATIC SEGMENTATION ALGORITHM FOR THE LUMEN OF THE CAROTID ARTERY IN ULTRASOUND B-MODE IMAGES

    OpenAIRE

    Santos, AMF; João Manuel R. S. Tavares; Sousa, L.; Santos, R.; CASTRO,P; E. Azevedo

    2012-01-01

    A new algorithm is proposed for the identification and segmentation of the lumen and bifurcation boundaries of the carotid artery in 2D longitudinal ultrasound B-mode images. It uses the hipoechogenic characteristics defining the lumen of the carotid for its identification and echogenic characteristics for the identification of the bifurcation. The input image is preprocessed with the application of an anisotropic diffusion filter for speckle removal, and morphologic operators for the detecti...

  16. Therapy Operating Characteristic (TOC) Curves and their Application to the Evaluation of Segmentation Algorithms.

    Science.gov (United States)

    Barrett, Harrison H; Wilson, Donald W; Kupinski, Matthew A; Aguwa, Kasarachi; Ewell, Lars; Hunter, Robert; Müller, Stefan

    2010-01-01

    This paper presents a general framework for assessing imaging systems and image-analysis methods on the basis of therapeutic rather than diagnostic efficacy. By analogy to receiver operating characteristic (ROC) curves, it utilizes the Therapy Operating Characteristic or TOC curve, which is a plot of the probability of tumor control vs. the probability of normal-tissue complications as the overall level of a radiotherapy treatment beam is varied. The proposed figure of merit is the area under the TOC, denoted AUTOC. If the treatment planning algorithm is held constant, AUTOC is a metric for the imaging and image-analysis components, and in particular for segmentation algorithms that are used to delineate tumors and normal tissues. On the other hand, for a given set of segmented images, AUTOC can also be used as a metric for the treatment plan itself. A general mathematical theory of TOC and AUTOC is presented and then specialized to segmentation problems. Practical approaches to implementation of the theory in both simulation and clinical studies are presented. The method is illustrated with a a brief study of segmentation methods for prostate cancer.

  17. Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services

    Directory of Open Access Journals (Sweden)

    Chinedu Pascal Ezenkwu

    2015-10-01

    Full Text Available The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers’ needs will earn it greater leverage in providing targeted customer services and developing customised marketing programs for the customers. This understanding can be possible through systematic customer segmentation. Each segment comprises customers who share similar market characteristics. The ideas of Big data and machine learning have fuelled a terrific adoption of an automated approach to customer segmentation in preference to traditional market analyses that are often inefficient especially when the number of customers is too large. In this paper, the k-Means clustering algorithm is applied for this purpose. A MATLAB program of the k-Means algorithm was developed (available in the appendix and the program is trained using a z-score normalised two-feature dataset of 100 training patterns acquired from a retail business. The features are the average amount of goods purchased by customer per month and the average number of customer visits per month. From the dataset, four customer clusters or segments were identified with 95% accuracy, and they were labeled: High-Buyers-Regular-Visitors (HBRV, High-Buyers-Irregular-Visitors (HBIV, Low-Buyers-Regular-Visitors (LBRV and Low-Buyers-Irregular-Visitors (LBIV.

  18. A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction

    Institute of Scientific and Technical Information of China (English)

    Wen-Qian Deng; Xue-Mei Li; Xifeng Gao; Cai-Ming Zhang

    2016-01-01

    In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.

  19. 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.

  20. Image Segmentation Algorithm Based on Spectral Clustering Algorithm%谱聚类图像分割算法研究

    Institute of Scientific and Technical Information of China (English)

    张权; 胡玉兰

    2012-01-01

    针对谱聚类算法对图像分割效果差强人意的特点,研究了一种改进的Nystr(o)m算法进行谱聚类图像分割,使谱聚类算法应用于图像分割的效果有所改善.该算法首先对图像进行预处理,变换图像的分布数据空间,再分别计算对选定样本空间的数据间以及样本与其他空间的数据间的距离矩阵,并转化为相似矩阵;然后对相似矩阵正交化并且特征分解,进行K-Means聚类;最后将聚类结果进行后期处理.通过实验验证了该算法的有效性.%Spectral clustering algorithm to image segmentation was not perfect. An algorithm is proposed for spectral clustering image segmentation, which makes the effect of image segmentation better. Firstly, the image was pre-processed, transformed the distribution of the image data space, and calculated the distance matrix between the data of the selected sample space as well as samples and other space. It is transformed into a similarity matrix,what is more,the similarity matrix is made by orthogonal . The characteristics is decomposing by K-Means clustering; Finally, it took some steps for clustering results to be processed . Effectiveness of the algorithm is verified by experiment reasults.

  1. A de-noising algorithm to improve SNR of segmented gamma scanner for spectrum analysis

    Energy Technology Data Exchange (ETDEWEB)

    Li, Huailiang, E-mail: li-huai-liang@163.com [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010 (China); Tuo, Xianguo [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010 (China); State Key Laboratory of Geohazard Prevention & Geoenvironmental Protection, Chengdu University of Technology, Chengdu 610059 (China); Shi, Rui [State Key Laboratory of Geohazard Prevention & Geoenvironmental Protection, Chengdu University of Technology, Chengdu 610059 (China); Zhang, Jinzhao; Henderson, Mark Julian [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010 (China); Courtois, Jérémie; Yan, Minhao [State Key Laboratory Cultivation Base for Nonmetal Composites and Functional Materials, Southwest University of Science and Technology, Mianyang 621010 (China)

    2016-05-11

    An improved threshold shift-invariant wavelet transform de-noising algorithm for high-resolution gamma-ray spectroscopy is proposed to optimize the threshold function of wavelet transforms and reduce signal resulting from pseudo-Gibbs artificial fluctuations. This algorithm was applied to a segmented gamma scanning system with large samples in which high continuum levels caused by Compton scattering are routinely encountered. De-noising data from the gamma ray spectrum measured by segmented gamma scanning system with improved, shift-invariant and traditional wavelet transform algorithms were all evaluated. The improved wavelet transform method generated significantly enhanced performance of the figure of merit, the root mean square error, the peak area, and the sample attenuation correction in the segmented gamma scanning system assays. We also found that the gamma energy spectrum can be viewed as a low frequency signal as well as high frequency noise superposition by the spectrum analysis. Moreover, a smoothed spectrum can be appropriate for straightforward automated quantitative analysis.

  2. Image Segmentation and Maturity Recognition Algorithm based on Color Features of Lingwu Long Jujube

    Directory of Open Access Journals (Sweden)

    Yutan Wang

    2013-12-01

    Full Text Available Fruits’ recognition under natural scenes is a key technology to intelligent automatic picking. In this study, an image segmentation method based on color difference fusion in RGB color space was proposed in order to implement image segmentation and recognition maturity intelligently according to Lingwu long jujubes’ color features under the complex environment. Firstly, the three-dimensional histograms of each color component which is widely used in color space currently are compared; and then the jujubes’ red area and non-red area was extracted respectively, thus, the whole target area is obtained by sum of those areas; then, watershed algorithm combined with mathematical morphology distance and gradient was utilized to overcome adhesion and occlusion phenomena; finally, the maturity level was recognized by the established recognition model of Lingwu long jujubes. The segmentation was tested through 100 sample set and 93.27% of precision rate was attained, so was correct estimating rate of maturity level recognition above 90%. The results indicate that a smaller average segmentation error probability is in this method, which is more efficient in the extraction and recognition of jujubes with red and green and the problem of segmentation and maturity level judgment of adhesive fruits is solved by the method as well.

  3. 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; del Arco, Maria J Sanchez; 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).

  4. A new segmentation algorithm for lunar surface terrain based on CCD images

    Science.gov (United States)

    Jiang, Hong-Kun; Tian, Xiao-Lin; Xu, Ao-Ao

    2015-09-01

    Terrain classification is one of the critical steps used in lunar geomorphologic analysis and landing site selection. Most of the published works have focused on a Digital Elevation Model (DEM) to distinguish different regions of lunar terrain. This paper presents an algorithm that can be applied to lunar CCD images by blocking and clustering according to image features, which can accurately distinguish between lunar highland and lunar mare. The new algorithm, compared with the traditional algorithm, can improve classification accuracy. The new algorithm incorporates two new features and one Tamura texture feature. The new features are generating an enhanced image histogram and modeling the properties of light reflection, which can represent the geological characteristics based on CCD gray level images. These features are applied to identify texture in order to perform image clustering and segmentation by a weighted Euclidean distance to distinguish between lunar mare and lunar highlands. The new algorithm has been tested on Chang'e-1 CCD data and the testing result has been compared with geological data published by the U.S. Geological Survey. The result has shown that the algorithm can effectively distinguish the lunar mare from highlands in CCD images. The overall accuracy of the proposed algorithm is satisfactory, and the Kappa coefficient is 0.802, which is higher than the result of combining the DEM with CCD images.

  5. 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.

  6. Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation

    Directory of Open Access Journals (Sweden)

    Nouredine AITALI

    2016-03-01

    Full Text Available Image segmentation in the medical field is one of the most important phases to diseases diagnosis. The bias field estimation algorithm is the most interesting techniques to correct the in-homogeneity intensity artifact on the image. However, the use of such technique requires a powerful processing and quite expensive for big size as medical images. Hence the idea of parallelism becomes increasingly required. Several researchers have followed this path mainly in the bioinformatics field where they have suggested different algorithms implementations. In this paper, a novel Single Instruction Multiple Data (SIMD architecture for bias field estimation and image segmentation algorithm is proposed. In order to accelerate compute-intensive portions of the sequential implementation, we have implemented this algorithm on three different graphics processing units (GPU cards named GT740m, GTX760 and GTX580 respectively, using Compute Unified Device Architecture (CUDA software programming tool. Numerical obtained results for the computation speed up, allowed us to conclude on the suitable GPU architecture for this kind of applications and closest ones.

  7. SEGMENTATION OF CT SCAN LUMBAR SPINE IMAGE USING MEDIAN FILTER AND CANNY EDGE DETECTION ALGORITHM

    Directory of Open Access Journals (Sweden)

    E.Punarselvam

    2013-09-01

    Full Text Available The lumbar vertebrae are the largest segments of the movable part of the vertebral column, they are elected L1 to L5, starting at the top. The spinal column, more commonly called the backbone, is made up primarily of vertebrae discs, and the spinal cord. Acting as a communication conduit for the brain, signals are transmitted and received through the spinal cord. It is otherwise known as vertebralcolumn consists of 24 separate bony vertebrae together with 5 fused vertebrae, it is the unique interaction between the solid and fluid components that provides the disc strength and flexibility required to bear loading of the lumbar spine. In this work the Segmentation of Spine Image using Median Filter and Canny Edge Detection Algorithm between lumbar spine CT scan spine disc image. The result shows thatthe canny edge detection algorithm produced better result when compared other edge detection algorithm. Finding the correct boundary in a noisy image of spine disc is still a difficult one. To find outabsolute edges from noisy images, the comparative result can be verified and validated with the standard medical values. The result shows that the canny edge detection algorithm performs well and produced a solution very nearer to the optimal solution. This method is vigorous for all kinds of noisy images.

  8. Co-adaptability solution to conflict events in construction projects by segmented hierarchical algorithm

    Institute of Scientific and Technical Information of China (English)

    HOU XueLiang; LU Mei

    2008-01-01

    In order to seek the co-adaptability solution to conflict events in construction en-gineering projects,a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model.By this new algorithm,local solutions to the first-order transformation of co-adaptability for conflict events can be ob-tained,based upon which,a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction de-gree of local solutions.The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently,but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.

  9. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells.

    Science.gov (United States)

    Helmuth, Jo A; Burckhardt, Christoph J; Koumoutsakos, Petros; Greber, Urs F; Sbalzarini, Ivo F

    2007-09-01

    Biological trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the underlying mechanisms. In this work, we develop a novel trajectory segmentation algorithm based on supervised support vector classification. The algorithm is validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift are efficiently detected. Additionally, directed motion is found for viruses in the cytoplasm. The algorithm enables the linking of microscopic observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal transduction.

  10. Co-adaptability solution to conflict events in construction projects by segmented hierarchical algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In order to seek the co-adaptability solution to conflict events in construction engineering projects, a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model. By this new algorithm, local solutions to the first-order transformation of co-adaptability for conflict events can be obtained, based upon which, a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction degree of local solutions. The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently, but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.

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

    DEFF Research Database (Denmark)

    Nadernejad, E; Barari, Amin

    2011-01-01

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

  12. Microsurgical anatomy of the third segment of vertebral artery%椎动脉第三段的显微解剖研究

    Institute of Scientific and Technical Information of China (English)

    杨帆; 佟小光; 洪健; 靳峥

    2009-01-01

    Objective To accumulate the morphological data of the third segment of vertebral artery and to provide the basis of microvascular anatomy for clinical treatment. Method Ten adult cadaver heads (20 sides)were used in the study. The morphology of the third segment of vertebral arteries was observed and their dimensions were measured with the aid of an operating microscope. Results The third segments of the vertebral arteries were tortuous. The external diameters of left vertebral arteries(4. 01 ±1.12)mm were larger than that of the right ones (3.45± 0. 32) mm. The length of the third segment of the vertebral artery was(50. 93 ±8. 23)ram. There was no significant anatomic variation. Conclusions The third segment of vertebral artery is tortuous to enable the movements of neck. The far-lateral approach is often used to expose the third segment of vertebral artery. The suboccipital triangle may be an anatomic marker for the third segment of vertebral artery.%目的 研究椎动脉第三段(V3)的形态特点,为临床应用提供解剖学依据.方法 10例(20侧)成人尸头标本,解剖观察V3段形态结构,测量椎动脉和枕动脉长度、外径等解剖数据.结果 V3段有明显而连续的多个弯曲,V3段全长(50.93±8.23)mm,未见明显解剖变异.结论 V3段的连续弯曲可适应头颈部复杂的运动,V3段的显露常采用枕下远外侧入路,枕下三角是术中暴露识别V3段的重要标志.

  13. Comparison of normalization algorithms for cross-batch color segmentation of histopathological images.

    Science.gov (United States)

    Hoffman, Ryan A; Kothari, Sonal; Wang, May D

    2014-01-01

    Automated processing of digital histopathology slides has the potential to streamline patient care and provide new tools for cancer classification and grading. Before automatic analysis is possible, quality control procedures are applied to ensure that each image can be read consistently. One important quality control step is color normalization of the slide image, which adjusts for color variances (batch-effects) caused by differences in stain preparation and image acquisition equipment. Color batch-effects affect color-based features and reduce the performance of supervised color segmentation algorithms on images acquired separately. To identify an optimal normalization technique for histopathological color segmentation applications, five color normalization algorithms were compared in this study using 204 images from four image batches. Among the normalization methods, two global color normalization methods normalized colors from all stain simultaneously and three stain color normalization methods normalized colors from individual stains extracted using color deconvolution. Stain color normalization methods performed significantly better than global color normalization methods in 11 of 12 cross-batch experiments (pnormalization method using k-means clustering was found to be the best choice because of high stain segmentation accuracy and low computational complexity.

  14. A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Theiler, J.; Gisler, G.

    1997-07-01

    The recent and continuing construction of multi and hyper spectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The reduction of this voluminous data to useful intermediate forms is necessary both for downlinking all those bits and for interpreting them. Smart onboard hardware is required, as well as sophisticated earth bound processing. A segmented image (in which the multispectral data in each pixel is classified into one of a small number of categories) is one kind of intermediate form which provides some measure of data compression. Traditional image segmentation algorithms treat pixels independently and cluster the pixels according only to their spectral information. This neglects the implicit spatial information that is available in the image. We will suggest a simple approach; a variant of the standard k-means algorithm which uses both spatial and spectral properties of the image. The segmented image has the property that pixels which are spatially contiguous are more likely to be in the same class than are random pairs of pixels. This property naturally comes at some cost in terms of the compactness of the clusters in the spectral domain, but we have found that the spatial contiguity and spectral compactness properties are nearly orthogonal, which means that we can make considerable improvements in the one with minimal loss in the other.

  15. Reconstruction-by-Dilation and Top-Hat Algorithms for Contrast Enhancement and Segmentation of Microcalcifications in Digital Mammograms

    Science.gov (United States)

    Diaz, Claudia C.

    2007-11-01

    I present some results of contrast enhancement and segmentation of microcalcifications in digital mammograms. These mammograms were obtained from MIAS-minidatabase and using a CR to digitize images. White-top-hat and black-top-hat transformations were used to improve the contrast of images, while reconstruction-by-dilation algorithm was used to emphasize the microcalcifications over the tissues. Segmentation was done using different gradient matrices. These algorithms intended to show some details which were not evident in original images.

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

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Barari, Amin

    2011-01-01

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

  17. 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

  18. Numerical arc segmentation algorithm for a radio conference: A software tool for communication satellite systems planning

    Science.gov (United States)

    Whyte, W. A.; Heyward, A. O.; Ponchak, D. S.; Spence, R. L.; Zuzek, J. E.

    1988-01-01

    The Numerical Arc Segmentation Algorithm for a Radio Conference (NASARC) provides a method of generating predetermined arc segments for use in the development of an allotment planning procedure to be carried out at the 1988 World Administrative Radio Conference (WARC) on the Use of the Geostationary Satellite Orbit and the Planning of Space Services Utilizing It. Through careful selection of the predetermined arc (PDA) for each administration, flexibility can be increased in terms of choice of system technical characteristics and specific orbit location while reducing the need for coordination among administrations. The NASARC software determines pairwise compatibility between all possible service areas at discrete arc locations. NASARC then exhaustively enumerates groups of administrations whose satellites can be closely located in orbit, and finds the arc segment over which each such compatible group exists. From the set of all possible compatible groupings, groups and their associated arc segments are selected using a heuristic procedure such that a PDA is identified for each administration. Various aspects of the NASARC concept and how the software accomplishes specific features of allotment planning are discussed.

  19. Digital Terrain from a Two-Step Segmentation and Outlier-Based Algorithm

    Science.gov (United States)

    Hingee, Kassel; Caccetta, Peter; Caccetta, Louis; Wu, Xiaoliang; Devereaux, Drew

    2016-06-01

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

  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 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.

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

    Science.gov (United States)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

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

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

    Science.gov (United States)

    Rudyanto, Rina D; Kerkstra, Sjoerd; van Rikxoort, Eva M; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, Ilkay; Ünay, Devrim; Kadipaşaoğlu, Kamuran; Estépar, Raúl San José; Ross, James C; Washko, George R; Prieto, Juan-Carlos; Hoyos, Marcela Hernández; Orkisz, Maciej; Meine, Hans; Hüllebrand, Markus; Stöcker, Christina; Mir, Fernando Lopez; Naranjo, Valery; Villanueva, Eliseo; Staring, Marius; Xiao, Changyan; Stoel, Berend C; Fabijanska, Anna; Smistad, Erik; Elster, Anne C; Lindseth, Frank; Foruzan, Amir Hossein; Kiros, Ryan; Popuri, Karteek; Cobzas, Dana; Jimenez-Carretero, Daniel; Santos, Andres; Ledesma-Carbayo, Maria J; Helmberger, Michael; Urschler, Martin; Pienn, Michael; Bosboom, Dennis G H; Campo, Arantza; Prokop, Mathias; de Jong, Pim A; Ortiz-de-Solorzano, Carlos; Muñoz-Barrutia, Arrate; van Ginneken, Bram

    2014-10-01

    The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

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

    Science.gov (United States)

    Karabiber, Fethullah; Vecchio, Pietro; Grassi, Giuseppe

    2011-12-01

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

  5. A quantum mechanics-based algorithm for vessel segmentation in retinal images

    Science.gov (United States)

    Youssry, Akram; El-Rafei, Ahmed; Elramly, Salwa

    2016-06-01

    Blood vessel segmentation is an important step in retinal image analysis. It is one of the steps required for computer-aided detection of ophthalmic diseases. In this paper, a novel quantum mechanics-based algorithm for retinal vessel segmentation is presented. The algorithm consists of three major steps. The first step is the preprocessing of the images to prepare the images for further processing. The second step is feature extraction where a set of four features is generated at each image pixel. These features are then combined using a nonlinear transformation for dimensionality reduction. The final step is applying a recently proposed quantum mechanics-based framework for image processing. In this step, pixels are mapped to quantum systems that are allowed to evolve from an initial state to a final state governed by Schrödinger's equation. The evolution is controlled by the Hamiltonian operator which is a function of the extracted features at each pixel. A measurement step is consequently performed to determine whether the pixel belongs to vessel or non-vessel classes. Many functional forms of the Hamiltonian are proposed, and the best performing form was selected. The algorithm is tested on the publicly available DRIVE database. The average results for sensitivity, specificity, and accuracy are 80.29, 97.34, and 95.83 %, respectively. These results are compared to some recently published techniques showing the superior performance of the proposed method. Finally, the implementation of the algorithm on a quantum computer and the challenges facing this implementation are introduced.

  6. MRI Mammogram Image Segmentation using NCut method and Genetic Algorithm with partial filters

    Directory of Open Access Journals (Sweden)

    Pitchumani Angayarkanni

    2011-01-01

    Full Text Available Cancer is one of the most common leading deadly diseases which affect men and women around the world. Among the cancer diseases, breast cancer is especially a concern in women. It has become a major health problem in developed and developing countries over the past 50 years and the incidence has increased in recent years. Recent trends in digital image processing are CAD systems, which are computerized tools designed to assist radiologists. Most of these systems are used for automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of breast increases. In this paper , the proposed algorithm uses partial filters to enhance the images and the Ncut method is applied to segment the malignant and benign regions , further genetic algorithm is applied to identify the nipple position followed by bilateral subtraction of the left and the right breast image to cluster the cancerous and non cancerous regions. The system is trained using Back Propagation Neural Network algorithm. Computational efficiency and accuracy of the proposed system are evaluated based on the Frequency Receiver Operating Characteristic curve(FROC. The algorithm are tested on 161 pairs of digitized mammograms from MIAS database. The Receiver Operating Characteristic curve leads to 99.987% accuracy in detection of cancerous masses.

  7. SAR Image Segmentation with Unknown Number of Classes Combined Voronoi Tessellation and Rjmcmc Algorithm

    Science.gov (United States)

    Zhao, Q. H.; Li, Y.; Wang, Y.

    2016-06-01

    This paper presents a novel segmentation method for automatically determining the number of classes in Synthetic Aperture Radar (SAR) images by combining Voronoi tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategy. Instead of giving the number of classes a priori, it is considered as a random variable and subject to a Poisson distribution. Based on Voronoi tessellation, the image is divided into homogeneous polygons. By Bayesian paradigm, a posterior distribution which characterizes the segmentation and model parameters conditional on a given SAR image can be obtained up to a normalizing constant; Then, a Revisable Jump Markov Chain Monte Carlo(RJMCMC) algorithm involving six move types is designed to simulate the posterior distribution, the move types including: splitting or merging real classes, updating parameter vector, updating label field, moving positions of generating points, birth or death of generating points and birth or death of an empty class. Experimental results with real and simulated SAR images demonstrate that the proposed method can determine the number of classes automatically and segment homogeneous regions well.

  8. Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape

    CERN Document Server

    Egger, Jan; Dukatz, Thomas; Kolodziej, Malgorzata; Zukic, Dzenan; Freisleben, Bernd; Nimsky, Christopher; 10.1371/journal.pone.0031064

    2012-01-01

    We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed nonuniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts...

  9. 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

  10. The algorithm study for using the back propagation neural network in CT image segmentation

    Science.gov (United States)

    Zhang, Peng; Liu, Jie; Chen, Chen; Li, Ying Qi

    2017-01-01

    Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can't accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.

  11. 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 ...

  12. 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 ...

  13. Hand Anatomy

    Science.gov (United States)

    ... Home Anatomy Bones Joints Muscles Nerves Vessels Tendons Anatomy The upper extremity is a term used to ... of the parts together. Learn more about the anatomy of the upper extremity using the links in ...

  14. 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 ...

  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. Development and validation of a segmentation-free polyenergetic algorithm for dynamic perfusion computed tomography.

    Science.gov (United States)

    Lin, Yuan; Samei, Ehsan

    2016-07-01

    Dynamic perfusion imaging can provide the morphologic details of the scanned organs as well as the dynamic information of blood perfusion. However, due to the polyenergetic property of the x-ray spectra, beam hardening effect results in undesirable artifacts and inaccurate CT values. To address this problem, this study proposes a segmentation-free polyenergetic dynamic perfusion imaging algorithm (pDP) to provide superior perfusion imaging. Dynamic perfusion usually is composed of two phases, i.e., a precontrast phase and a postcontrast phase. In the precontrast phase, the attenuation properties of diverse base materials (e.g., in a thorax perfusion exam, base materials can include lung, fat, breast, soft tissue, bone, and metal implants) can be incorporated to reconstruct artifact-free precontrast images. If patient motions are negligible or can be corrected by registration, the precontrast images can then be employed as a priori information to derive linearized iodine projections from the postcontrast images. With the linearized iodine projections, iodine perfusion maps can be reconstructed directly without the influence of various influential factors, such as iodine location, patient size, x-ray spectrum, and background tissue type. A series of simulations were conducted on a dynamic iodine calibration phantom and a dynamic anthropomorphic thorax phantom to validate the proposed algorithm. The simulations with the dynamic iodine calibration phantom showed that the proposed algorithm could effectively eliminate the beam hardening effect and enable quantitative iodine map reconstruction across various influential factors. The error range of the iodine concentration factors ([Formula: see text]) was reduced from [Formula: see text] for filtered back-projection (FBP) to [Formula: see text] for pDP. The quantitative results of the simulations with the dynamic anthropomorphic thorax phantom indicated that the maximum error of iodine concentrations can be reduced from

  17. An efficient algorithm for multiphase image segmentation with intensity bias correction.

    Science.gov (United States)

    Zhang, Haili; Ye, Xiaojing; Chen, Yunmei

    2013-10-01

    This paper presents a variational model for simultaneous multiphase segmentation and intensity bias estimation for images corrupted by strong noise and intensity inhomogeneity. Since the pixel intensities are not reliable samples for region statistics due to the presence of noise and intensity bias, we use local information based on the joint density within image patches to perform image partition. Hence, the pixel intensity has a multiplicative distribution structure. Then, the maximum-a-posteriori (MAP) principle with those pixel density functions generates the model. To tackle the computational problem of the resultant nonsmooth nonconvex minimization, we relax the constraint on the characteristic functions of partition regions, and apply primal-dual alternating gradient projections to construct a very efficient numerical algorithm. We show that all the variables have closed-form solutions in each iteration, and the computation complexity is very low. In particular, the algorithm involves only regular convolutions and pointwise projections onto the unit ball and canonical simplex. Numerical tests on a variety of images demonstrate that the proposed algorithm is robust, stable, and attains significant improvements on accuracy and efficiency over the state-of-the-arts.

  18. A two-dimensional Segmented Boundary Algorithm for complex moving solid boundaries in Smoothed Particle Hydrodynamics

    Science.gov (United States)

    Khorasanizade, Sh.; Sousa, J. M. M.

    2016-03-01

    A Segmented Boundary Algorithm (SBA) is proposed to deal with complex boundaries and moving bodies in Smoothed Particle Hydrodynamics (SPH). Boundaries are formed in this algorithm with chains of lines obtained from the decomposition of two-dimensional objects, based on simple line geometry. Various two-dimensional, viscous fluid flow cases have been studied here using a truly incompressible SPH method with the aim of assessing the capabilities of the SBA. Firstly, the flow over a stationary circular cylinder in a plane channel was analyzed at steady and unsteady regimes, for a single value of blockage ratio. Subsequently, the flow produced by a moving circular cylinder with a prescribed acceleration inside a plane channel was investigated as well. Next, the simulation of the flow generated by the impulsive start of a flat plate, again inside a plane channel, has been carried out. This was followed by the study of confined sedimentation of an elliptic body subjected to gravity, for various density ratios. The set of test cases was completed with the simulation of periodic flow around a sunflower-shaped object. Extensive comparisons of the results obtained here with published data have demonstrated the accuracy and effectiveness of the proposed algorithms, namely in cases involving complex geometries and moving bodies.

  19. 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.

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

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

    In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...

  1. Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms.

    Science.gov (United States)

    Zhu, Yanjie; Tan, Yongqing; Hua, Yanqing; Zhang, Guozhen; Zhang, Jianguo

    2012-06-01

    Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.

  2. A Fast Semiautomatic Algorithm for Centerline-Based Vocal Tract Segmentation

    Directory of Open Access Journals (Sweden)

    Anton A. Poznyakovskiy

    2015-01-01

    Full Text Available Vocal tract morphology is an important factor in voice production. Its analysis has potential implications for educational matters as well as medical issues like voice therapy. The knowledge of the complex adjustments in the spatial geometry of the vocal tract during phonation is still limited. For a major part, this is due to difficulties in acquiring geometry data of the vocal tract in the process of voice production. In this study, a centerline-based segmentation method using active contours was introduced to extract the geometry data of the vocal tract obtained with MRI during sustained vowel phonation. The applied semiautomatic algorithm was found to be time- and interaction-efficient and allowed performing various three-dimensional measurements on the resulting model. The method is suitable for an improved detailed analysis of the vocal tract morphology during speech or singing which might give some insights into the underlying mechanical processes.

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

    CERN Document Server

    Wang, Hai Peng; Liu, Shuang Quan; Fan, Xin; Cao, Xue Xiang; Chai, Pei; Shan, Bao Ci

    2014-01-01

    In scintillation detector, scintillation crystals are typically made into 2-dimension modular array. The location of incident gamma-ray need 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 crystal position lookup table which maps the inaccurate location calculated by Anger logic to the exact hitting crystal position has been proposed, Firstly, position histogram is segmented into disconnected regions. Then crystal marking points are labeled by finding the centroids of regions. Finally, crystal boundaries are determined and crystal position lookup table is generated. The scheme is evaluated by the whole-body PET scanner and breast dedicated SPECT detector developed by Institute of High Energy Physics, Chinese Academy of Sciences. The results demonstrate that the algorithm is accurate, efficient, robust and general purpose.

  4. 融合阈值法和边缘提取的图像分割算法%Image Segmentation Algorithm of Combined Threshold Segmentation and Edge Extraction

    Institute of Scientific and Technical Information of China (English)

    张恒; 高敏; 徐超

    2014-01-01

    针对阈值法分割目标区域不完整和边缘提取断续边缘多的问题,提出了一种融合阈值法分割和多尺度边缘提取的图像分割算法。该算法充分利用阈值分割提供的目标区域信息和边缘提取给出的边缘信息,采用基于区域生长的最大轮廓法连接断续边缘,填充目标孔洞,完成目标分割。利用 Matlab平台对该算法进行了验证,结果表明:该算法能有效分割出目标完整轮廓。%Aiming at the problem that the target area is incomplete processed by threshold segmentation and lots of intermittent edges retain after edge extraction,an image segmentation algorithm is proposed which combines the threshold segmentation and multi-scale edge extraction.The algorithm makes the best of the information of target area got from threshold segmentation and edge information provided by edge extraction,and then uses the maximum profile method based on region growing to connect the intermittent edge for filling target hole and completing target segmentation.The validity of this algorithm is proved by using Matlab,and the complete target profile can be extracted effectively.

  5. Genetic algorithms as a useful tool for trabecular and cortical bone segmentation.

    Science.gov (United States)

    Janc, K; Tarasiuk, J; Bonnet, A S; Lipinski, P

    2013-07-01

    The aim of this study was to find a semi-automatic method of bone segmentation on the basis of computed tomography (CT) scan series in order to recreate corresponding 3D objects. So, it was crucial for the segmentation to be smooth between adjacent scans. The concept of graphics pipeline computing was used, i.e. simple graphics filters such as threshold or gradient were processed in a manner that the output of one filter became the input of the second one resulting in so called pipeline. The input of the entire stream was the CT scan and the output corresponded to the binary mask showing where a given tissue is located in the input image. In this approach the main task consists in finding the suitable sequence, types and parameters of graphics filters building the pipeline. Because of the high number of desired parameters (in our case 96), it was decided to use a slightly modified genetic algorithm. To determine fitness value, the mask obtained from the parameters found through genetic algorithms (GA) was compared with those manually prepared. The numerical value corresponding to such a comparison has been defined by Dice's coefficient. Preparation of reference masks for a few scans among the several hundreds of them was the only action done manually by a human expert. Using this method, very good results both for trabecular and cortical bones were obtained. It has to be emphasized that as no real border exists between these two bone types, the manually prepared reference masks were quite conventional and therefore charged with errors. As GA is a non-deterministic method, the present work also contains a statistical analysis of the relations existing between various GA parameters and fitness function. Finally the best sets of the GA parameters are proposed.

  6. An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

    Energy Technology Data Exchange (ETDEWEB)

    Karakaya, Mahmut [ORNL; Barstow, Del R [ORNL; Santos-Villalobos, Hector J [ORNL; Boehnen, Chris Bensing [ORNL

    2013-01-01

    Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texture etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

  7. A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

    Science.gov (United States)

    Liu, Zhi; Xu, Shuqiong; Zhang, Yun; Chen, Chun Lung Philip

    2014-11-01

    This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

  8. An iris segmentation algorithm based on edge orientation for off-angle iris recognition

    Science.gov (United States)

    Karakaya, Mahmut; Barstow, Del; Santos-Villalobos, Hector; Boehnen, Christopher

    2013-03-01

    Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texture etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

  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

    Energy Technology Data Exchange (ETDEWEB)

    Ahmadian, Alireza; Ay, Mohammad R.; Sarkar, Saeed [Medical Sciences/University of Tehran, Research Center for Science and Technology in Medicine, Tehran (Iran); Medical Sciences/University of Tehran, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran (Iran); Bidgoli, Javad H. [Medical Sciences/University of Tehran, Research Center for Science and Technology in Medicine, Tehran (Iran); East Tehran Azad University, Department of Electrical and Computer Engineering, Tehran (Iran); Zaidi, Habib [Geneva University Hospital, Division of Nuclear Medicine, Geneva (Switzerland)

    2008-10-15

    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 ({mu}map), 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 {mu}maps 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

  11. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

    Science.gov (United States)

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly A

    2013-02-15

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.

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

    NARCIS (Netherlands)

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

    2014-01-01

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

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

    Science.gov (United States)

    Meena Prakash, R; Shantha Selva Kumari, R

    2017-01-01

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

  14. DIALIGN-T: An improved algorithm for segment-based multiple sequence alignment

    Directory of Open Access Journals (Sweden)

    Kaufmann Michael

    2005-03-01

    Full Text Available Abstract Background We present a complete re-implementation of the segment-based approach to multiple protein alignment that contains a number of improvements compared to the previous version 2.2 of DIALIGN. This previous version is superior to Needleman-Wunsch-based multi-alignment programs on locally related sequence sets. However, it is often outperformed by these methods on data sets with global but weak similarity at the primary-sequence level. Results In the present paper, we discuss strengths and weaknesses of DIALIGN in view of the underlying objective function. Based on these results, we propose several heuristics to improve the segment-based alignment approach. For pairwise alignment, we implemented a fragment-chaining algorithm that favours chains of low-scoring local alignments over isolated high-scoring fragments. For multiple alignment, we use an improved greedy procedure that is less sensitive to spurious local sequence similarities. To evaluate our method on globally related protein families, we used the well-known database BAliBASE. For benchmarking tests on locally related sequences, we created a new reference database called IRMBASE which consists of simulated conserved motifs implanted into non-related random sequences. Conclusion On BAliBASE, our new program performs significantly better than the previous version of DIALIGN and is comparable to the standard global aligner CLUSTAL W, though it is outperformed by some newly developed programs that focus on global alignment. On the locally related test sets in IRMBASE, our method outperforms all other programs that we evaluated.

  15. Metal Artifact Reduction and Segmentation of Dental Computerized Tomography Images Using Least Square Support Vector Machine and Mean Shift Algorithm.

    Science.gov (United States)

    Mortaheb, Parinaz; Rezaeian, Mehdi

    2016-01-01

    Segmentation and three-dimensional (3D) visualization of teeth in dental computerized tomography (CT) images are of dentists' requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. On the other hand, dental CT image segmentation is a difficult process because of the specific characteristics of the tooth's structure. This paper presents a method for automatic segmentation of dental CT images. We present a multi-step method, which starts with a preprocessing phase to reduce the metal artifact using the least square support vector machine. Integral intensity profile is then applied to detect each tooth's region candidates. Finally, the mean shift algorithm is used to partition the region of each tooth, and all these segmented slices are then applied for 3D visualization of teeth. Examining the performance of our proposed approach, a set of reliable assessment metrics is utilized. We applied the segmentation method on 14 cone-beam CT datasets. Functionality analysis of the proposed method demonstrated precise segmentation results on different sample slices. Accuracy analysis of the proposed method indicates that we can increase the sensitivity, specificity, precision, and accuracy of the segmentation results by 83.24%, 98.35%, 72.77%, and 97.62% and decrease the error rate by 2.34%. The experimental results show that the proposed approach performs well on different types of CT images and has better performance than all existing approaches. Moreover, segmentation results can be more accurate by using the proposed algorithm of metal artifact reduction in the preprocessing phase.

  16. Research on remote sensing image segmentation based on ant colony algorithm: take the land cover classification of middle Qinling Mountains for example

    Science.gov (United States)

    Mei, Xin; Wang, Qian; Wang, Quanfang; Lin, Wenfang

    2009-10-01

    Remote sensing image based on the complexity of the background features, has a wealth of spatial information, how to extract huge amounts of data in the region of interest is a serious problem. Image segmentation refers to certain provisions in accordance with the characteristics of the image into different regions, and it is the key of remote sensing image recognition and information extraction. Reasonably fast image segmentation algorithm is the base of image processing; traditional segmentation methods have a lot of the limitations. Traditional threshold segmentation method in essence is an ergodic process, the low efficiency impacts on its application. The ant colony algorithm is a populationbased evolutionary algorithm heuristic biomimetic, since proposed, it has been successfully applied to the TSP, job-shop scheduling problem, network routing problem, vehicle routing problem, as well as other cluster analysis. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. Improved ant colony algorithm can greatly enhance the speed of image segmentation, while reducing the noise on the image. The research background of this paper is land cover classification experiments according to the SPOT images of Qinling area. The image segmentation based on ant colony algorithm is carried out and compared with traditional methods. Experimental results show that improved the ant colony algorithm can quickly and accurately segment target, and it is an effective method of image segmentation, it also has laid a good foundation of image classification for the follow-up work.

  17. An Improved Character Segmentation Algorithm Based on Local Adaptive Thresholding Technique for Chinese NvShu Documents

    Directory of Open Access Journals (Sweden)

    Yangguang Sun

    2014-06-01

    Full Text Available For the structural characteristics of Chinese NvShu character, by combining the basic idea in LLT local threshold algorithm and introducing the maximal between-class variance algorithm into local windows, an improved character segmentation algorithm based on local adaptive thresholding technique for Chinese NvShu documents was presented in this paper. Because of designing the corresponding correction parameters for the threshold and using secondary search mechanism, our proposed method could not only automatically obtain local threshold, but also avoid the loss of the character image information and improve the accuracy of the character image segmentation. Experimental results demonstrated its capability to reduce the effect of background noise, especially for Chinese NvShu character images with uneven illumination and low contrast

  18. Phasing the segments of the Keck and Thirty Meter Telescopes via the narrowband phasing algorithm: chromatic effects

    Science.gov (United States)

    Chanan, Gary; Troy, Mitchell; Raouf, Nasrat

    2016-07-01

    The narrowband phasing algorithm that was originally developed at Keck has largely been replaced by a broad- band algorithm that, although it is slower and less accurate than the former, has proved to be much more robust. A systematic investigation into the lack of robustness of the narrowband algorithm has shown that it results from systematic errors (of order 20 nm) that are wavelength-dependent. These errors are not well-understood at present, but they do not appear to arise from instrumental effects in the Keck phasing cameras, or from the segment coatings. This leaves high spatial frequency aberrations or scattering within 60 mm of the segment edges as the most likely origin of the effect.

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

    Science.gov (United States)

    Cervantes-Sanchez, Fernando; Hernandez-Aguirre, Arturo; Solorio-Meza, Sergio; Ornelas-Rodriguez, Manuel; Torres-Cisneros, Miguel

    2016-01-01

    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. PMID:27738422

  20. 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.

  1. Robottens Anatomi

    DEFF Research Database (Denmark)

    Antabi, Mimo

    Artiklen "Robottens Anatomi - mellem kunst og videnskab". Handler om brugen af robotter i kunstens og videnskabens verden.......Artiklen "Robottens Anatomi - mellem kunst og videnskab". Handler om brugen af robotter i kunstens og videnskabens verden....

  2. Tooth anatomy

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/article/002214.htm Tooth anatomy To use the sharing features on this page, ... upper jawbone is called the maxilla. Images Tooth anatomy References Lingen MW. Head and neck. In: Kumar ...

  3. 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 ...

  4. 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 ...

  5. Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm.

    Science.gov (United States)

    Onoma, D P; Ruan, S; Thureau, S; Nkhali, L; Modzelewski, R; Monnehan, G A; Vera, P; Gardin, I

    2014-12-01

    A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.

  6. An algorithm for target airplane segmentation & extraction%一种目标飞机分割提取方法

    Institute of Scientific and Technical Information of China (English)

    谷东格

    2016-01-01

    In this paper, an algorithm for target airplane segmentation & extraction was proposed. In order to segment &extract the target airplane quickly and accurately, the algorithm adopted GrabCut algorithm which was improved by using pyramid segment tactics and based on color Gaussian Mixture Model and iterative energy minimum. The test results show that in the majority situation, this algorithm can accurately segment&extract the target airplane without using any other interaction while its processing speed is almost five times in comparison to the primary algorithm.%提出了一种目标飞机分割提取方法,该方法采用改进的使用金字塔式分割策略的以彩色高斯混合模型GMM (Gaussian Mixture Model)和迭代能量最小化为基础的GrabCut算法,达到将目标飞机快速精确分割提取的目的。实验结果表明在多数情况下,只需围绕目标飞机画一个框无需额外交互,就可以快速的将目标飞机精确分割提取出来,即便是在某些情况下不能够将目标飞机精确提取分割也只需额外的少数交互就可以达到将目标飞机精确分割提取的目的。

  7. 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.

  8. 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; Housden, R James; 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.

  9. Anatomy atlases.

    Science.gov (United States)

    Rosse, C

    1999-01-01

    Anatomy atlases are unlike other knowledge sources in the health sciences in that they communicate knowledge through annotated images without the support of narrative text. An analysis of the knowledge component represented by images and the history of anatomy atlases suggest some distinctions that should be made between atlas and textbook illustrations. Textbook and atlas should synergistically promote the generation of a mental model of anatomy. The objective of such a model is to support anatomical reasoning and thereby replace memorization of anatomical facts. Criteria are suggested for selecting anatomy texts and atlases that complement one another, and the advantages and disadvantages of hard copy and computer-based anatomy atlases are considered.

  10. A Review of Hand Segmentation Algorithm in Gesture Recognition%手势识别中手分割算法

    Institute of Scientific and Technical Information of China (English)

    郭雷

    2015-01-01

    Technical difficulties in hand segmentation as well as and the features that might be used in this process are generally analyzed at the beginning in this paper. After that, the ideas and features of existing hand segmentation algorithm are introduced and compared. At last, an introduction of deep learning technology and a conclusion of the future research direction of hand segmentation are carried out in this paper.%首先分析了手势分割存在的技术难点及人进行手势分割过程中可能使用的特征,然后分析比较了现有手势分割算法的基本思想和特点,最后介绍了深度学习技术并总结了手势分割未来的研究方向.

  11. Fingerprint Segmentation

    OpenAIRE

    Jomaa, Diala

    2009-01-01

    In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve th...

  12. Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation.

    Science.gov (United States)

    Cohen, Assaf; Rivlin, Ehud; Shimshoni, Ilan; Sabo, Edmond

    2015-07-01

    In this paper, we introduce a novel method for detection and segmentation of crypts in colon biopsies. Most of the approaches proposed in the literature try to segment the crypts using only the biopsy image without understanding the meaning of each pixel. The proposed method differs in that we segment the crypts using an automatically generated pixel-level classification image of the original biopsy image and handle the artifacts due to the sectioning process and variance in color, shape and size of the crypts. The biopsy image pixels are classified to nuclei, immune system, lumen, cytoplasm, stroma and goblet cells. The crypts are then segmented using a novel active contour approach, where the external force is determined by the semantics of each pixel and the model of the crypt. The active contour is applied for every lumen candidate detected using the pixel-level classification. Finally, a false positive crypt elimination process is performed to remove segmentation errors. This is done by measuring their adherence to the crypt model using the pixel level classification results. The method was tested on 54 biopsy images containing 4944 healthy and 2236 cancerous crypts, resulting in 87% detection of the crypts with 9% of false positive segments (segments that do not represent a crypt). The segmentation accuracy of the true positive segments is 96%.

  13. VALIDATION OF POINT CLOUDS SEGMENTATION ALGORITHMS THROUGH THEIR APPLICATION TO SEVERAL CASE STUDIES FOR INDOOR BUILDING MODELLING

    Directory of Open Access Journals (Sweden)

    H. Macher

    2016-06-01

    Full Text Available 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.

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

    Directory of Open Access Journals (Sweden)

    Yue Zhang

    2016-10-01

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

  15. 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.

  16. [Segmentation of Winter Wheat Canopy Image Based on Visual Spectral and Random Forest Algorithm].

    Science.gov (United States)

    Liu, Ya-dong; Cui, Ri-xian

    2015-12-01

    Digital image analysis has been widely used in non-destructive monitoring of crop growth and nitrogen nutrition status due to its simplicity and efficiency. It is necessary to segment winter wheat plant from soil background for accessing canopy cover, intensity level of visible spectrum (R, G, and B) and other color indices derived from RGB. In present study, according to the variation in R, G, and B components of sRGB color space and L*, a*, and b* components of CIEL* a* b* color space between wheat plant and soil background, the segmentation of wheat plant from soil background were conducted by the Otsu's method based on a* component of CIEL* a* b* color space, and RGB based random forest method, and CIEL* a* b* based random forest method, respectively. Also the ability to segment wheat plant from soil background was evaluated with the value of segmentation accuracy. The results showed that all three methods had revealed good ability to segment wheat plant from soil background. The Otsu's method had lowest segmentation accuracy in comparison with the other two methods. There were only little difference in segmentation error between the two random forest methods. In conclusion, the random forest method had revealed its capacity to segment wheat plant from soil background with only the visual spectral information of canopy image without any color components combinations or any color space transformation.

  17. Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation.

    Science.gov (United States)

    Rabbani, Hossein; Kafieh, Rahele; Kazemian Jahromi, Mahdi; Jorjandi, Sahar; Mehri Dehnavi, Alireza; Hajizadeh, Fedra; Peyman, Alireza

    2016-01-01

    Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise. In this paper, segmentation methods such as Gaussian Mixture Model (GMM), Graph Cut, and Level Set are used for automatic segmentation of three clinically important corneal layer boundaries on OCT images. Using the segmentation of the boundaries in three-dimensional corneal data, we obtained thickness maps of the layers which are created by these borders. Mean and standard deviation of the thickness values for normal subjects in epithelial, stromal, and whole cornea are calculated in central, superior, inferior, nasal, and temporal zones (centered on the center of pupil). To evaluate our approach, the automatic boundary results are compared with the boundaries segmented manually by two corneal specialists. The quantitative results show that GMM method segments the desired boundaries with the best accuracy.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-01-15

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

  19. 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....

  20. Integer anatomy

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-11-15

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

  1. 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.

  2. 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.

  3. Hepatic surgical anatomy.

    Science.gov (United States)

    Skandalakis, John E; Skandalakis, Lee J; Skandalakis, Panajiotis N; Mirilas, Petros

    2004-04-01

    The liver, the largest organ in the body, has been misunderstood at nearly all levels of organization, and there is a tendency to ignore details that do not fit the preconception. A complete presentation of the surgical anatomy of the liver includes the study of hepatic surfaces, margins, and fissures; the various classifications of lobes and segments; and the vasculature and lymphatics. A brief overview of the intrahepatic biliary tract is also presented.

  4. Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification

    Science.gov (United States)

    Huang, Jian; Liu, Gui-xiong

    2016-09-01

    The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm ( k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S r was classified by the k-NN algorithm with training set T z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler's numbers. Last, while the classification confidence coefficient equaled k, made S r as one sample of pre-training set T z '. The training set T z increased to T z+1 by T z ' if T z ' was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T 0 to T 5 by itself.

  5. An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

    OpenAIRE

    Mengling Zhao; Hongwei Liu

    2015-01-01

    As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new class...

  6. Comparative evaluation of a novel 3D segmentation algorithm on in-treatment radiotherapy cone beam CT images

    Science.gov (United States)

    Price, Gareth; Moore, Chris

    2007-03-01

    Image segmentation and delineation is at the heart of modern radiotherapy, where the aim is to deliver as high a radiation dose as possible to a cancerous target whilst sparing the surrounding healthy tissues. This, of course, requires that a radiation oncologist dictates both where the tumour and any nearby critical organs are located. As well as in treatment planning, delineation is of vital importance in image guided radiotherapy (IGRT): organ motion studies demand that features across image databases are accurately segmented, whilst if on-line adaptive IGRT is to become a reality, speedy and correct target identification is a necessity. Recently, much work has been put into the development of automatic and semi-automatic segmentation tools, often using prior knowledge to constrain some grey level, or derivative thereof, interrogation algorithm. It is hoped that such techniques can be applied to organ at risk and tumour segmentation in radiotherapy. In this work, however, we make the assumption that grey levels do not necessarily determine a tumour's extent, especially in CT where the attenuation coefficient can often vary little between cancerous and normal tissue. In this context we present an algorithm that generates a discontinuity free delineation surface driven by user placed, evidence based support points. In regions of sparse user supplied information, prior knowledge, in the form of a statistical shape model, provides guidance. A small case study is used to illustrate the method. Multiple observers (between 3 and 7) used both the presented tool and a commercial manual contouring package to delineate the bladder on a serially imaged (10 cone beam CT volumes ) prostate patient. A previously presented shape analysis technique is used to quantitatively compare the observer variability.

  7. 基于小波分形的图像分割算法%Wavelet Fractal-Based Image Segment Algorithm

    Institute of Scientific and Technical Information of China (English)

    叶俊勇; 汪同庆; 彭健; 杨波

    2002-01-01

    The image of shoe leather lumen is not very satisfaction because of technology of CT. The smart imagesegment is the base of getting smart measurement data. An algorithm of image segment based on wavelet and fractalhas been proposed after analyzing the specialty of images. The image is decomposed through wavelet multi-resolutiondecomposition , and the fractal dimension is calculated by the decomposed image. This approach is more satisfied thangeneral method in image segment of shoe leather lumen image by CT. This algorithm can segment the edge of shoe lu-men exactly. The experimentations prove the approach is rational.

  8. 基于线段特征匹配的EKF-SLAM算法%EKF-SLAM Algorithm Based on Line Segment Matching

    Institute of Scientific and Technical Information of China (English)

    张国良; 汤文俊; 敬斌; 程展欣

    2012-01-01

    针对EKF-SLAM算法在机器人被“绑架”时失效的问题,提出一种新的基于线段特征匹配的EKF-SLAM算法——EKFLineSLAM算法.该算法在线段特征观测模型和改进的基于逐点搜索的线段提取算法的基础上,将线段特征匹配引入EKF-SLAM算法,并对线段长度和姿态角进行EKF更新,创建环境的线段特征地图.在未知室内结构化环境中,将该算法与弱匹配EKFLineSLAM算法进行比较,验证了EKFLineSLAM算法在结构化环境中克服机器人“绑架”问题的可行性和有效性.%For the problem of EKF-SLAM algorithm being invalid when robot is kidnapped, a new EKF-SLAM algorithm called "EKF-LineSLAM Algorithm" based on line segment matching is presented. This algorithm is based on the line segment observation model, and the improved line segment extraction algorithm based on point by point search. It introduces the line segment match in the EKF-SLAM algorithm, and renews the line segment's length and posture angle by EKF to find the line segment characteristic map. Finally, the presented algorithm and the weak matching EKFLineSLAM algorithm are compared in an unknown structured indoor environment. The comparison results confirm the feasibility and the effectiveness of the EKFLineSLAM algorithm for the kidnapped robot problem in structured environments.

  9. THE SEGMENTATION OF A TEXT LINE FOR A HANDWRITTEN UNCONSTRAINED DOCUMENT USING THINING ALGORITHM

    NARCIS (Netherlands)

    Tsuruoka, S.; Adachi, Y.; Yoshikawa, T.

    2004-01-01

    For printed documents, the projection analysis of black pixels is widely used for the segmentation of a text line. However, for handwritten documents, we think that the projection analysis is not appropriate, as the separating border line of a text line is not a straight line on a paper with no rule

  10. 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.

  11. A Benchmark Data Set to Evaluate the Illumination Robustness of Image Processing Algorithms for Object Segmentation and Classification.

    Science.gov (United States)

    Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

    2015-01-01

    Developers of image processing routines rely on benchmark data sets to give qualitative comparisons of new image analysis algorithms and pipelines. Such data sets need to include artifacts in order to occlude and distort the required information to be extracted from an image. Robustness, the quality of an algorithm related to the amount of distortion is often important. However, using available benchmark data sets an evaluation of illumination robustness is difficult or even not possible due to missing ground truth data about object margins and classes and missing information about the distortion. We present a new framework for robustness evaluation. The key aspect is an image benchmark containing 9 object classes and the required ground truth for segmentation and classification. Varying levels of shading and background noise are integrated to distort the data set. To quantify the illumination robustness, we provide measures for image quality, segmentation and classification success and robustness. We set a high value on giving users easy access to the new benchmark, therefore, all routines are provided within a software package, but can as well easily be replaced to emphasize other aspects.

  12. Segmentation d'images par l'algorithme des flot maximum continu

    OpenAIRE

    2012-01-01

    In recent years, with the advance of computing equipment and image acquisition techniques, the sizes, dimensions and content of acquired images have increased considerably. Unfortunately as time passes there is a steadily increasing gap between the classical and parallel programming paradigms and their actual performance on modern computer hardware. In this thesis we consider in depth one particular algorithm, the continuous maximum flow computation. We review in detail why this algorithm is ...

  13. Segmentation algorithm of chemical molecular structure images%化学分子结构图分割算法

    Institute of Scientific and Technical Information of China (English)

    管燕; 李存华; 仲兆满; 孙兰兰

    2012-01-01

    为了将化学分子结构图中化学键与杂原子、基团分割开,提出了基于区域尺寸和弯曲度的化学分子结构图分割算法。首先,根据连通区域尺寸大小,将化学分子结构图分割成两部分:一部分是由化学键组成的所有大尺寸连通区域的组合,另一部分是杂原子、基团和单化学键等小尺寸区域的组合。然后,根据弯曲度将小尺寸组合图中的表示化学键的单线段和类线段“I”、“1”、“-”提取出。最后根据位置等信息将“I”、“1”、“-”和单线段进行区分,将单线段的化学键和大尺寸连通区组合,实现了化学键与杂原子、基团的分离。实验结果表明,该图像分割算法准确率高达98.3%,与人类视觉感知具有一致性。这为后续的化学分子结构图像信息的自动提取奠定了基础。%The segmentation algorithm of chemical molecular structure based on area size and bending degree was proposed to segment chemical bonds, heteroatoms and perssad. First, chemical molecular structure images were segmented into two parts according to connection area size. One was the combination of all big size connection areas containing chemical bonds, and the other was the combination of heteroatoms, perssad and single chemical bonds. Second, single lines were extracted based on bending degree, which represents small size combination images, and like-lines such as "I" ,"l" and " - ". Finally, "I" ,"l", " - " and single lines were distinguished, chemical bonds of single lines and big size connection areas were combined, and the segmentation of chemical bonds, heteroatoms and perssad was realized. The accuracy by the proposed algorithm reached to 98. 3%, and the segmentation effect was consistent with human visual perception. This is the foundation for automatically extracting chemical molecular structure images.

  14. Silence and Speech Segmentation for Noisy Speech Using a Wavelet Based Algorithm

    Institute of Scientific and Technical Information of China (English)

    MEI Xiaodan; SUN Shenghe

    2001-01-01

    In this paper, we present a newmethod to segment silence and speech for noisy con-dition. Conventional segmentation methods usuallylack in robustness under high background noise be-cause they are mostly dependent on amplitude or en-ergy of speech signal. For speech signal, the corre-lation between the neighbor frequency bands includ-ing most speech energy is high and little effected bynoise, but the correlation between the neighbor fre-quency bands of noise is low. So we employ the cross-correlation of neighbor sub-bands of signal to locatespeech and noise. We first performed the wavelettransform to denoise and further calculated the cross-correlation between the wavelet coefficients in two se-lected sub-bands and then used the standard deviationof cross-correlation coefficients to segment speech andnoise duration. The simulation and the result analysisshow that this method is efficient for the low-energyphonemes even in low signal-to-noise ratio, and theamount of computation is less.

  15. 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.

  16. Robottens Anatomi

    DEFF Research Database (Denmark)

    Antabi, Mimo

    Rapport der beskriver de samlede erfaringer fra arbejdet med produktionen af teaterforestillingen Robottens Anatomi. Indehoder bl.a. interviews med medvirkende, bidrag fra instruktør, synopsis, beskrivelse af scenografi mv.......Rapport der beskriver de samlede erfaringer fra arbejdet med produktionen af teaterforestillingen Robottens Anatomi. Indehoder bl.a. interviews med medvirkende, bidrag fra instruktør, synopsis, beskrivelse af scenografi mv....

  17. A effective immune multi-objective algorithm for SAR imagery segmentation

    Science.gov (United States)

    Yang, Dongdong; Jiao, Licheng; Gong, Maoguo; Si, Xiaoyun; Li, Jinji; Feng, Jie

    2009-10-01

    A novel and effective immune multi-objective clustering algorithm (IMCA) is presented in this study. Two conflicting and complementary objectives, called compactness and connectedness of clusters, are employed as optimization targets. Besides, adaptive ranks clone, variable length chromosome crossover operation and k-nearest neighboring list based diversity holding strategies are featured by the algorithm. IMCA could automatically discover the right number of clusters with large probability. Seven complicated artificial data sets and two widely used synthetic aperture radar (SAR) imageries are used for test IMCA. Compared with FCM and VGA, IMCA has obtained good and encouraging clustering results. We believe that IMCA is an effective algorithm for solving these nine problems, which should deserve further research.

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

    NARCIS (Netherlands)

    Ashraf, H.; Hoop, B. 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

  19. Algorithm Research of Fingerprint Image Segmentation%指纹图像分割算法的研究

    Institute of Scientific and Technical Information of China (English)

    张伟

    2011-01-01

    Fingerprint image segmentation as an important pan of fingerprint recognition, is the right basis of fingerprint recognition, and only select the appropriate fingerprint segmentation, effective separation of the fingerprint image from the foreground and background in order to follow-up treatment, can realize the effective fingerprint recognition; otherwise will increase the number of false features, affect the final match of the fingerprint can not be valid identification. In this paper, fingerprint image segmentation, the analysis of variance and consistency of orientation fingerprint problem, design a smooth contour with the fingerprint segmentation algorithm, simulation results show that the algorithm can accurately extract the background from the fingerprint image, and can be removed irregular interruption of the edge endpoints, to ensure effective fingerprint areas are intact%指纹图像分割作为指纹识别的重要部分,是正确进行指纹识别的的基础,只有选择合适的指纹分割方法,有效地分离出指纹图像的前景和背景两部分,才能进行后续的处理,才能对指纹进行有效的识别;否则由于背景区域对前景区域影响会增加许多虚假特征,影响最终匹配,不能有效地对指纹进行识别;在指纹图像分割方面,分析了指纹的方差和方向一致性问题,设计了一种具有光滑轮廓线的指纹分割算法,仿真结果表明,该算法能够从指纹背景中准确提取指纹图像,并且能够除去边缘非正常中断端点,保证有效指纹区得到完整保留.

  20. The backtracking search optimization algorithm for frequency band and time segment selection in motor imagery-based brain-computer interfaces.

    Science.gov (United States)

    Wei, Zhonghai; Wei, Qingguo

    2016-09-01

    Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain-computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem. A novel evolutionary algorithm, the backtracking search optimization algorithm is used to find the optimal frequency band and the optimal combination of frequency band and time segment. The former is searched by a frequency window with changing width of which starting and ending points are selected by the backtracking optimization algorithm; the latter is searched by the same frequency window and an additional time window with fixed width. The three parameters, the starting and ending points of frequency window and the starting point of time window, are jointly optimized by the backtracking search optimization algorithm. Based on the chosen frequency band and fixed or chosen time segment, the same feature extraction is conducted by CSP and subsequent classification is carried out by Fisher discriminant analysis. The classification error rate is used as the objective function of the backtracking search optimization algorithm. The two methods, named BSA-F CSP and BSA-FT CSP, were evaluated on data set of BCI competition and compared with traditional wideband (8-30[Formula: see text]Hz) CSP. The classification results showed that backtracking search optimization algorithm can find much effective frequency band for EEG preprocessing compared to traditional broadband, substantially enhancing CSP performance in terms of classification accuracy. On the other hand, the backtracking search optimization algorithm for joint selection of frequency band and time segment can find their optimal combination, and thus can further improve classification rates.

  1. Multi-detector row computed tomography of the heart: does a multi-segment reconstruction algorithm improve left ventricular volume measurements?

    Energy Technology Data Exchange (ETDEWEB)

    Juergens, Kai Uwe; Maintz, David; Heimes, Britta; Fallenberg, Eva Maria; Heindel, Walter; Fischbach, Roman [University of Muenster, Department of Clinical Radiology, Muenster (Germany); Grude, Matthias [University of Muenster, Department of Cardiology and Angiology, Muenster (Germany); Boese, Jan M. [Siemens Medical Solutions, Forchheim (Germany)

    2005-01-01

    A multi-segment cardiac image reconstruction algorithm in multi-detector row computed tomography (MDCT) was evaluated regarding temporal resolution and determination of left ventricular (LV) volumes and global LV function. MDCT and cine magnetic resonance (CMR) imaging were performed in 12 patients with known or suspected coronary artery disease. Patients gave informed written consent for the MDCT and the CMR exam. MDCT data were reconstructed using the standard adaptive cardiac volume (ACV) algorithm as well as a multi-segment algorithm utilizing data from three, five and seven rotations. LV end-diastolic (LV-EDV) and end-systolic volumes and ejection fraction (LV-EF) were determined from short-axis image reformations and compared to CMR data. Mean temporal resolution achieved was 192{+-}24 ms using the ACV algorithm and improved significantly utilizing the three, five and seven data segments to 139{+-}12, 113{+-}13 and 96{+-}11 ms (P<0.001 for each). Mean LV-EDV was without significant differences using the ACV algorithm, the multi-segment approach and CMR imaging. Despite improved temporal resolution with multi-segment image reconstruction, end-systolic volumes were less accurately measured (mean differences 3.9{+-}11.8 ml to 8.1{+-}13.9 ml), resulting in a consistent underestimation of LV-EF by 2.3-5.4% in comparison to CMR imaging (Bland-Altman analysis). Multi-segment image reconstruction improves temporal resolution compared to the standard ACV algorithm, but this does not result in a benefit for determination of LV volume and function. (orig.)

  2. Anatomy of the Brain

    Science.gov (United States)

    ... Menu Brain Tumor Information Brain Anatomy Brain Structure Neuron Anatomy Brain Tumor Symptoms Diagnosis Types of Tumors Risk Factors ... form Brain Tumor Information Brain Anatomy Brain Structure Neuron Anatomy Brain Tumor Symptoms Diagnosis Types of Tumors Risk Factors ...

  3. Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

    Energy Technology Data Exchange (ETDEWEB)

    Weekley, R. Andrew; Goodrich, R. Kent; Cornman, Larry B.

    2016-04-01

    An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

  4. 改进的快速Otsu自适应分割算法及其应用%Improved fast adaptive Otsu segmentation algorithm & its application

    Institute of Scientific and Technical Information of China (English)

    陈滨; 田启川

    2012-01-01

    This paper introduced an improved Otsu algorithm for getting a good segmentation effect even if the histogram' s peak was not obvious for image segmenting. The algorithm took advantage of the prior knowledge about target and background to decrease classification difficult by clustering known background gray value, then achieved a satisfied threshold automatically through iterating procedure based on Otsu method. Finally, the target could be extracted by comparing every pixel' s gray value with the achieved threshold, and used it in iris image segmentation. From the segmentation results in eye image database, the algorithm' s performance on real-time and robustness is satisfied. The algorithm can be used in the real-time system of iris image segmentation.%对Otsu算法因灰度直方图峰值不明显导致分割效果差提出了改进,根据分割目标背景信息的先验值对类内灰度值进行调整,通过迭代计算,使类间方差最大化,从而自动确定阈值,并应用于虹膜图像分割.实验结果表明,该算法对虹膜图像分割效果好,运算速度快,具有一定的鲁棒性和自适应性,可用于虹膜图像的实时分割.

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

    Science.gov (United States)

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

    2011-01-01

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

  6. An infared polarization image fusion method based on NSCT and fuzzy C-means clustering segmentation algorithms

    Science.gov (United States)

    Yu, Xuelian; Chen, Qian; Gu, Guohua; Qian, Weixian; Xu, Mengxi

    2014-11-01

    The integration between polarization and intensity images possessing complementary and discriminative information has emerged as a new and important research area. On the basis of the consideration that the resulting image has different clarity and layering requirement for the target and background, we propose a novel fusion method based on non-subsampled Contourlet transform (NSCT) and fuzzy C-means (FCM) segmentation for IR polarization and light intensity images. First, the polarization characteristic image is derived from fusion of the degree of polarization (DOP) and the angle of polarization (AOP) images using local standard variation and abrupt change degree (ACD) combined criteria. Then, the polarization characteristic image is segmented with FCM algorithm. Meanwhile, the two source images are respectively decomposed by NSCT. The regional energy-weighted and similarity measure are adopted to combine the low-frequency sub-band coefficients of the object. The high-frequency sub-band coefficients of the object boundaries are integrated through the maximum selection rule. In addition, the high-frequency sub-band coefficients of internal objects are integrated by utilizing local variation, matching measure and region feature weighting. The weighted average and maximum rules are employed independently in fusing the low-frequency and high-frequency components of the background. Finally, an inverse NSCT operation is accomplished and the final fused image is obtained. The experimental results illustrate that the proposed IR polarization image fusion algorithm can yield an improved performance in terms of the contrast between artificial target and cluttered background and a more detailed representation of the depicted scene.

  7. 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.

  8. Fruit image segmentation based on evolutionary algorithm%基于演化算法的水果图像分割

    Institute of Scientific and Technical Information of China (English)

    彭红星; 邹湘军; 陈琰; 杨磊; 熊俊涛; 陈燕

    2014-01-01

    An improved evolutionary algorithm based on queen mating combined with elite and truncated choices by stages was proposed for fruit image segmentation, which was appropriate for the demand of the picking robot for real-time image and adaptive processing algorithms. The 8 bit binary code was used to correspond with the gray value of the fruit image in the improved evolutionary algorithm. The number of the initial population was set to 12 in the phase of the population initialized and the corresponding individual values, which ranged between 0 and 255, were generated by the random function. The twelve random numbers were the initial values of the evolutionary algorithm. Then an improved Otsu algorithm formula was selected as the fitness function. In the selection phase, the iterative process was divided into before stage, middle stage, and after stage, which were respectively used by queen mating algorithm, elitist choices strategy, and truncated choices strategy to select the fitness value. In the first stage, the individuals were produced by a random function and then the best individual (queen) of the evolutionary algorithm was hybridized with the rest of the individuals (including the randomly generated individuals) to generate new individuals. Finally, the individuals with the smallest fitness values were replaced by the new individuals. In the second stage, the elitist choices strategy was used and the individual with the smallest fitness value in the current generation was replaced by the individual with the highest fitness value in the previous generation. In the third stage, the truncated choices strategy was used and the last half of the individuals with the smallest fitness value in the current generation was replaced by the same number of individuals with the highest fitness value in the previous generation. This not only ensures the diversity of the population, but also overcomes the disadvantage of local optimized and too fast a convergence of the

  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. 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....

  11. The Application of K-means Clustering Algorithm in Image segmentation%K-均值聚类算法在图像分割中的应用

    Institute of Scientific and Technical Information of China (English)

    郭秀娟

    2015-01-01

    通过研究图像分割方法的现状和存在的问题,结合聚类算法在图像分割中应用的特点,对聚类算法及其应用进行了研究.本文利用聚类分析中的K-均值算法在RGB颜色空间下实现图像分割;将图像分割系统在实际图像中进行验证,取得了较好效果.%This paper analyzes the present situation of research on image segmentation and major problems to be faced. Based on the characteristics of clustering algorithm for image segmentation, the passage focuses on the cluste-ring algorithm and its application on image segmentation. The realization of image segmentation in RGB colour space using K-means algorithm in clustering analysis;image segmentation system is validated in real images, and has a-chieved good results.

  12. Development and evaluation of an algorithm for the computer-assisted segmentation of the human hypothalamus on 7-Tesla magnetic resonance images.

    Science.gov (United States)

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

    2013-01-01

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

  13. Development and evaluation of an algorithm for the computer-assisted segmentation of the human hypothalamus on 7-Tesla magnetic resonance images.

    Directory of Open Access Journals (Sweden)

    Stephanie Schindler

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

  14. Otsu image threshold segmentation method based on new genetic algorithm%基于新遗传算法的 Otsu图像阈值分割方法

    Institute of Scientific and Technical Information of China (English)

    王宏文; 梁彦彦; 王志华

    2014-01-01

    Maximum between-class variance ( Otsu ) image segmentation method is a common image threshold segmentation method based on statistical theory , but Otsu image segmentation method has some disadvantages , such as more time-consuming , low segmentation accuracy and false image segmentation .Combining the principles of monkey king genetic algorithms, with Otsu algorithm, image gray, just as optimal threshold, was found.The results show that combined method not only improves the quality of image segmentation but also reduce the computation time .It is very suitable for real-time image processing .%最大类间方差( Otsu)图像分割法是常用的一种基于统计原理的图像阈值分割方法。为了改善Otsu耗时较多、分割的精度低、易产生图像误分割等不足,将猴王遗传算法与Otsu算法结合,运用猴王遗传算法的原理,寻找图像灰度的最大类间方差,即最佳阈值。结果表明,结合后的方法不仅提高了图像的分割质量、缩短了运算时间,而且非常适合图像的实时处理。

  15. 基于空间相关性的图像分割算法研究%Image segmentation algorithm based on spatial correlation

    Institute of Scientific and Technical Information of China (English)

    李鹏; 李玲; 李敏

    2013-01-01

    提出一种充分利用图像的空间相关性来达到高效快速地进行图像分割的新方法.利用均值漂移算法对图像进行分割形成过度分割的区域,并使这些区域保持理想的边缘和空间相关部分,用图结构表示的区域相邻图来代替分割的区域.和K-均值算法的思想一样,迭代循环置信传播算法以其具有收敛速度快的特点被用于最小化开销函数、整合过度分割的区域和获得最终的分割结果.基于分割区域而不是图像像素的图像聚类分割方法可降低噪声敏感性,同时提高图像分割质量.与FCM和MRF算法相比较,该算法在复杂场景图像中显示了更好的分割性能.%This paper presented a full use of spatial image correlation to achieve efficient fast image segmentation method. First of all, it used mean shift image segmentation algorithm to formate an excessive segmentation, so that it made these areas to maintain the desired edge and spatial correlation part. Then, it used the graph structure of the region adjacency graph instead of segmentation. Like K-means algorithm, iterative belief propagation algorithm had the advantages of fast convergence was used to minimize the cost function, integrate over segmentation and obtain the final segmentation result. Based on the segmentation of the region rather than the image pixel, image clustering segmentation method could reduce the noise sensitivity, while improving the quality of image segmentation. Comparing with FCM and MRF algorithm, the new algorithm in entropy evaluation standard especially complex scene images shows a better performance.

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

    Directory of Open Access Journals (Sweden)

    Jane Tufvesson

    2015-01-01

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

  17. Maximum Entropy Threshold Segmentation Algorithm Based on 2D-WLDH%基于2D-WLDH的最大熵阈值分割算法

    Institute of Scientific and Technical Information of China (English)

    邹小林

    2012-01-01

    The traditional 2D maximum entropy threshold segmentation algorithm has an inadequately reasonable assumption that the sum of probabilities of main-diagonal distinct is approximately one in the 2D histogram and the algorithm is time-consuming. Aiming at this problem, a new maximum entropy segmentation algorithm is proposed in this paper. Based on gray level and Weber Local Descriptors(WLD), it constructs a 2D WLD Histogram(2D-WLDH), and applies it to the maximum entropy threshold segmentation. In order to further improve the speed of the proposed algorithm, the fast recursive algorithm is deduced. Experimental results show that, compared with existing corresponding algorithms, the proposed algorithm can reduce the running time and achieve better segmentation quality.%在传统二维最大熵图像阈值分割算法中,二维直方图主对角区域的概率和近似为1的假设不够合理,且算法耗时较多.为此,提出一种新的最大熵分割算法.根据灰度级和韦伯局部描述子(WLD)建立二维WLD直方图(2D-WLDH),将其用于最大熵的阈值分割,并设计快速递推算法,以提高运行速度.实验结果表明,该算法的运行时间较少,分割效果较好.

  18. An Algorithm of Multiscale Segmentation for Documen Image%一种文档图像多尺度分割算法

    Institute of Scientific and Technical Information of China (English)

    朱庆生; 舒润震; 朱征宇

    2003-01-01

    Segmenting a document image into text and picture areas is very important for compressing efficiently document images. This paper introduces an algorithm of multiscale image segmentation for document image compression,which uses of wavelet-domain hidden Markov tree model in order to directly calculate the parameter of model based on original image to be segmented,and to obtain multiscale classification and segmentation of image. The idea of the method is to combine several new technologies such as multilevel wavelet transform,multiscale decision,across-scale dependencies and joint probability density function. The paper describes in detail the concept of the dyadic block,the correspondency between wavelets and dyadic blocks based on quad-tree,the hidden Markov model and multiscale likelihood computation.

  19. 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.…

  20. 几种常用CT图像分割算法分析和探讨%Research and Analysis of Several CT Image Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    毛慧华; 王枫红; 陈炽坤; 赖泽鑫

    2012-01-01

    Image segmentation is an old and difficult problem in digital image processing. The segmentation quality is directly connected to the image processing follow-up. Therefore in the theoretical study and practical application it attracts researchers' extensive attention. In this paper, the current study uses several methods of segmentation, including thresholding segmentation, LOG algorithms segmentation, FCM clustering segmentation, and watershed segmentation. CT images are combined with image segmentation. This paper describes a variety of concepts and principles of segmentation. And the segmentation results are analyzed and discussed. Then it obtains the strengths and weaknesses of various methods and the direction of the main application. Follow-up study of these experimental results will provide a better scientific basis.%图像分割是数字图像处理的一个经典难题.其分割质量的好坏直接影响到图像处理的后续工作.因而在理论研究和实际应用中受到研究人员的广泛重视.本文在总结了过去的分割方法基础上,就目前常用几种分割方法进行实验研究.主要包括阈值分割、LOG算子分割、FCM聚类分割、分水岭分割,并结合CT图像进行图像分割.文章阐述了各种分割方法的概念及原理,并在此基础上对分割结果进行了分析和探讨,得出各种方法的优点和不足.这些实验结论将为后续研究提供科学依据.

  1. 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.

  2. MEDICAL IMAGE SEGMENTATION FOR ANATOMICAL KNOWLEDGE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Ms Maya Eapen

    2014-01-01

    Full Text Available Computed Tomography-Angiography (CTA images of the abdomen, followed by precise segmentation and subsequent computation of shape based features of liver play an important role in hepatic surgery, patient/donor diagnosis during liver transplantation and at various treatment stages. Nevertheless, the issues like intensity similarity and Partial Volume Effect (PVE between the neighboring organs; left the task of liver segmentation critical. The accurate segmentation of liver helps the surgeons to perfectly classify the patients based on their liver anatomy which in turn helps them in the treatment decision phase. In this study, we propose an effective Advanced Region Growing (ARG algorithm for segmentation of liver from CTA images. The performance of the proposed technique was tested with several CTA images acquired across a wide range of patients. The proposed ARG algorithm identifies the liver regions on the images based on the statistical features (intensity distribution and orientation value. The proposed technique addressed the aforementioned issues and been evaluated both quantitatively and qualitatively. For quantitative analysis proposed method was compared with manual segmentation (gold standard. The method was also compared with standard region growing.

  3. Fingerprint Image Segmentation Algorithm Based on the Direction of the Field of Information and Gray Feature Fingerprint Segmentation%基于方向场信息和灰度特征的指纹分割算法研究

    Institute of Scientific and Technical Information of China (English)

    陈婧; 张苏

    2016-01-01

    Fingerprint image segmentation is a key step in the pre-processing, with the purpose of facilitating the effective extraction of fingerprint image feature. According to the basic principles of the common fingerprint segmentation process, this paper summarizes two common segmentation algorithms:methods of information-based approach and the statistical properties of the base direction. On this basis, this paper proposed segmentation algorithm based on orientation field information and gray feature, the results showed that:this method can efficiently and reliably segment fingerprint image, and the segmentation effect can meet the fingerprint image pre-processing purposes.%指纹图像分割是指纹图像预处理过程中的关键步骤,目的是便于指纹图像特征点的有效提取。根据常见的指纹分割处理的基本原理,归纳总结了两种常用的分割算法:基于统计特性的方法和基于方向信息的方法。在此基础上,提出基于方向场信息和灰度特征的分割算法,结果表明:此法可以有效、可靠地进行指纹图像分割,分割效果达到指纹图像预处理的目的。

  4. 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.

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

    Science.gov (United States)

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

    2016-09-01

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

  6. Maximum Entropy Method of Image Segmentation Based on Genetic Algorithm%改进的最大熵算法在图像分割中的应用

    Institute of Scientific and Technical Information of China (English)

    王文渊; 王芳梅

    2011-01-01

    The traditional entropy threshold has shortcomings of theory and computational complexity, resulting in time - consuming in image segmentation and low efficiency. In order to improve the efficiency and accuracy of image segmentation, an image segmentation method is proposed, which combines the improved genetic algorithm with maxi-mum entropy algorithm. First, the two -dimensional histogram based on the image gray value information is used to extract features, then three genetic operations of selecting, crossover and mutation are used to search for the optimal threshold for image segmentation. Simulation results show that the improved algorithm, compared with the traditional maximum entropy image segmentation algorithm, increases segmentation efficiency, and the accuracy of image seg-mentation has greatly improved, which speeds up the segmentation speed.%研究图像分割优化问题,要求图像分割速度快,清晰度高.针对传统的熵值法在理论上存在的不足,同时抗噪能力差,速度慢,图像模糊等缺陷,造成图像分割过程耗时长,分割效率低等问题.为了提高图像分割效率和精确度,提出一种改进的遗传算法和最大熵算法相结合的图像分割新方法.首先依据图像二维直方图信息来对图像进行特征提取,最后通过遗传算法的选择、交叉和变异操作搜索最优阈值,从而获得最优阈值来对图像进行分割.仿真结果表明,改进的算法与传统最大熵值的图像分割算法相比,分割效率明显提高,同时图像分割的精度也大大提高,加快了图像分割的速度,为设计提供了依据.

  7. 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

  8. 基于区域边界最优映射的图像分割算法%Image segmentation algorithm based on optimal region boundary map

    Institute of Scientific and Technical Information of China (English)

    赵婕; 张春美; 张小勇; 姚峰林

    2016-01-01

    In order to enhance the robustness of image segmentation approaches and avoid producing false or disconnected contours,this paper put forward the optimal region boundary map (ORBM)segmentation algorithm to attain accurate segmenta-tion lines.The algorithm defined the region segmentation model expressed by Gibbs distribution.It averaged the several edge maps based on Canny and K-means to obtain the optimal boundary map for six different color spaces,and computed the interac-tion potentials of adjacent pixels from the optimal boundary map.It solved the optimization of the objective function over the la-bel space by using α-βswap algorithm.Finally,achieved the reliable and accurate segmentation results through merging the simple region.It compared ORBMalgorithm with several state-of-art image segmentation algorithms.The experiments show that the proposed algorithm is efficient to image region segmentation by producing closed continuous boundary,and has excellent performance and robustness.%为了增强图像分割算法的鲁棒性,避免出现错误或间断的边缘轮廓曲线,获得准确的区域分割线,提出区域边界最优映射分割(ORBM)算法。该算法采用 Gibbs 分布定义区域分割模型,将多个颜色空间的不同边缘映射求平均值,用得到的边界最优映射确定邻域(相邻像素)的相互作用势函数,利用α-β交换算法求解标签参数空间上目标函数的局部极值并采取简单区域合并策略,获得准确、可靠的区域分割结果。将 ORBM算法与几种经典的图像分割算法进行对比,实验结果显示该算法能够生成连续封闭的边界线,实现了图像多区域的正确分割,并且执行速度快、鲁棒性强。

  9. Microsurgical anatomy related to craniocervical junction segment of the vertebral artery in far lateral approach%寰枢段椎动脉在远外侧入路中的应用显微解剖研究

    Institute of Scientific and Technical Information of China (English)

    贾旺; 毕智勇; 鲁润春; 于春江

    2013-01-01

    Objective Microsurgical anatomy of craniocervical junction (CCJ) segment of the vertebral artery (VA) were studied to provide an applied anatomic basis for the far lateral approach.Methods Simulated operation of far lateral approach was performed on 10 cadaveric heads specimens and 10 dry skulls for measurment of the osseous relationships in the region.Results Craniocervical junction segment of the vertebral artery has five curvatures in most of the specimens,and compensatory vascular expansion in the curvatures was found.The average diameter is (4.3 ± 0.5) mm with changeful direction.The average half length of posterior arch of atlas is (19.3 ±4.7) mm,also the safe extent for exposing vertebral artery.Conclusions The key points to successfully preserve vertebral artery in far lateral approach are familiarity with the microanatomical relationship of craniocervical junction segment of the vertebral artery,especially the five curvatures.%目的 为颅颈交界区手术入路提供解剖学参数,帮助神经外科医生安全、准确地暴露手术靶区.方法 应用10%甲醛固定的汉族成人尸头标本10例20侧;漂白干颅骨及寰枢椎10例20侧.模拟手术入路逐层解剖,并对解剖结构进行精确测量和拍照.结果 寰枢段椎动脉在颅颈交界区形成比较恒定的五个生理弯曲,平均直径(4.3±0.5) mm,角度多变.寰椎后弓外侧半距(19.3±4.7)mm.结论 熟悉寰枢段椎动脉五个生理弯曲的定位方法,有助于提高颅颈交界区手术入路的安全性.

  10. Effect of different segmentation algorithms on metabolic tumor volume measured on 18F-FDG PET/CT of cervical primary squamous cell carcinoma

    Science.gov (United States)

    Xu, Weina; Yu, Shupeng; Ma, Ying; Liu, Changping

    2017-01-01

    Background and purpose It is known that fluorine-18 fluorodeoxyglucose PET/computed tomography (CT) segmentation algorithms have an impact on the metabolic tumor volume (MTV). This leads to some uncertainties in PET/CT guidance of tumor radiotherapy. The aim of this study was to investigate the effect of segmentation algorithms on the PET/CT-based MTV and their correlations with the gross tumor volumes (GTVs) of cervical primary squamous cell carcinoma. Materials and methods Fifty-five patients with International Federation of Gynecology and Obstetrics stage Ia∼IIb and histologically proven cervical squamous cell carcinoma were enrolled. A fluorine-18 fluorodeoxyglucose PET/CT scan was performed before definitive surgery. GTV was measured on surgical specimens. MTVs were estimated on PET/CT scans using different segmentation algorithms, including a fixed percentage of the maximum standardized uptake value (20∼60% SUVmax) threshold and iterative adaptive algorithm. We divided all patients into four different groups according to the SUVmax within target volume. The comparisons of absolute values and percentage differences between MTVs by segmentation and GTV were performed in different SUVmax subgroups. The optimal threshold percentage was determined from MTV20%∼MTV60%, and was correlated with SUVmax. The correlation of MTViterative adaptive with GTV was also investigated. Results MTV50% and MTV60% were similar to GTV in the SUVmax up to 5 (P>0.05). MTV30%∼MTV60% were similar to GTV (P>0.05) in the 50.05) in the 100.05) in the SUVmax of at least 15 group. MTViterative adaptive was similar to GTV in both total and different SUVmax groups (P>0.05). Significant differences were observed among the fixed percentage method and the optimal threshold percentage was inversely correlated with SUVmax. The iterative adaptive segmentation algorithm led to the highest accuracy (6.66±50.83%). A significantly positive correlation was also observed between MTViterative

  11. 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 ...

  12. Normal Pancreas Anatomy

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

  13. 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 ...

  14. Ranking algorithms based on segmentation and purification for search engine%基于分块和净化的搜索引擎排序算法

    Institute of Scientific and Technical Information of China (English)

    姜楚江; 余轶军

    2012-01-01

    The intelligent ranking algorithm is researched. The definition and computation are presented tor Web page quality and extended Web page quality. A new intelligent ranking algorithm is proposed and improved by Web page segmentation algorithm VIPS. The results are purified and improved. The experiment shows the recommendation accuracy is improved by new algorithm.%展开了对互联网搜索引擎结果集的智能排序研究,提出了一种基于扩展网页质量和VIPS分块算法的智能网页排序算法,并对结果进行净化处理以及查询优化.实验结果表明研究提出的新算法提高了推荐精度.

  15. Objective Performance Evaluation of Video Segmentation Algorithms with Ground-Truth%一种客观的视频对象分割算法性能评价方法

    Institute of Scientific and Technical Information of China (English)

    杨高波; 张兆扬

    2004-01-01

    While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance.In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation.Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy.Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames.The experimental results show the feasibility of our approach.Moreover, it is computationally more efficient than previous methods.It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.

  16. 电偶极子切分算法研究%Research on electric dipole segmentation algorithm

    Institute of Scientific and Technical Information of China (English)

    胡瑞华; 林君; 孙彩堂; 刘长胜; 周逢道

    2014-01-01

    电偶源是研究电磁法探测原理中常被使用的一种主动源。在实际工作中,常采用供电导线向大地发射电流,当发射端与接收点距离远大于供电导线两极长度时,才可将供电导线看作是电偶极子。但在很多情况下并不满足此条件,使基于电偶极子的场计算公式不再适用,因此正反演中需要先将长导线源切分成若干个电偶极子,然后对各偶极子的场响应进行叠加。对此,这里研究并实现了将长导线源切分成多个电偶极子的算法,以二分法为原理的递归切分算法切分出的电偶极子数目相对较多;以穷举法为原理的切分算法在穷举步长较小(如0.1 m)时能较准确地切分出电偶极子的位置。两种算法都可应用于长导线源正反演场的计算。%Electric dipole is a positive electrical current source which is usually used to research principles on electromagnetic prospecting method.We often introduce the power supply wire to transmit current towards the ground in actual jobs.When the distance between the transmitter and the receiver is far larger than the length of the power supply wire,which can be regarded as electric dipole.Howere,the condition of the power supply wire being electric dipole is not be satisfied in many case.This leads to formulas of calculating fields based on the electric dipole not be applicable.In this case,long wire source needs to be cut some electric dipoles,then to add every dipole's response.To solve the problem,the paper studied and realized algorithms to segment long wire source into some electric dipoles.The more electric dipoles can be got when to use the recursion algorithm based on the principle of binary search,the more accuracy positions of electric dipoles can be found when to use the algorithm based on exhaustion method if a smaller step chosen (for instance 0.1 meters).Both of the two algorithms can be applied for the calculating fields of long

  17. Color Image Segmentation Algorithm Based on Spherical Granular Computing%彩色图像的球形粒计算分割算法

    Institute of Scientific and Technical Information of China (English)

    李为华; 刘宏兵

    2014-01-01

    Considering the low segmentation speed of color image segmentation based on clustering method, a color image segmentation algorithm was proposed based on spherical granular computing. For a color image, each pixel point was represented as a sphere with the center RGB and radii 0, the union operator between two spherical granules was designed, the granularity threshold was used to unite two sphere granules and obtain the sphere granules with different granularities. The RGB of pixels belonging to the sphere granule was replaced by that of the sphere granule’ s center. The experimental results showed that the spherical granular computing segmentation algorithm was stable and speeded up 6 times and 34 times compared with K-means and FCM segmentation algorithms,respectively.%针对基于聚类的彩色图像分割算法速度较慢,提出了彩色图像的球形粒计算分割算法。将彩色图像每个像素点表示为以该点RGB像素值为中心0为半径的球形粒,设计球形粒之间的合并算子,利用粒度阈值对两球形粒进行有条件合并,得到不同粒度的球形粒组成的球形粒集,以球形粒中心对应的RGB值代替球形粒包含像素点的RGB值。实验结果表明:与K-means算法和FCM算法相比,球形粒计算分割算法是稳定的而且分别加快了6倍和34倍。

  18. PCNN image segmentation method based on bactrial foraging optimization algorithm%PCNN文本图像分割的细菌觅食优化算法

    Institute of Scientific and Technical Information of China (English)

    廖艳萍; 张鹏

    2015-01-01

    为解决脉冲耦合神经网络(pulse-coupled neural network,PCNN)模型参数人工凭经验和需要反复实验才能确定的难题,提出一种基于改进的PCNN模型.以最大类间方差函数作为细菌觅食算法的适应度函数,采用细菌觅食优化算法搜索最优参数的图像分割算法,避免了人工实验设定参数的盲目性.实验结果表明,该算法可以有效实现文本图像分割,并且分割效果明显优于对比算法.%To handle the difficult task of setting the relative parameters properly in the research and application of Pulse Coupled Neural Networks ( PCNN),an improved PCNN algorithm is proposed.It uses the maximum between-cluster variance function as the fitness function of bacterial foraging optimization algorithm,and adopts bacterial foraging optimization algorithm to search the optimal parameters,and eliminates the trouble of manually setting the experiment parameters.Experimental results show that the proposed algorithm can effectively complete document image segmentation,and result of the segmentation is obviously better than the contrast algorithms.

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Yachun Pang

    2012-01-01

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

  1. 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.

  2. Automatic segmentation of pulmonary segments from volumetric chest CT scans.

    NARCIS (Netherlands)

    Rikxoort, E.M. van; Hoop, B. de; Vorst, S. van de; Prokop, M.; Ginneken, B. van

    2009-01-01

    Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmenta

  3. Image Segmentations for Space Target Based-on SUSAN Algorithm%基于SUSAN算法的空间目标分割算法

    Institute of Scientific and Technical Information of China (English)

    淡雪; 岳晓奎

    2011-01-01

    With the rapid development of space technology, space background of the division of non-cooperative target has become the new focus of concern. SUSAN algorithm is a new class of parallel boundary segmentation algorithm using the USAN principle, covered by the template target pixel to extract the statistical features. Characteristics of the target image for the space, this paper presents a space-based object segmentation algorithm SUSAN algorithm. Target by the edges in the image information extraction, to achieve the artificial separation of target and background. The algorithm has good noise immunity, feature location accuracy, computing speed, better able to maintain the structural information of the characteristics of the image features, ideal for real-time image segmentation space.%随着航天科技的迅猛发展,空间背景下非合作目标的分割问题已经成为人们关注的新焦点.SUSAN算法是一种新兴的并行边界类分割算法,采用USAN原理,通过对模板覆盖像素的统计来提取目标的特征.针对空间目标图像的特点,提出了一种基于SUSAN算法的空间目标分割算法.利用图像中目标的边缘轮廓信息进行特征提取,实现了人造目标与背景的分离.该算法具有抗噪声能力好、特征定位准确、计算速度快、能够较好的保持图像的特征结构信息等特点,非常适用于航天图像的实时分割处理.

  4. Genetic fuzzy clustering algorithm for point cloud data segmentation%应用遗传模糊聚类实现点云数据区域分割

    Institute of Scientific and Technical Information of China (English)

    李海伦; 黎荣; 丁国富; 葛源坤

    2012-01-01

    为了准确地实现点云数据的区域分割,将基于遗传算法的模糊聚类算法应用于逆向工程中的点云数据区域分割中.首先估算出法矢量、高斯曲率和平均曲率,并与坐标一起组成八维特征向量,用加权距离代替欧氏距离,然后通过遗传算法获得全局最优解的近似解;最后将近似解作为模糊聚类的初始解进行迭代,实现点云数据的区域分割,从而避免传统FCM算法的局部性和对初始解的敏感性,减少了迭代次数.以汽车钣金件为例,证明了应用遗传模糊聚类实现点云数据区域分割的有效性,并验证了该方法能快速、准确地实现点云数据的区域分割.%In order to realize point cloud data segmentation accurately, this paper applied genetic fuzzy clustering algorithm to the point cloud data segmentation in reverse engineering. First, it estimated the normal vector, Gaussian curvature and mean curvature, together with the coordinates of the eight-dimensional feature vector component, using weight distance replaced the Euclidean distance. Through the genetic algorithm, it obtained the approximate solution of the global optimal solution. Finally it used the approximate solution as the initial solutions of fuzzy clustering iteration achieved the point cloud data region segmentation , therefore, avoided the locality and sensitiveness of the initial condition of fuzzy clustering algorithm, at the same time, it reduced the number of iterations. Taking car sheet metal for an example proves the validation of genetic fuzzy clustering algorithm applied to the point cloud data segmentation. And point cloud data can be segmented fast and accurately by this algorithm.

  5. 一种改进的多类 SVM 彩色图像分割算法%An Improved Multiclass SVM Segmentation Algorithm of Color Image

    Institute of Scientific and Technical Information of China (English)

    吕景美

    2013-01-01

    An algorithm of color image segmentation based on multi -class SVM (Support Vector Machine ) is proposed in the paper.The input vectors consist of texture features derived from GLCM (gray-level co-oc-currence matrices) and LAB color moments which synthesize the texture features and color features .The Princi-pal Component Analysis (PCA) algorithm is applied to reduce the dimensions to get the sample eigenvectors . The color image segmentation is worked out via improved multi -class SVM.The disadvantage of existing inva-lid borders yielded by SVM pixel segmentation is conquered with image preprocessing and median filtering , by which the quality of segmentation is improved .The influence of different color space and kernel function for segmentation is also analyzed .The experimental results show that the segmentation result via the algorithm in this paper is better than the one by the Fast Fuzzy C -Means (FFCM) clustering algorithm, and the image seg-mentation time is reduced.The segmentation result has an obvious object and the algorithm in this paper can be used to image segmentation for object recognition .%针对复杂自然场景下的目标分割和识别问题,提出了一种基于多类支持向量机(SVM)的彩色图像分割算法,该算法综合纹理特征和颜色特征,把灰度共生矩阵(GLCM)提取的纹理特征和LAB颜色空间的颜色矩组成输入向量,用主成份分析(PCA)算法降维,提取样本特征向量,再用改进后的多类SVM进行彩色图像分割。通过数据预处理并结合中值滤波算法,有效克服了SVM基于像素分割产生无效边缘的缺点,提高了彩色图像的分割质量,并分析了不同颜色空间和核函数对该分割算法的影响。实验结果表明,与快速模糊C均值聚类算法(FFCM)相比,图像分割的质量有明显改善,缩短了分割时间,分割图像中的目标明显,适用于以目标识别为目的的图像分割。

  6. 一种生物在体荧光成像的自适应分割算法%Adaptive Segmentation Algorithm of Bioluminescent Image

    Institute of Scientific and Technical Information of China (English)

    常志军; 杨鑫

    2011-01-01

    Bioluminescence imaging is regarded as an imaging modality with a high performance, low cost and good prospect in molecular imaging technique. This paper proposes a new adaptive segmentation algorithm, which is based on the characteristics and the application requirements of the bioluminescent images. The adaptive segmentation is realized by performing the normalized processing, connectivity operation and the interested regions distinguishing on bioluminescent images. Experimental results show that this algorithm can get better segmentation results in the ease of weak signal, low signal-to-noise ratio and multiple light sources, so it is a kind of effective segmentation algorithm of bioluminescent images.%生物在体荧光成像是新兴分子影像技术中性能高、费用低、前景好的一种成像模态.针对生物在体荧光图像的特点和应用需求,提出一种全新的自适应图像分割算法.通过对荧光图像的归一化处理、连通性操作、感兴趣区域区分实现自适应分割.实验结果表明,该算法能够在弱信号、低信噪比、多光源的情况下得到较理想的分割结果,是一种有效的荧光图像分割算法.

  7. Algorithm of human thermal image segmentation under complicated background%复杂背景下的人体热图像分割

    Institute of Scientific and Technical Information of China (English)

    谭建辉; 潘保昌

    2011-01-01

    The accurate segmentation of infrared body is a difficult problem under complicated background,especially in the environment that the gray values are very similar between the infrared human movement target and background when their temperatures vary slightly. Therefore, the background subtraction method based on Gaussian mixture model is improved. The fine segmentation is implemented by the modified Pulse Coupled Neural Network(PCNN) in its binary stage,and meanwhile the PCNN segmentation parameters are determined by using the multi-modal immune evolution algorithm(MIEA).The simulation results show that this algorithm is achieved fast automatic segmentation, and has gotten the ideal effect of image segmentation that its precision is high.%复杂背景下,特别是在环境与人体温度相差不大的情况下,红外运动人体目标与背景的灰度值会非常相似,准确的红外人体分割是一个难题.对基于混合高斯模型的背景减除法进行改进,在二值化阶段采用改进型的脉冲耦合神经网络(PCNN)进行精细分割,利用多模态免疫进化算法(MIEA)自动确定PCNN分割参数.仿真实验结果表明,该算法图像分割精度高,实现了快速自动分割,取得了较为理想的图像分割效果.

  8. 结合方差和方向的指纹图像分割算法%Fingerprint Image Segmentation Algorithm Based on Variance and Orientation

    Institute of Scientific and Technical Information of China (English)

    蔡秀梅; 张永健; 梁辉

    2011-01-01

    The task of fingerprint segmentation is to isolate the fingerprint area from the image hackground. The good fingerprint segmentation can not only improve the accuracy of the feature extraction but also reduce the time of subsequent processing effectively. A fingerprint image segmentation algorithm based on variance and orientation is proposed. The fingerprint image is partitioned into several blocks, and then the block variance and its point orientation map are computed. The fingerprint image is segmented with two kinds of threshold of block variance and its consistency of point orientation. The experimental results show that the proposed algorithm has the advantages of the fingerprint segmentation method based on the orientation map and gray variance, it is simple, fast and effective.%指纹图像分割指从图像背景中分离出有用的指纹区域的过程.良好的指纹分割能够有效提高特征提取精度和减少后续处理时间,在指纹图像预处理中具有重要作用.结合指纹图像灰度方差和方向信息,提出一种指纹图像分割算法.将指纹图像分块,计算每块灰度方差和点方向图,根据图像块方差和点方向一致性设定两种分割阈值实现基于方差和方向的指纹图像分割.实验结果表明该算法兼具方差法和方向法的优点,简单快速,是一种有效的指纹图像分割方法.

  9. 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.

  10. Infrared warship target detection based on SUSAN algorithm and segmentation algorithm using the mean of image rows and columns%基于SUSAN算法和行列均值分割的海面舰船检测

    Institute of Scientific and Technical Information of China (English)

    严发宝; 邹常文; 王璐; 乐静; 陈伟; 董海

    2011-01-01

    提出一种基于SUSAN检测和行列均值分割的复杂海天背景的红外舰船目标检测算法.首先建立一个简单的舰船轮廓模型,结合SUSAN算子检测得到感兴趣区域ROI,并且感兴趣区域的大小可以由轮廓模型进行一定的控制;然后对感兴趣区域运用改进的行列均值分割得到舰船目标.实验结果表明,提出的算法能正确分割出舰船目标.%In this paper,a method for detecting infrared warship target in the complex background of sea-sky based on SUSAN detection and segmentation algorithm using the mean of image rows and columns. First, a simple model of warship contour was built. And the ROI can be decided subsequently, combine with SUSAN detection. Besides, the model of warship contour can control the size of the ROI in a way. Then, an improved segmentation algorithm based on the mean of image rows and columns was proposed. After the image segmentation, ship target was extracted. The experimental results show that the proposed algorithm is successful and can extract the infrared warship target.

  11. Weighted MRF Algorithm for Automatic Unsupervised Image Segmentation%变权重MRF算法在图像自动无监督分割中的应用

    Institute of Scientific and Technical Information of China (English)

    刘雪娜; 侯宝明

    2012-01-01

    In order to achieve the automatic unsupervised image segmentation, an algorithm based on the adaptive classification and the weighted MRF is proposed. First, combined with the MDL criterion, the number of image classification under the framework of Markov random fields is computed adaptively. And then, the weighted MRF algorithm is used to expand the option range of the potential function, thus to eliminate the complex calculation of the potential function. Finally, by using ICM algorithm to optimize the model, the segmentation image under MAP criterion is obtained. In the Matlab, test results show that the proposed algorithm is effective, which can correctly calculate the number of classification and effectively reduce the segmentation error.%为了实现图像的自动无监督分割,本文提出类自适应变权重马尔可夫随机场分割算法.首先结合最小描述长度准则,自适应计算马尔可夫随机场框架下的图像分类数;然后引入变权重的马尔可夫随机场算法,扩大势函数的选择范围,消除势函数的复杂计算;最后用迭代条件模式进行优化,获得最大后验概率准则下的分割图像.在Matlab环境中的测试结果表明,该算法具有实效性,能正确计算分类数,同时有效减少了分割错误.

  12. A Universal Fingerprint Segmentation Algorithm Based on Linear Classifier%一种基于线性分类器的通用指纹分割方法

    Institute of Scientific and Technical Information of China (English)

    邓亮; 张新曼; 沈虹晖; 许学斌; 张海涛; 田中民; 韩九强

    2011-01-01

    针对不同传感器采集的指纹难以进行统一准确的分割的问题,提出主频率带能量比值,归一化的灰度均值及灰度对比度值三个更为通用的指纹描述特征,并利用这三个指纹特征,使用SVM方法训练一个线性分类器,对指纹进行有效分割.实验表明,该方法不仅实现了对指纹的准确分割,在FVC2002指纹数据库上平均分割误差约为2%,并且该分割方法具有很好的通用性,适用各种传感器采集的指纹.%Fingerprints are always captured from different sensors,and to segment these fingerprints accurately with a united algorithm is very difficult.A universal algorithm based on linear classifier is proposed to sovle the problem.This algorithm uses three new general features extracted from the fingerprint to train a linear classifier with SVM,and then segment the fingerprints with the classifier.These new general features include the dominant frequency band energy ratio,the normalized gray mean value and the gray contrast.The experiments in the fingerprint database FVC2002 show that,this algorithm can segment the fingerprints accurately,with the average error rate of only about 2%,and it is universal in the fingerprint database acquired from different sensors.

  13. The Improved Algorithm of Fingerprint Segmentation Based on Adaptive Threshold%改进的指纹自适应阈值分割算法

    Institute of Scientific and Technical Information of China (English)

    纪星波; 张海峰

    2015-01-01

    指纹分割算法在自动指纹识别算法领域中具有重要地位。通过对传统的指纹分割算法分析比较,针对存在的问题,在基于纹理特征的自适应指纹图像分割算法的基础上,采用分割前后特定参数的逼近方式,提出一种改进的自适应指纹图像分割算法,有效解决了在低质量指纹图像中前者算法阈值误差的偏大问题。实验结果表明,该算法的分割效果更好,对噪声的抵抗能力更强,且对不同类型的指纹图像有较高的适应性。%The algorithms of fingerprint segmentation have an important position in the field of automatic fingerprint recognition . The traditional algorithms of fingerprint image segmentation are analyzed and compared , and in order to improve the effect of segmentation , this paper puts forward an improved algorithm based on the textural feature of fingerprint image .The experimental results show that the proposed algorithm is more effective, and it also has stronger resistance to noise , lower error of threshold and higher adaptability to different types of fingerprint images .

  14. HTM-based Genetic Time Series Segmentation Algorithm%基于 HTM 的遗传时间序列分割算法

    Institute of Scientific and Technical Information of China (English)

    吴大华

    2014-01-01

    结合层级实时记忆( Hierarchical Temporal Memory,HTM)模型与基于模式集的遗传时间序列分割算法各自的优点,用基于HTM的适应值函数替换原基于模式集的适应值函数,提出基于HTM的遗传时间序列分割算法。该算法可实现时间序列的分割及其相应子序列的分类识别。同时,针对HTM对训练样本的要求,提出一种基于模式集的HTM训练样本生成算法。最后在股票序列上验证了这2种算法的有效性。%This paper proposes a time series segmentation approach by combining the advantages of hierarchical temporal memory ( HTM) model and the pattern-based genetic time series segmentation algorithm.The approach is a HTM-based genetic algorithm which replaces the pattern-based fitness value function with the HTM-based fitness value function.The approach can be applied to find segments from a time series and to identify the class of sub series.In addition, a pattern-based algorithm of HTM sample generation is proposed for generating sample set with HTM trait.Experimental results show that the two algorithms are effective on the time series of stocks.

  15. Pituitary Adenoma Segmentation

    CERN Document Server

    Egger, Jan; Kuhnt, Daniela; Freisleben, Bernd; Nimsky, Christopher

    2011-01-01

    Sellar tumors are approximately 10-15% among all intracranial neoplasms. The most common sellar lesion is the pituitary adenoma. Manual segmentation is a time-consuming process that can be shortened by using adequate algorithms. In this contribution, we present a segmentation method for pituitary adenoma. The method is based on an algorithm we developed recently in previous work where the novel segmentation scheme was successfully used for segmentation of glioblastoma multiforme and provided an average Dice Similarity Coefficient (DSC) of 77%. This scheme is used for automatic adenoma segmentation. In our experimental evaluation, neurosurgeons with strong experiences in the treatment of pituitary adenoma performed manual slice-by-slice segmentation of 10 magnetic resonance imaging (MRI) cases. Afterwards, the segmentations were compared with the segmentation results of the proposed method via the DSC. The average DSC for all data sets was 77.49% +/- 4.52%. Compared with a manual segmentation that took, on the...

  16. Left atrium segmentation for atrial fibrillation ablation

    Science.gov (United States)

    Karim, R.; Mohiaddin, R.; Rueckert, D.

    2008-03-01

    Segmentation of the left atrium is vital for pre-operative assessment of its anatomy in radio-frequency catheter ablation (RFCA) surgery. RFCA is commonly used for treating atrial fibrillation. In this paper we present an semi-automatic approach for segmenting the left atrium and the pulmonary veins from MR angiography (MRA) data sets. We also present an automatic approach for further subdividing the segmented atrium into the atrium body and the pulmonary veins. The segmentation algorithm is based on the notion that in MRA the atrium becomes connected to surrounding structures via partial volume affected voxels and narrow vessels, the atrium can be separated if these regions are characterized and identified. The blood pool, obtained by subtracting the pre- and post-contrast scans, is first segmented using a region-growing approach. The segmented blood pool is then subdivided into disjoint subdivisions based on its Euclidean distance transform. These subdivisions are then merged automatically starting from a seed point and stopping at points where the atrium leaks into a neighbouring structure. The resulting merged subdivisions produce the segmented atrium. Measuring the size of the pulmonary vein ostium is vital for selecting the optimal Lasso catheter diameter. We present a second technique for automatically identifying the atrium body from segmented left atrium images. The separating surface between the atrium body and the pulmonary veins gives the ostia locations and can play an important role in measuring their diameters. The technique relies on evolving interfaces modelled using level sets. Results have been presented on 20 patient MRA datasets.

  17. 结合图论的JSEG彩色图像分割算法%JSEG Color Image Segmentation Algorithm Combining Graph Theory

    Institute of Scientific and Technical Information of China (English)

    耿永政; 陈坚

    2014-01-01

    Joint Systems Engineering Group ( JSEG) is a classical method of image segmentation algorithm. It fully takes the local image information into account,so it can get more precise segmentation boundary. But the JSEG algorithm has the large computation and over-segmentation problems. For this reason,propose a segmentation algorithm combining JSEG and graph theory. Firstly,calculate J value on-ly on a small scale instead of the iterative process on multi-scale. Secondly,use the K-means clustering method on the J-map to get over-segmentation regions. Finally,use a point to replace a region,and then use the graph theory for region merging. Experimental results show that the new algorithm has the advantage of a high accuracy and low complexity.%静态图像压缩标准( JSEG)分割算法是一种经典的图像分割方法,它充分考虑到了图像的局部信息,可以获得比较精确的分割边界。但JSEG算法在分割过程中计算量相当大并且分割结果容易出现过分割现象。由此,文中提出一种结合图论的JSEG图像分割算法。首先去除JSEG算法中在多个尺度上反复计算J值的过程,改为仅在一个小尺度上进行计算。其次,在得到的J图上使用K-means方法进行聚类,分割得到过分割区域。最后,将分割后的小区域对应为图中的点,进而利用图理论的方法进行区域合并。实验结果表明新算法具有高精度和低复杂度的优势。

  18. Fast image segmentation algorithm based on noise benefit%基于噪音受益的快速图像分割算法

    Institute of Scientific and Technical Information of China (English)

    牛艺蓉; 王士同

    2016-01-01

    Image segmentation denotes a process by which a raw image is partitioned into nonoverlapping regions. When using the existing improved Gaussian mixture model in image segmentation, how to speed up its segmentation process is a significant research topic. Based on the latest noise-benefit EM algorithm, this paper speeds up the convergence speed of the existing improved Gaussian mixture model by adding artificial noise, which achieves the goal of speeding up image segmentation. Additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likeli-hood surface when adding noise to meet the noise-benefit EM theorem. Improved Gaussian mixture model is a special case of the expectation-maximization algorithm, therefore, noise-benefit EM theorem applies to improved gaussian mix-ture model. Experimental results indicate that the algorithm speeds up the convergence speed when it is used for image segmentation, and the time complexity is decreased significantly.%图像分割是指将一幅图像分解为若干互不交迭的区域的集合。当用已有的改进高斯混合模型于图像分割时,如何加快其分割过程是一个有研究意义的课题。基于最新的噪音受益EM算法,通过人工加噪来加快已有的改进高斯混合模型的收敛速度,从而达到加快图像分割的目的。当添加的噪声满足噪音受益EM定理时,加性噪声加快了EM算法收敛到局部最大值的平均收敛速度。改进的高斯混合模型是EM算法的特例,因此,噪音受益EM定理同样适用于改进的高斯混合模型。实验表明,提出的算法进行图像分割时,其收敛速度明显加快,时间复杂度明显变小。

  19. The Three-Dimensional Fast Segmentation Algorithm Based on Level Set Method%基于Level Set的交互式快速分割算法

    Institute of Scientific and Technical Information of China (English)

    孙海鹏; 余伟巍; 席平

    2011-01-01

    三维医学图像数据量大,并且受噪声、边界模糊等原因的影响,致使三维分割过程消耗时间较长,容易产生欠分割或过度分割.针对以上问题,提出一种基于LevelSet的三维快速分割算法,采用Fast Marching获取二维分割区域,优化轮廓边界,利用直线数值微分算法(Digital Differential Analyzer,DDA)提取轮廓像素;进一步引入扫描线种子填充思想,实现医学图像的三维快速分割.实验结果表明,上述算法能够快速准确地分割出感兴趣区域.%Because of the large volume of medical image data, the impact of noise, blurred boundaries and other reasons, the three-dimensional segmentation process is time-consuming, and easily produces less or over segmentation. To solve the above problems, this paper proposes a three-dimensional fast segmentation algorithm based on Level Set, using Level Set Fast Marching Method to obtain two-dimensional segmental region, optimizing the boundary contour, using the Digital Differential Analyzer method to extract contour pixels, finally introducing the idea of the Scan Line Seed-filling to achieve the three-dimensional fast segmentation. The actual clinical CT images of vertebral segmentation experiment result shows that this method can quickly and accurately separate out the interested area.

  20. 基于侧抑制网络的二维Otsu阈值分割算法%Two-dimensional Otsu Threshold Segmentation Algorithm Based on Lateral Inhibition Network

    Institute of Scientific and Technical Information of China (English)

    董悫; 王江晴; 孙阳光

    2015-01-01

    The traditional two-dimensional Otsu thresholding segmentation algorithms do not think about human vision characteristics and the result of segmentation can not match up to the visual perception of human eye. In order to solve this problem,an algorithm based on the two-dimensional Otsu algorithm and the lateral inhibition network is proposed. In this algorithm, the lateral inhibition network of human visual system that has the features of enhancing center and inhibiting surroundings is fully used. The lateral inhibition network is utilized to process the original picture and obtains the lateral inhibition picture. A two-dimensional histogram based on the gray information and lateral inhibition information of pixels is established. The maximum between-cluster variance is chosen as the criterion to select the optimal threshold. Experimental results show that this algorithm not only is well adapted to the contrast and illumination intensity, but also has the capacity for fitting the breaks compared with the traditional Otsu algorithm and two-dimensional Otsu algorithm. It improves the robustness to image noise and obtains more perfect segmentation results.%传统二维Otsu阈值分割算法未考虑人类视觉特性,分割结果不符合人眼视觉感受。为此,提出一种二维Otsu算法与侧抑制网络相结合的分割算法。该算法从基于人类视觉系统的侧抑制网络出发,利用侧抑制网络增强中心,抑制周围的特性,通过侧抑制网络处理原始图像,得到侧抑制图像,构建基于像素的灰度信息和侧抑制信息的二维直方图,并采用类间最大方差作为最佳阈值的选取准则。实验结果表明,与传统的Otsu算法和二维Otsu算法等相比,该算法具有较好的对比度、光照强度适应性和间断拟合能力,并能提高对图像噪声的鲁棒性,获得更理想的分割结果。

  1. Lettuce image target clustering segmentation based on MFICSC algorithm%基于MFICSC 算法的生菜图像目标聚类分割

    Institute of Scientific and Technical Information of China (English)

    孙俊; 武小红; 张晓东; 王艳; 高洪燕

    2012-01-01

    生菜图像目标分割是基于图像处理的生菜生理信息无损检测的前提.为了解决因生菜富含水分使得图像采集镜头反光而导致生菜叶片图像灰度分布不均的问题,该文采用一种修正的图像灰度均衡算法对生菜图像进行灰度均衡处理,应用混合模糊类间分离聚类算法(MFICSC)进行生菜图像目标分割,使总体类间距离最大化,能够同时生成模糊隶属度和典型值,对处理噪声数据和克服一致性聚类问题均表现良好.分别采用MFICSC算法和Otsu算法进行了生菜图像目标分割对比试验,结果表明MFICSC算法具有较好的聚类准确度,效果优于传统Otsu分割算法.%Lettuce image target segmentation is the premise of the nondestructive detection of lettuce physiological information based on image processing. Because lettuce contains more water, the camera len is likely to occur reflex, leading to uneven gray distribution of lettuce leaf image. A modified image equalization algorithm is used to equalize the image gray. In this paper, the mixed fuzzy inter-cluster separation clustering(MFICSC) is applied in lettuce image target segmentation, which can make the distance between classes be maximum on the whole and can produce the fuzzy memberships and possibilities simultaneously. MFICSC can overcome the noise sensitivity and the coincident clusters problem. In the test, the MFICSC algorithm and Otsu algorithm were applied to lettuce image target segmentation respectively. The test results show that the MFICSC algorithm has better clustering accuracy, and its segmentation effect is superior to the one of traditional Otsu algorithm.

  2. Quick Dissection of the Segmental Bronchi

    Science.gov (United States)

    Nakajima, Yuji

    2010-01-01

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

  3. 基于组织细胞的彩色图像分割算法研究%Color Image Segmentation Algorithm Research Based on Tissue Cells

    Institute of Scientific and Technical Information of China (English)

    曹凤; 霍春宝

    2015-01-01

    针对彩色图像滤波去噪的同时把图像的一些特征信息去除和图像分割边缘模糊的问题,提出了将小波变换与数学形态学相结合的图像分割算法,通过Matlab仿真结果分析,该方法在组织切片细胞的分割应用中有很明显的优势。%For color image filtering denoising and to remove some features of the image information and image segmentation edge fuzzy problem, combining wavelet transform and mathematical morphology of image segmentation algorithm, was proposed. By analyzing the simulation results, the method of the division of cells in a tissue section applications shows the effectiveness of itself.

  4. 手背静脉图像分割及细化算法研究%Research on the Segmentation and Thinning Algorithms of Hand Vein Image

    Institute of Scientific and Technical Information of China (English)

    蔡超峰; 苏丹; 闫艳霞; 姜利英

    2014-01-01

    手背静脉识别技术通常基于静脉纹路的细节特征点对个人身份进行验证。为了准确地提取出手背静脉纹路中的细节特征点,提取手背静脉图像中的有效区域并对其进行归一化、增强和去噪处理,分别采用局部最大类间方差法(OSTU)、阈值图像法和NiBlack法对图像进行分割,分别采用Hilditch算法、快速细化算法、Zhang&Suen算法和OPTA算法对分割后得到的二值图像进行细化以获取静脉纹路。实验结果表明,基于合理的参数,NiBlack 法和Hilditch算法分别取得较好的分割与细化处理结果。%Minutiae of hand vein skeleton are usually employed in personal identification recognition. To extract the minutiae of hand vein skeleton accurately, extracts and normalizes the effective area of hand vein image and enhances followed by noise reduction. Uses the OSTU method, Threshold Image method and NiBlack method to segment the image. Uses the Hilditch algorithm, Fast algorithm, Zhang&Suen al-gorithm and OPTA algorithm to obtain the hand vein skeleton. The results indicate that, given reasonable parameters, the NiBlack method and the Hilditch algorithm do well in the hand vein segmentation and thinning, respectively.

  5. Automated lung segmentation of low resolution CT scans of rats

    Science.gov (United States)

    Rizzo, Benjamin M.; Haworth, Steven T.; Clough, Anne V.

    2014-03-01

    Dual modality micro-CT and SPECT imaging can play an important role in preclinical studies designed to investigate mechanisms, progression, and therapies for acute lung injury in rats. SPECT imaging involves examining the uptake of radiopharmaceuticals within the lung, with the hypothesis that uptake is sensitive to the health or disease status of the lung tissue. Methods of quantifying lung uptake and comparison of right and left lung uptake generally begin with identifying and segmenting the lung region within the 3D reconstructed SPECT volume. However, identification of the lung boundaries and the fissure between the left and right lung is not always possible from the SPECT images directly since the radiopharmaceutical may be taken up by other surrounding tissues. Thus, our SPECT protocol begins with a fast CT scan, the lung boundaries are identified from the CT volume, and the CT region is coregistered with the SPECT volume to obtain the SPECT lung region. Segmenting rat lungs within the CT volume is particularly challenging due to the relatively low resolution of the images and the rat's unique anatomy. Thus, we have developed an automated segmentation algorithm for low resolution micro-CT scans that utilizes depth maps to detect fissures on the surface of the lung volume. The fissure's surface location is in turn used to interpolate the fissure throughout the lung volume. Results indicate that the segmentation method results in left and right lung regions consistent with rat lung anatomy.

  6. A COLOUR IMAGE AUTOMATIC SEGMENTATION ALGORITHM BASED ON VPCNN%一种基于矢量PCNN的彩色图像自动分割算法

    Institute of Scientific and Technical Information of China (English)

    刘勍; 马小姝; 张利军; 马义德; 董忠

    2011-01-01

    为了对彩色图像实施自动分割,在彩色图像RGB空间中,对传统PCNN模型进行了改进与推广,提出一种基于指数熵矢量脉冲耦合神经网络(VPCNN)彩色图像自动分割新算法.该方法在考虑VPCNN互联矢量神经元动态时空相似特性的同时,利用改进指数动态阈值矢量与神经元内部活动项矢量间的信息对比关系确定分割图像的目标和背景区域,结合最大指数熵判据来达到彩色图像的自动分割,并与最大香农熵准则VPCNN分割方法做了比较.实验结果表明:算法具有图像分割精度高、适应性强、能较好地保持彩色图像边缘和细节等信息的优点.%In order to execute colour image automatic segmentation, in the colour image RGB space, the traditional pulse couple neural networks (PCNN) is improved and popularised. A colour image automatic segmentation algorithm based on exponent entropy vector pulse coupling neural networks (VPCNN) is put forward. While taking into account VPCNN linking vector neurons' dynamic temporal-spatial similarities , the method takes advantage of improving the information comparison between exponent dynamic threshold vector and neuron interior activity item vectors to confirm how to segment image target and background area, combine maximum exponent entropy fact to realize colour image automatic segmentation and make comparison against maximum Shannon entropy principle VPCNN segmentation method. Experimental result shows that the algorithm described in the article is precise at image segmentation, strong at adaptivity, and complete at preserving colour image edges, details and so on.

  7. Algorithm for localized adaptive diffuse optical tomography and its application in bioluminescence tomography

    Science.gov (United States)

    Naser, Mohamed A.; Patterson, Michael S.; Wong, John W.

    2014-04-01

    A reconstruction algorithm for diffuse optical tomography based on diffusion theory and finite element method is described. The algorithm reconstructs the optical properties in a permissible domain or region-of-interest to reduce the number of unknowns. The algorithm can be used to reconstruct optical properties for a segmented object (where a CT-scan or MRI is available) or a non-segmented object. For the latter, an adaptive segmentation algorithm merges contiguous regions with similar optical properties thereby reducing the number of unknowns. In calculating the Jacobian matrix the algorithm uses an efficient direct method so the required time is comparable to that needed for a single forward calculation. The reconstructed optical properties using segmented, non-segmented, and adaptively segmented 3D mouse anatomy (MOBY) are used to perform bioluminescence tomography (BLT) for two simulated internal sources. The BLT results suggest that the accuracy of reconstruction of total source power obtained without the segmentation provided by an auxiliary imaging method such as x-ray CT is comparable to that obtained when using perfect segmentation.

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

    DEFF Research Database (Denmark)

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

    2017-01-01

    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...

  9. Anatomy of the Eye

    Science.gov (United States)

    ... Conditions Frequently Asked Questions Español Condiciones Chinese Conditions Anatomy of the Eye En Español Read in Chinese External (Extraocular) Anatomy Extraocular Muscles: There are six muscles that are ...

  10. 基于GrabCut改进的图像分割算法%Improved image segmentation algorithm based on GrabCut

    Institute of Scientific and Technical Information of China (English)

    周良芬; 何建农

    2013-01-01

    针对GrabCut算法对于局部噪声敏感、耗时且提取边缘不理想等缺点,提出一种基于GrabCut改进的图像分割新算法.采用多尺度分水岭对梯度图像平滑去噪;对新梯度图像再次进行分水岭运算,不仅增强了图像的边缘点,还减少了后续处理的计算量;再用熵惩罚因子优化分割能量函数,抑制了目标信息的损失.实验结果表明,所提算法同传统算法的分割结果相比较,降低了错误率,增大了Kappa系数,提高了运行效率,并且,提取的边缘也更完整、平滑,适用于不同类型的图像分割.%To solve the problem that GrabCut algorithm is sensitive to local noise, time consuming and edge extraction is not ideal, the paper put forward a new algorithm of improving image segmentation based on GrabCut. Multi-scale watershed was used for gradient image smoothing and denoising. Watershed operation was proposed again for the new gradient image, which not only enhanced image edge points, but also reduced the computation cost of the subsequent processing. Then the entropy penalty factor was used to optimize the segmentation energy function to prevent target information loss. The experimental results show that the error rate of the proposed algorithm is reduced, Kappa coefficient is increased and the efficiency is improved compared with the traditional algorithm. In addition, the edge extraction is more complete and smooth. The improved algorithm is applicable to different types of image segmentation.

  11. 基于交点和区域特征的线段裁剪算法%A Segment Clipping Algorithm Based on the Intersection and Region Features

    Institute of Scientific and Technical Information of China (English)

    陈定钰; 丁有和

    2014-01-01

    Proposes a new algorithm of straight-line segment clipping against rectangular window based on the thought of Weiler-Atherton and Co-hen-Sutherland algorithm. In this algorithm, divides the rectangular window into three regions by the horizontal and vertical direction each other. To reserve the straight-line directionality, every line endpoint in a region code is assigned a different value of-1, 0 or 1, and it can easily determine the "whole out" situation by code operations. To reduce the number of intersection computed, it takes full advan-tage of the properties that the line segment has only "in" and "out" point and exist in pairs. As a result, the application proves that this algorithm has the strong stability and high clipping efficiency.%由Weiler-Atherton和Cohen-Sutherland算法思想,提出一种基于交点和区域特征的线段裁剪算法。算法将矩形窗口按水平方向和竖直方向各划分成三个区域,并从线段的有向性出发,根据起点和终点的不同给出-1、0和1的编码值,从而简化了“弃之”情况的判断。在求交中,为了避免直线段与裁剪边的多次求交,充分利用直线段“入点”和“出点”的唯一性和成对存在的性质,使得该算法具有较强的稳定性和较高的裁剪效率。

  12. 基于半结构特征分割的 Web数据挖掘算法%Web Data Mining Algorithm Based on Semi Structure Feature Segmentation

    Institute of Scientific and Technical Information of China (English)

    杨丽萍

    2015-01-01

    提出一种基于半结构特征分割的Web数据挖掘算法。进行Web热点数据的信息流信号模型构建,对Web热点信息流进行包络特征分解,为了提高数据挖掘的纯度和抗干扰性能,采用前馈调制滤波器进行数据干扰滤波,采用半结构特征分割进行Web热点数据的特征提取,实现数据挖掘算法改进。仿真结果表明,采用该算法能提高对Web数据特征的检测性性能,数据挖掘中受到的旁瓣干扰较小,挖掘精度较高,性能优于传统算法。%A Web data mining algorithm based on semi structure feature segmentation is proposed .The information stream signal model of Web hot date is constructed and the characteristic erwelope decomposition of Web hot information stream is finished ,in order to improve the purity of data mining and the anti‐interference performance by feedforward filter modulation data interference filter ,using semi structural feature segmentation for web hot number according to feature extraction . The data mining algorithm is realized . Simulation results show that the new algorithm can improve the detection capability of characteristics of Web data , data mining has little sidelobe interference ,mining precision is high ,performance is better than traditional algorithm .

  13. 一种在线学习的视频图像分割算法%Online Learning Based Video Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    王爱平; 潘衡岳; 李思昆

    2012-01-01

    提出了一种在线学习的视频图像分割算法,通过结合视频图像的全局信息和局部信息,来完成视频图像的准确分割。该算法首先采用分类器对视频图像的无指导预分割结果进行整体的识别处理,得到粗糙的像素级前后景分割图像,再通过时空条件随机场最优化完成局部平滑处理,得到最终精确的像素级前后景分割图像。同时还提出了一种平衡采样策略和一种基于分割图像指导的样本更新算法,分别用以实现分类器准确的初始化和高效稳定的在线学习。基于真实视频序列的实验表明,相比已有方法,算法在低时间开销下,显著提高了分割的准确性与稳定性。%A novel online learning based video segmentation algorithm was proposed, combining both the global and local information of video images. The videos were pre-segmented by the unsupervised image segmentation method firstly, and then the coarse foreground was extracted by the detection of the classifier. After that, the final optimal pixel-wise segmentation was achieved by using spatial-temporal Conditional Random Fields, and the classifier was updated with the constraints of the segmentation result. Meanwhile, a balance sampling strategy and a sample-updating approach supervised by segmentation were proposed, to improve the accuracy and stability of the classifier on initialization and updating separately. Experiments on challenging video sequences show that the proposed method highly improves the precision and the stability of video segmentation with low time cost, compared to state-of-the-art methods.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-11-15

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

  15. One New Algorithm of Sperm Image Segmentation and Recognition%一种新的精子图像分割与识别算法

    Institute of Scientific and Technical Information of China (English)

    黄建灯

    2011-01-01

    In this paper, the image characteristics of sperm movement from the target and background of the differences between category set,put forward a method based on maximum cross entropy thresholding segmentation algorithm for image segmentation,sperm, introducing constraints to filter to remove impurities,application of morphology in each sperm target displayed on digital marker.Experimental results show that,the method can achieve fast and accurate segmentation of sperm, and automatic statistic image sperm number.%本文针对精子运动图像特点,从目标和背景的类间差异性出发,提出了一种基于最大类间交叉熵准则的阈值化分割新算法,对精子图像进行分割,引入约束条件筛选去除杂质,应用形态学在每个精子目标上显示数字标记。实验表明,该方法可以实现精子的快速准确分割,并自动统计出图像中精子的个数。

  16. Segmentation Algorithm for Oil Spill SAR Images Based on Hierarchical Agglomerative Clustering%基于HAC的溢油SAR图像分割算法

    Institute of Scientific and Technical Information of China (English)

    苏腾飞; 孟俊敏; 张晰

    2013-01-01

    图像分割是SAR溢油检测中的关键步骤,但由于SAR影像中存在斑点噪声,使得一般的图像分割算法难以收到理想的效果,严重影响溢油检测的精度.发展一种基于凝聚层次聚类(Hierarchical Agglomerative Clustering,HAC)的溢油SAR图像分割算法.该算法利用多尺度分割的思想,能够有效保持SAR影像中溢油斑块的形状特征,并能减少细碎斑块的产生.利用2010年墨西哥湾的Envisat ASAR影像开展了溢油SAR图像分割实验,并将该算法和Canny边缘检测、OTSU阈值分割、FCM分割、水平集分割等方法进行了对比.结果显示,HAC方法可以有效减少细碎斑块的产生,有助于提高SAR溢油检测的精度.%Image segmentation is a crucial stage in the SAR oil spill detection.However,the common image segmentation algorithms can hardly achieve satisfactory results due to speckle noise in the SAR images,thus affecting seriously the accuracy of oil spill detection.For this reason,an image segmentation algorithm which is based on HAC (Hierarchical Agglomerative Clustering) is developed for the oil spill SAR images.This method takes advantage of multi-resolution segmentation to maintain effectively the shape property of oil spill patches,and can reduce the formation of small patches.By using Envisat ASAR images of the Gulf of Mexico obtained in 2010,an experiment of SAR oil spill image segmentation has been conducted.Comparing with other approaches such as Canny,OTSU,FCM and Levelset,the results show that HAC can effectively reduce the producing of small patches,which is helpful to improve the accuracy of SAR oil spill detection.

  17. 基于视差图像的重叠果实图像分割算法%Image Segmentation Algorithm for Overlapping Fruits Based on Disparity Map

    Institute of Scientific and Technical Information of China (English)

    彭辉; 吴鹏飞; 翟瑞芳; 刘善梅; 吴兰兰; 景秀

    2012-01-01

    To solve the problem of segmentation for overlapping fruits, an image segmentation algorithm based on disparity map was developed. Firstly, binocular stereo images were obtained by binocular stereo vision system. Then, these images were preprocessed and rectified. Thirdly, stereo matching for rectified images pair to get disparity values for every pixel was made. At last, the disparity map was generated. Distance information in 3-D space was added to algorithm besides color, shape and texture information in 2-D space, so the fruits with different distances in actual space were segmented better in disparity map than in normal image. Experimental results showed that the accuracy of segmentation for disparity map by area-based method was 0. 90, and the edge detection error was 5. 74% . The proposed method was valid for segmentation of overlapping fruits.%为解决自动采摘视觉系统中重叠果实的分割问题,提出了基于视差图像的果实分割算法.采用双目立体视觉系统获取图像对,对图像对进行预处理和校正,通过图像对的立体匹配来获取视差图像,最后对视差图像进行分割.该算法将分割的依据和信息从二维图像的颜色、形状、纹理等扩展到三维空间的深度,对空间距离不同的目标具有较好的分割效果.实验表明,对获取的视差图像进行基于区域的分割时,其区域间灰度对比度为0.98,目标计数一致性达到0.90;进行基于边缘的分割时,其边缘检测误差为5.74%,因此,该方法对重叠果实区域的分割是有效的.

  18. ADAPTACIÓN DEL ALGORITMO MARACAS PARA SEGMENTACIÓN DE LA ARTERIA CARÓTIDA Y CUANTIFICACIÓN DE ESTENOSIS EN IMÁGENES TAC Adaptation of the MARACAS Algorithm for Carotid Artery Segmentation and Stenosis Quantification on CT Images

    Directory of Open Access Journals (Sweden)

    MARIA A ZULUAGA

    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 segmentations, and the mean stenosis quantification error was 14.4%.

  19. 基于谱聚类的极化SAR影像分割改进算法%Polarimetric SAR Image Segmentation Algorithm by Spectral Clustering

    Institute of Scientific and Technical Information of China (English)

    魏思奇; 张煜; 叶松

    2016-01-01

    谱聚类的影像分割算法是一种基于点的聚类方法,其通过选用不同的特征构建相似性度量矩阵,来衡量像元间的相似性程度.在解算过程中需要计算每2个像元间的相似性度量,在处理大幅影像时,运算量大、耗时长.针对这一问题,提出了一种改进方法.首先通过均值漂移算法对极化SAR影像进行预处理,然后选取中心像元,构建相似性度量矩阵,采用归一化分割准则完成影像分割.实验结果表明,该算法分割结果优良,准确性高,有效地提高了原算法的分割效率,具有一定的实践意义.%Image segmentation by spectral clustering is a clustering method based on points. It is characterized by the use of similarity measure matrixes. We usually need to calculate the similarity matrixes between every two cells, which consumes huge computation task and a lot of time when processing large images. To solve this problem, we propose an improved method. First, we use mean shift algorithm for polarimetric SAR image, and then select center pixel to construct similarity measure matrix. At last, we use the normalized segmentation rule for image segmenta-tion. Computation experiment proves that the algorithm could improve the efficiency with high accuracy and satisfac-tory result, hence is of practical significance.

  20. 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...

  1. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.

    Science.gov (United States)

    Tohka, Jussi

    2014-11-28

    Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

  2. Medical image segmentation by MDP model

    Science.gov (United States)

    Lu, Yisu; Chen, Wufan

    2011-11-01

    MDP (Dirichlet Process Mixtures) model is applied to segment medical images in this paper. Segmentation can been automatically done without initializing segmentation class numbers. The MDP model segmentation algorithm is used to segment natural images and MR (Magnetic Resonance) images in the paper. To demonstrate the accuracy of the MDP model segmentation algorithm, many compared experiments, such as EM (Expectation Maximization) image segmentation algorithm, K-means image segmentation algorithm and MRF (Markov Field) image segmentation algorithm, have been done to segment medical MR images. All the methods are also analyzed quantitatively by using DSC (Dice Similarity Coefficients). The experiments results show that DSC of MDP model segmentation algorithm of all slices exceed 90%, which show that the proposed method is robust and accurate.

  3. Electron Conformal Radiotherapy for Post-Mastectomy Irradiation: A Bolus-Free, Multi-Energy, Multi-Segmented Field Algorithm

    Science.gov (United States)

    2005-08-01

    The radiation oncologist outlines the PTV, which is the target area that will be treated with electrons . Figure 2 .2 shows a clinica l example of...post-mastectomy clinica l cases. These particular cases were previously treated using bolus ECT . After the segmented-field ECT plans were developed...size. This data was collected by the medica l physics staff at M . D. Anderson during the machine commissioning proces s for a linear accelerator . 19 3

  4. Ensemble segmentation using efficient integer linear programming.

    Science.gov (United States)

    Alush, Amir; Goldberger, Jacob

    2012-10-01

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

  5. Influence of reconstruction settings on the performance of adaptive thresholding algorithms for FDG-PET image segmentation in radiotherapy planning.

    Science.gov (United States)

    Matheoud, Roberta; Della Monica, Patrizia; Loi, Gianfranco; Vigna, Luca; Krengli, Marco; Inglese, Eugenio; Brambilla, Marco

    2011-01-30

    The purpose of this study was to analyze the behavior of a contouring algorithm for PET images based on adaptive thresholding depending on lesions size and target-to-background (TB) ratio under different conditions of image reconstruction parameters. Based on this analysis, the image reconstruction scheme able to maximize the goodness of fit of the thresholding algorithm has been selected. A phantom study employing spherical targets was designed to determine slice-specific threshold (TS) levels which produce accurate cross-sectional areas. A wide range of TB ratio was investigated. Multiple regression methods were used to fit the data and to construct algorithms depending both on target cross-sectional area and TB ratio, using various reconstruction schemes employing a wide range of iteration number and amount of postfiltering Gaussian smoothing. Analysis of covariance was used to test the influence of iteration number and smoothing on threshold determination. The degree of convergence of ordered-subset expectation maximization (OSEM) algorithms does not influence TS determination. Among these approaches, the OSEM at two iterations and eight subsets with a 6-8 mm post-reconstruction Gaussian three-dimensional filter provided the best fit with a coefficient of determination R² = 0.90 for cross-sectional areas ≤ 133 mm² and R² = 0.95 for cross-sectional areas > 133 mm². The amount of post-reconstruction smoothing has been directly incorporated in the adaptive thresholding algorithms. The feasibility of the method was tested in two patients with lymph node FDG accumulation and in five patients using the bladder to mimic an anatomical structure of large size and uniform uptake, with satisfactory results. Slice-specific adaptive thresholding algorithms look promising as a reproducible method for delineating PET target volumes with good accuracy.

  6. Morphology-driven automatic segmentation of MR images of the neonatal brain.

    Science.gov (United States)

    Gui, Laura; Lisowski, Radoslaw; Faundez, Tamara; Hüppi, Petra S; Lazeyras, François; Kocher, Michel

    2012-12-01

    The segmentation of MR images of the neonatal brain is an essential step in the study and evaluation of infant brain development. State-of-the-art methods for adult brain MRI segmentation are not applicable to the neonatal brain, due to large differences in structure and tissue properties between newborn and adult brains. Existing newborn brain MRI segmentation methods either rely on manual interaction or require the use of atlases or templates, which unavoidably introduces a bias of the results towards the population that was used to derive the atlases. We propose a different approach for the segmentation of neonatal brain MRI, based on the infusion of high-level brain morphology knowledge, regarding relative tissue location, connectivity and structure. Our method does not require manual interaction, or the use of an atlas, and the generality of its priors makes it applicable to different neonatal populations, while avoiding atlas-related bias. The proposed algorithm segments the brain both globally (intracranial cavity, cerebellum, brainstem and the two hemispheres) and at tissue level (cortical and subcortical gray matter, myelinated and unmyelinated white matter, and cerebrospinal fluid). We validate our algorithm through visual inspection by medical experts, as well as by quantitative comparisons that demonstrate good agreement with expert manual segmentations. The algorithm's robustness is verified by testing on variable quality images acquired on different machines, and on subjects with variable anatomy (enlarged ventricles, preterm- vs. term-born).

  7. Segmentation of the whole breast from low-dose chest CT images

    Science.gov (United States)

    Liu, Shuang; Salvatore, Mary; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

    2015-03-01

    The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.

  8. 基于改进特征值的语音分割算法研究%A Speech Segmentation Algorithm Based on Improved Eigenvalue

    Institute of Scientific and Technical Information of China (English)

    任新社; 缪华; 马青玉

    2011-01-01

    随着网络技术和媒体应用的迅速发展,传统的文本检索已不能满足需要,视频检索由于数据量大而得不到应用,语音检索就显示出重要的研究价值.一个语音序列由多种不同类型的语音片段构成,而每一种类型的语音往往又包含不同的意义,因此通过语音特征进行语音分段来实现语音检索是现代媒体数据进行检索的重要手段.通过对语音信号每一帧的基本特征值与整个语音序列的平均基本特征值进行比较,得到一个改进的特征值,并利用K—Nearest Neighbor算法进行语音分割,结果表明基于改进特征值的语音分割算法能够有效提高语音分割的准确性.%With the rapid development of internet technology and media application, text-based retrieval cannot satisfy the requirements and auditory-visual processing can not be applied for the large data amount, so the emergence of speech retrieval is particularly important. An audio clip usually consists of many different types of audio segments with different meanings ; therefore, it becomes a new method to perform speech retrieval with audio segmentation for modern media based on audio eigenvalue. In the article, the basic eigenvalue of each audio frame is compared with the average eigen- value of the entire audio clip and then the improved eigenvalue can be obtained for audio segmentation by using the K- Nearest Neighbor algorithm. The experimental results show that the proposed algorithm based on the improved eigenvalue can efficiently improve the accuracy of audio segmentation.

  9. Algorithm of Web Hot Data Mining Based on Structured Segmentation%基于半结构化分割的Web热点数据挖掘算法

    Institute of Scientific and Technical Information of China (English)

    阮梦黎

    2015-01-01

    随着大数据信息技术的发展,数据在线监测和数据挖掘成为计算机信息领域研究的热点。通过对Web热点数据分割挖掘,提高信息热点追踪和Web数据分类能力。传统算法采用非结构化数据挖掘算法,无法有效对Web热点数据进行准确定位和分层挖掘。提出一种基于半结构化分割的Web热点数据挖掘算法。采用半结构化数据进行特征分割,基于优秀基因位进行差分进化,使寻优曲线不断趋于平缓,在多个节点上并行的运行比较脚本,采用半结构化分割,使得Web热点特征挖掘实现自适应寻优,得到Web热点数据的分配因子,提高了挖掘性能。仿真结果表明,该算法获得了良好的效率和精度,提高了Web热点数据挖掘的自适应寻优能力。%With the development of big data information technology, online monitoring data and data mining has become a hot research field of computer information. The segmentation of Web hot data mining, improve the classification ability of information focus and Web data. Using the traditional algorithm of unstructured data mining algorithms, it is not valid for Web hot data for accurate positioning and layered mining. The paper proposed a mining algorithm Web hot data structured based on segmentation, feature segmentation using semi structured data, excellent genes are based on differential evolution, make the optimization curve tends to be gentle, parallel on multiple nodes running script, through the code makes the un⁃structured data mapped to the data block, make the data stored in the database relational data model, to get the distribution factor Web hot data, improve the mining performance.The simulation results show that the high efficiency and accuracy, it improved adaptive Web hotspot of data mining optimization ability.

  10. Improved Target Segmentation Algorithm for Aircraft Parking at Airport%改进的机场停泊飞行器目标分割算法

    Institute of Scientific and Technical Information of China (English)

    陆钰; 王金根; 陈秀娟; 徐靖涛

    2009-01-01

    考虑到飞行器目标在整幅图像中所占的比例往往较小,且图像背景复杂,本文提出了一种基于机场区域提取的飞行器目标分割算法.该算法首先利用Hough变换检测直线的特性,定位机场跑道和停机坪的位置,并结合教学形态学等图像处理技术去除了非机场区域;在提取机场区域后,再选择适当的阈值对图像进行分割,最后经过形态学去噪、小区域去除等步骤分割出飞行器目标.实验结果表明,该算法改进了以往机场区域提取算法保留候机楼等附属部分以及提取结果中存在机场区域以外区域的缺点,较好地实现了机场停泊飞行器目标的分割,为下一步准确识别飞行器类型奠定了基础.%Considering that the aircraft targets have less scale on the whole image with a complicated background,an algorithm is proposed to segment aircraft targets based on the airport area extracting.Firstly,by using the features of the detecting lines of the Hough transform,the runway and the apron are located.Then,the area out of the airport are wiped off by using some image processing technologies,such as mathematical morphology.After extracting the airport area,a proper threshold value is selected to segment the image.Finally,the aircraft targets are segmented after some steps,such as morphological de-noising and small area removing.The experimental result shows that the algorithm can overcome the disadvantages of the previous algorithms of the airport extraction for retaining the subsidiary parts,such as terminal buildings,thus the extracting results contain area out of the airport area.

  11. An image segmentation algorithm based on PCNN and improved OTSU%一种基于PCNN和改进的 OTSU 的图像分割算法

    Institute of Scientific and Technical Information of China (English)

    张松; 汪烈军; 祁彦庆

    2016-01-01

    The pulse coupled neural network (PCNN) is widely used in image segmentation in recent years .However ,due to the drawbacks of numerous parameters ,complex computation ,manual distinguishing for the number of iterations and the mediocre a‐daptability for low contrast images ,an improved algorithm was proposed in this paper .First ,PCNN parameters were simplified to single adjustable parameters .Then ,the image contrast as a new judge factor was added to the OTSU discriminant algorithm to determine PCNN iteration stop time .Finally PCNN synchronization ignition mechanism was applied to design a novel bidirectional PCNN algorithm which can remove isolated noise ,and make the edge of the segmentation more clear .Simulation results show that this method has better segmentation result and strong adaptability .%脉冲耦合神经网络(PCNN)已广泛应用于图像分割领域,但其参数众多,计算复杂,迭代次数需人工判别,且对低对比度的图像分割效果不理想。针对这些情况提出了算法改进,首先将PCNN的参数简化为单参数可调,然后将图像对比度作为新的判断因子加入最大类间方差(OTSU)的判别式中,以判断PCNN迭代的停止时间。最后利用PCNN的同步点火机制,设计出一种新的双向PCNN算法,对图像分割结果进行优化,去除孤立的噪点,同时使分割边缘更加清晰。计算机仿真结果表明,该方法具有较好的图像分割效果和较强的适应性。

  12. Brain CT segmentation based on multi-threshold optimized by glowworm swarm optimization algorithm%萤火虫群优化算法多阈值的脑CT图像分割

    Institute of Scientific and Technical Information of China (English)

    何毅; 葛延治

    2014-01-01

    The optimal threshold is the key in the brain tumor image segmentation of image threshold segmentation algorithm. In order to improve the segmentation accuracy of brain tumor, a novel brain tumor image segmentation method based on multi-threshold optimized by glowworm swarm optimization algorithm was proposed in this paper. Firstly, the mathematic model of multi-threshold method was established, secondly, glowworm swarm optimization al_gorithm was used to solve the mathematic model and find the optimal segmentation threshold of the image, and finally, image was segmented according to the optimal threshold. The results showed that our algorithm has improved brain tumor image segmentation accuracy and obtained better results of brain tumor image segmentation.%在基于多阈值的脑CT图像分割算法中,最佳阈值选取是脑CT图像中的关键,针对传统多阈值法的阈值选择难题,为了提高脑CT图像的分割准确率,提出一种萤火虫群算法优化多阈值的脑CT图像分割方法。首先建立了基于多阈值法的脑CT图像分割数学模型,然后通过萤火虫群算法数学模型进行求解,搜索到脑CT图像分割的最佳阈值,最后采用最佳阈值完成脑CT图像的分割。仿真结果表明,萤火虫群算法提高了脑CT图像的精度,获得了更加理想的脑CT图像结果。

  13. 前景/背景分割算法及其嵌入式实时实现%Real-time embedded implementation of foreground/background segmentation algorithm

    Institute of Scientific and Technical Information of China (English)

    张浩钧; 常勇; 李范鸣; 沈永格

    2012-01-01

    前景/背景分割算法是计算机视觉中一种常见的算法.其基本思想是利用背景中不同像素或帧与帧之间的相关性,判断每个像素点的灰度值,然后根据预测值和实际观察值判断当前像素属于前景还是背景.首先介绍了几种应用在不同场合的前景/背景分割算法.考虑到应用传统的基于处理器的平台很难实时实现这类计算量很大的算法,所以在该算法的有效性被确认后,重点介绍其嵌入式实时实现.同时引入了一种先进的实现算法的方法:可重构计算及其设计方法和流程.另外还讨论了几个重要的关于硬件实现算法的问题.在给出了如何应用可重构计算实现算法的实例后,介绍了如何把已实现的算法嵌入基于片上系统的成像系统,以实现一个完整的系统.%Foreground/Background (FG/BG) segmentation is one of the most widely-used computer vision (CV) algorithms. Its basic algorithm takes advantages of correlation between background of different pixels or frames, predicts gray intensity for every pixel point and judges whether it belongs to foreground or background based on the prediction and actual value. Several FG/BG algorithms used in different situation were introduced firstly. Considering that those tomputationally expensive algorithms are hard to operate in real time using the traditional processor-based platform, its real-time embedded implementation was highlighted after this algorithm being verified. The design methodology and flow of reconfigurable computing (RC) which was an advanced method to implement those algorithms were introduced. In addition, several important issues about hardware implementation were discussed. A case of study to elaborate how it works was presented, and how to attach the implemented algorithm into the SoPC-based imaging system to achieve an integrated system was described.

  14. 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

    2015-03-17

    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.

  15. 基于马尔可夫模型的多分辨率图像分割算法%Multi-resolution Image Segmentation Algorithm Based on Markov Model

    Institute of Scientific and Technical Information of China (English)

    肖然; 侯进

    2012-01-01

    为解决网像分割中过分割、欠分割和依赖初始分割问题,提出一种基于马尔可夫模型的多分辨率图像分割算法.利用变权重方法改进多分辨率马尔可夫随机场算法,结合曲波和小波变换对图像进行多分辨率分析,并通过区域合并减少图像中的区域数.实验结果表明,与经典算法相比,该算法的分割性能较好.%To solve the problem such as over-segmentation, under segmentation and dependence on initial segmented in image segmentation, this paper proposes a new multi-resolution image segmentation algorithm based on Markov model. It makes improvements on variable weight of Multi-resolution Markov Random Field(MRMRF) algorithm, combines curvelet transform and wavelet transform to multi-resolution analysis. It reduces area number of image by area merging. Experimental result proves the effectiveness of this algorithm compared with classical algorithms.

  16. Image segmentation based on improved spectral clustering algorithm%基于改进谱聚类的图像分割算法

    Institute of Scientific and Technical Information of China (English)

    关昕; 周积林

    2014-01-01

    针对传统谱聚类算法应用于图像分割时仅采用特征相似性信息构造相似性矩阵,而忽略了像素分布的空间临近信息的缺陷,提出一种新的相似性度量公式——加权欧氏距离的高斯核函数,充分利用图像特征相似性信息和空间临近信息构造相似性矩阵。在谱映射过程中,采用Nystrom逼近策略近似估计相似性矩阵及其特征向量,大大减少了求解相似性矩阵的运算复杂度,降低了内存消耗。对得到的低维向量子空间采用一种新型的聚类算法——近邻传播聚类算法进行聚类,避免了传统谱聚类采用K-means算法对初始值敏感,易陷入局部最优的缺陷。实验表明该算法获得了比传统谱聚类算法更好的分割效果。%Aiming at the default that when the traditional spectral clustering algorithm is applied to image segmentation, it only uses the feature similarity information to construct similarity matrix and ignores the spatial adjacency information defect of spatial distribution of pixels, this paper presents a new similarity measure formula—weighted euclidean distance of the Gaussian kernel function, making full use of image feature similarity information and spatial adjacency information to structure similarity matrix. In the spectral mapping process, using Nystrom approximation strategy to approximate simi-larity matrix and eigenvectors, it greatly reduces the computational complexity to solve similarity matrix and reduces the memory consumption. This paper applies a new clustering algorithm—Affinity Propagation to the low-dimensional sub-space. It avoids the defect that traditional spectral clustering using K-means algorithm can not automatically determine the number of clusters and it is sensitive to initial value and easy to fall into local optimum. The experiments prove that the proposed algorithm obtains better segmentation results than the traditional spectral clustering algorithm.

  17. AnatomiQuiz

    DEFF Research Database (Denmark)

    Brent, Mikkel Bo; Kristoffersen, Thomas

    2015-01-01

    AnatomiQuiz er en quiz-app udviklet til bevægeapparatets anatomi. Den består af mere end 2300 spørgsmål og over 1000 anatomiske billeder. Alle spørgsmål tager udgangspunkt i lærebogen Bevægeapparatets anatomi af Finn Bojsen-Møller m.fl.......AnatomiQuiz er en quiz-app udviklet til bevægeapparatets anatomi. Den består af mere end 2300 spørgsmål og over 1000 anatomiske billeder. Alle spørgsmål tager udgangspunkt i lærebogen Bevægeapparatets anatomi af Finn Bojsen-Møller m.fl....

  18. Infrared target segmentation algorithm based on partial histogram%基于局部直方图的红外目标分割算法

    Institute of Scientific and Technical Information of China (English)

    冯帅; 赵金博

    2014-01-01

    In infrared remote target detection system,the sky-background changes slowly,but the target shows the par-tial corner. It has a broad gray range and is brighter than the background. According to these characteristics,a target segmentation algorithm based on partial histogram is presented. The distribution characteristics of multi-target histo-gram are analyzed. According to gray level distribution rule and the image pixels restriction,the image segmentation is realized. Experiment results show that this method can segment flying targets rapidly and effectively,and it has strong practicability.%在远程红外探测系统中,背景为缓慢变化的天空,而目标则表现为局部奇异点。目标灰度分布范围大,局部较亮,边缘与背景对比度低。根据这一特性,提出了一种基于局部直方图的目标分割算法。文中分析了多个目标的直方图分布特性,根据其灰度分布规律和像素个数判决条件实现了目标的有效分割。该算法适用于空域背景下的飞行目标分割。经过仿真验证表明,本文所提出的算法能快速有效地分割出红外飞行目标,有很强的实用性。

  19. Computerized segmentation algorithm with personalized atlases of murine MRIs in a SV40 large T-antigen mouse mammary cancer model

    Science.gov (United States)

    Sibley, Adam R.; Markiewicz, Erica; Mustafi, Devkumar; Fan, Xiaobing; Conzen, Suzanne; Karczmar, Greg; Giger, Maryellen L.

    2016-03-01

    Quantities of MRI data, much larger than can be objectively and efficiently analyzed manually, are routinely generated in preclinical research. We aim to develop an automated image segmentation and registration pipeline to aid in analysis of image data from our high-throughput 9.4 Tesla small animal MRI imaging center. T2-weighted, fat-suppressed MRIs were acquired over 4 life-cycle time-points [up to 12 to 18 weeks] of twelve C3(1) SV40 Large T-antigen mice for a total of 46 T2-weighted MRI volumes; each with a matrix size of 192 x 256, 62 slices, in plane resolution 0.1 mm, and slice thickness 0.5 mm. These image sets were acquired with the goal of tracking and quantifying progression of mammary intraepithelial neoplasia (MIN) to invasive cancer in mice, believed to be similar to ductal carcinoma in situ (DCIS) in humans. Our segmentation algorithm takes 2D seed-points drawn by the user at the center of the 4 co-registered volumes associated with each mouse. The level set then evolves in 3D from these 2D seeds. The contour evolution incorporates texture information, edge information, and a statistical shape model in a two-step process. Volumetric DICE coefficients comparing the automatic with manual segmentations were computed and ranged between 0.75 and 0.58 for averages over the 4 life-cycle time points of the mice. Incorporation of these personalized atlases with intra and inter mouse registration is expected to enable locally and globally tracking of the morphological and textural changes in the mammary tissue and associated lesions of these mice.

  20. 基于Mean Shift和随机游走的图像分割算法%Image Segmentation Algorithm Based on Mean Shift and Random Walk

    Institute of Scientific and Technical Information of China (English)

    穆克; 程伟; 褚俊霞

    2012-01-01

    An improved random walk algorithm was proposed herein.First,Mean Shift algorithm was adopted to preprocess the image,which was partitioned into a series of homogeneous areas,so that the homogeneous areas were taken as nodes to walk at random,with noise inhibited while reducing the number of nodes.Second,PMD was used to define the weight between regions.Thirdly,seeds were improved to have added the auxiliary seeds,and the auxiliary and signed seeds were used to walk random,with region merging realized.The final image segmentation was reached.Experimental results expatiates that the proposed method highlights the segmentation accuracy.%提出了一种改进的随机游走算法。首先,采用Mean Shift算法对图像进行预处理,将图像划分成一些同质区域,用同质区域作为节点进行随机游走,在降低节点数的同时也抑制了噪声对分割的影响;其次,利用马氏距离定义区域之间的权值;对种子点进行了改进,增加了辅助种子点,利用辅助种子点和用户标记的种子点进行随机游走,实现同质区域的合并,实现图像的最终分割。实验结果表明,该算法提高了图像分割的精度。

  1. 基于视觉特征的网页最优分割算法%Web Page Optimal Segmentation Algorithm Based on Visual Features

    Institute of Scientific and Technical Information of China (English)

    李文昊; 彭红超; 童名文; 石俊杰

    2015-01-01

    网页分割技术是实现网页自适应呈现的关键.针对经典的基于视觉的网页分割算法VIPS(Vision-based Page Segmentation Algorithm)分割过碎和半自动的问题,基于图最优划分思想提出了一种新颖的基于视觉的网页最优分割算法VWOS(Vision-based Web Optimal Segmentation).考虑到视觉特征和网页结构,将网页构造为加权无向连通图,网页分割转化为图的最优划分,基于Kruskal算法并结合网页分割的过程,设计网页分割算法VWOS.实验证明,与VIPS相比,采用VWOS算法分割网页的语义完整性更好,且不需要人工参与.

  2. 手背静脉图像增强和分割方法%Novel Algorithm For Hand Vein Image Enhancement and Segmentation

    Institute of Scientific and Technical Information of China (English)

    胡学友

    2014-01-01

    近红外摄像机采集到的手背静脉图像对比度较低且静脉结构简单,为了有效提取手背静脉结构特征,首先分割出包含主要静脉结构信息的区域,并进行灰度归一化;然后利用动态全局阈值法对静脉结构进行粗分割;最后根据静脉的几何结构特征,去除虚假静脉,获得真实的手背静脉图像;实验结果证明了算法的有效性。%The hand vein structure is simple and the contrast is low for the hand vein image captured by the near infrared camera. In order to effectively extract the hand vein structure feature, firstly, the AOI is segmented to reduce the influence of background and edge, and the gray values normalization is done ; secondly, dynamic global threshold method is used to roughly segment the vein structure; Finally, according to the structure characteristics of hand vein, the false vein is removed and get the read vein image;The experimental results show the efficiency for the algorithm proposed in this paper.

  3. Patellofemoral anatomy and biomechanics.

    Science.gov (United States)

    Sherman, Seth L; Plackis, Andreas C; Nuelle, Clayton W

    2014-07-01

    Patellofemoral disorders are common. There is a broad spectrum of disease, ranging from patellofemoral pain and instability to focal cartilage disease and arthritis. Regardless of the specific condition, abnormal anatomy and biomechanics are often the root cause of patellofemoral dysfunction. A thorough understanding of normal patellofemoral anatomy and biomechanics is critical for the treating physician. Recognizing and addressing abnormal anatomy will optimize patellofemoral biomechanics and may ultimately translate into clinical success.

  4. Anatomy of the lymphatics.

    Science.gov (United States)

    Skandalakis, John E; Skandalakis, Lee J; Skandalakis, Panagiotis N

    2007-01-01

    The lymphatic system is perhaps the most complicated system of Homo sapiens. An introduction to the anatomy, embryology, and anomalies of the lymphatics is presented. The overall anatomy and drainage of the lymphatic vessels in outlined. The topographic anatomy, relations, and variations of the principle vessels of the lymphatic system (the right lymphatic duct, the thoracic duct, and the cisterna chyli) are presented in detail.

  5. Robust Optic Nerve Segmentation on Clinically Acquired CT.

    Science.gov (United States)

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

    2014-03-21

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

  6. Color image segmentation based on normalized cut and fish swarm optimization algorithm%基于鱼群算法优化normalized cut的彩色图像分割方法

    Institute of Scientific and Technical Information of China (English)

    周逊; 郭敏; 马苗

    2013-01-01

    为了克服传统的谱聚类算法求解normalized cut彩色图像分割时,分割效果差、算法复杂度高的缺点,提出了一种基于鱼群算法优化normalized cut的彩色图像分割方法.先对图像进行模糊C-均值聚类预处理,然后用鱼群优化算法替代谱聚类算法求解Ncut的最小值,最后通过最优个体鱼得到分割结果.实验表明,该方法耗时少,且分割效果好.%Traditional spectral clustering algorithm minimizing normalized cut criterion has an inaccurate result and a high algorithm complexity in color image segmentation. In order to improve these disadvantages, this paper proposed a color image segmentation method based on normalized cut and fish swarm optimization algorithm. It firstly used fuzzy C-means dealing with color image, then employed fish swarm optimization algorithm instead of spectral clustering algorithm to minimize normalized cut, finally got segmentation result by the optimal individual fish. Experimental results show that the method achieves consumes less time, and achieves a precise segmentation result.

  7. 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.

  8. Application of SVM Algorithm for Particle Swarm Optimization in Apple Image Segmentation%基于粒子群优化SVM的苹果图像分割

    Institute of Scientific and Technical Information of China (English)

    黄奇瑞

    2015-01-01

    苹果图像分割是苹果采摘机器人视觉系统中识别和定位的关键技术。针对目前苹果采摘机器人对果实识别误差大、处理时间长等问题,结合粒子群算法在求解组合优化问题时具有的全局搜索特性,提出了一种基于粒子群参数优化的 SVM 分割算法。试验结果表明:该算法能很好地实现苹果果实与图像背景的分离,后续利用数学形态学中的闭运算对分割后的图像进行处理,能够较好地保存苹果轮廓信息、消除孔洞现象,为完善苹果采摘机器视觉系统的识别和定位提供技术支持。%The apple image segmentation is the key technology of identification and location in the apple-picking machine vision system. On account of huge errors in the process of discriminating fruits by apple-picking robots at present and the long-time processing, the SVM theory in fingerprint image segmentation method is conducted. Combined with the global search ability of particle swarm optimization in solving combinational optimization problems, the SVM partitioning algorithm, which is based on the parameter optimization of particle swarm, is put forward. The results show that this algorithm makes the separation of apple fruits and the image background come true. It also preserves the outline of apples, then polishes the image after segmentation by the close operation in mathematical morphology, which eliminates the pore phenomenon effectively and provides convenience for the further apple-picking and apple-discriminating.

  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. High efficient image segmentation algorithm based on improved differential evolution%基于改进差分演化的高效图像分割算法

    Institute of Scientific and Technical Information of China (English)

    范泽华; 白铁成

    2016-01-01

    差分演化算法的实现简单有效,但其搜索能力较弱,对此提出一种基于贝塔分布的控制参数动态设置策略以提高差分演化的优化效果,并将其应用于图像分割问题。首先,将图像的直方图按强度分为两类,并按类内方差、类间方差与总方差总结为待优化的目标函数;然后,使用改进的差分演化算法搜索图像分割目标函数的最优解,其中在每轮迭代中使用贝塔分布动态的设置控制参数。仿真实验表明,该方法获得了较好的优化结果,并获得了较好的图像分割效果。%The differential evolution algorithm is effective and easy to realize,but it has poor search ability,so a control parameter dynamic setting strategy based on beta distribution is proposed to improve the optimization effect of the differential evo⁃lution,and applied to the image segmentation. In the scheme,the image histograms are divided into two classes according their intensity,and summarized to the waiting optimization target function according to the inner⁃class variance,inter⁃class variance and total variance. And then,the improved differential evolution algorithm is used to search the optimal solution of the image segmentation target function,in which the beta distribution is used to set the control parameters dynamically in each iteration. The simulation experiment results show that the proposed method can obtain better optimal result and good image segmentation effect.

  11. Keypoint Transfer Segmentation

    OpenAIRE

    Wachinger, C.; Toews, M.; Langs, G.; Wells, W.; Golland, P.

    2015-01-01

    We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for th...

  12. FUZZY CLUSTERING ALGORITHMS FOR WEB PAGES AND CUSTOMER SEGMENTS%Web页面和客户群体的模糊聚类算法

    Institute of Scientific and Technical Information of China (English)

    宋擒豹; 沈钧毅

    2001-01-01

    Web log mining is broadly used in E-commerce and personalizationof the Web. In this paper, the fuzzy clustering algorithms for Web pages and customers is presented. First, the fuzzy sets of Web page and customer are setup separately according to the hitting information of customers. Second, the fuzzy similarity matrices ave constructed on the basis of the fuzzy sets and the Max-Min similarity measure scheme. Finally, Web page clusters and tustomer segments are abstracted directly from the corresponding fuzzy similarity matrix. Experiments show the effectiveness of the algorithm.%web日志挖掘在电子商务和个性化web等方面有着广泛的应用.文章介绍了一种web页面和客户群体的模糊聚类算法.在该算法中,首先根据客户对Web站点的浏览情况分别建立Web页面和客户的模糊集,在此基础上根据Max—Min模糊相似性度量规则构造相应的模糊相似矩阵,然后根据模糊相似矩阵直接进行聚类.实验结果表明该算法是有效的.

  13. Segmentation of Color Images Based on Different Segmentation Techniques

    Directory of Open Access Journals (Sweden)

    Purnashti Bhosale

    2013-03-01

    Full Text Available In this paper, we propose an Color image segmentation algorithm based on different segmentation techniques. We recognize the background objects such as the sky, ground, and trees etc based on the color and texture information using various methods of segmentation. The study of segmentation techniques by using different threshold methods such as global and local techniques and they are compared with one another so as to choose the best technique for threshold segmentation. Further segmentation is done by using clustering method and Graph cut method to improve the results of segmentation.

  14. A Novel DTI Image Segmentation Algorithm Based on Markov Random Field%一种基于马尔可夫随机场的弥散张量成像(DTI)图像分割的新算法

    Institute of Scientific and Technical Information of China (English)

    彭洁; 徐启飞; 王华峰; 吕庆文; 冯衍秋; 陈武凡

    2012-01-01

    Objective To propose a novel Markov random field (MRF) based segmentation algorithm for diffusion tensor images (DTI). Methods The distance measure defined by Frobenius norm was introduced in order to utili2e more spacial information of the diffusion tensor matrix of image voxels. The segmentation issue was transformed to the Minimum A Posteriori (MAP) by Beyes theorem, and the Iterative Conditional Model (ICM) algorithm was employed to achieve the solution of latter MAP problem. Results The comparison of segmentation results between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which indicated that the proposed algorithm could segment the DTI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DTI image than that in conventional MRI T2WI image. Conclusion The proposed algorithm can adequately utilize spacial information contained in voxel's diffusion tensor matrix to achieve the efficient segmentation of DTI images.[Chinese Medical Equipment Journal.2012.33(4):12-13]%目的:提出一种基于马尔可夫随机场(MRF)的弥散张量成像(DTI)图像分割的算法.方法:利用马尔可夫随机场模型,挖掘图像中的弥散张量信息,根据贝叶斯定理将图像分割问题转化为最小后验能量的求取,运用迭代条件模型求解.结果:该算法对DTI图像分割效果明显优于K均值算法,且该效果亦优于该算法对常规MRI T2WI图像的分割效果.结论:该算法能够充分利用弥散张量矩阵蕴含的空间上下文信息实现DTI图像的有效分割.

  15. 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.

  16. 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…

  17. 基于小波变换和改进的FCM算法的医学 CT图像分割法%Medical CT Image Segmentation Based on Wavelet Transform and Improved FCM Algorithm

    Institute of Scientific and Technical Information of China (English)

    马春

    2016-01-01

    为提高计算机辅助诊断的准确性,提出一种基于小波变换和改进的模糊C均值( Fuzzy C-Means, FCM)算法的医学CT图像分割方法。以FCM算法为基础,首先利用小波变换对医学图像进行分解,用分解后低频图像的像素点作为FCM算法的样本点;其次,利用马氏距离来进一步修正 FCM_S( FCM_Spatial)算法,修正后的 FCM 算法能更加精确地反映医学图像的信息。实验结果表明,算法的效率得到较大提高。%In order to enhance the accuracy of computer auxiliary diagnosis, a medical CT image segmentation algorithm based on wavelet transform and improved FCM algorithm is proposed .Because the traditional FCM algorithm usually run on all im-age pixels, which makes the efficiency of the algorithm reduced.On the basis of FCM algorithm, firstly this algorithm processes the image using wavelet transform, and the low frequency images by wavelet transform are inputted into FCM algorithm to obtain seg-mentation results.It not only greatly reduces the time complexity of the algorithm but also effectively suppresses image noise .Sec-ondly, the algorithm introduces the Mahalanobis distance to improve FCM_S algorithm, and the improved FCM algorithm can be more accurate to obtain medical image information .The experiments show that this algorithm significantly improves the segmenta-tion’s efficiency.

  18. Random walks with shape prior for cochlea segmentation in ex vivo μCT

    DEFF Research Database (Denmark)

    Ruiz Pujadas, Esmeralda; Kjer, Hans Martin; Piella, Gemma;

    2016-01-01

    Purpose Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we...... previously proposed the use of a high-resolution model built from μCT images and then adapted to patient-specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate μCT segmentation algorithms. Methods We...... propose a new framework for cochlea segmentation in ex vivo μCT images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image...

  19. A two-level approach towards semantic colon segmentation: removing extra-colonic findings.

    Science.gov (United States)

    Lu, Le; Wolf, Matthias; Liang, Jianming; Dundar, Murat; Bi, Jinbo; Salganicoff, Marcos

    2009-01-01

    Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.

  20. 一种基于PSO优化HWFCM的快速水下图像分割算法%A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

    Institute of Scientific and Technical Information of China (English)

    王士龙; 徐玉如; 庞永杰

    2011-01-01

    The S/N of an underwater image is low and has a fuzzy edge. Ifusing traditional methods to process it directly, the result is not satisfying. Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background, its time-consuming computation is often an obstacle. The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task. So,by using the statistical characteristics of the gray image histogram, a fast and effective fuzzy C-means underwater image segmentation algorithm was presented. With the weighted histogram modifying the fuzzy membership, the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm, so as to speed up the efficiency of the segmentation, but also improve the quality of underwater image segmentation. Finally, particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above. It made up for the shortcomings that the FCM algorithm can not get the global optimal solution. Thus, on the one hand,it considers the global impact and achieves the local optimal solution, and on the other hand, further greatly increases the computing speed. Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced. They enhance efficiency and satisfy the requirements of a highly effective, real-time AUV.

  1. A Novel Method for the Detection of Microcalcifications Based on Multi-scale Morphological Gradient Watershed Segmentation Algorithm

    Directory of Open Access Journals (Sweden)

    S. Vijaya Kumar

    2010-07-01

    Full Text Available This paper presents an automated system for detecting masses in mammogram images. Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of ammograms generated in widespread screening. Microcalcifications (calcium deposits and masses are the earliest signs of breast carcinomas and their detection is one of the key issues for breast cancer control. Computer-aided detection of Microcalcifications and masses is an important and challenging task in breast cancer control. This paper presents a novel approach for detecting microcalcification clusters. First digitized mammogram has been taken from Mammography Image Analysis Society (MIAS database. The Mammogram is preprocessed using Adaptive median filtered. Next, the microcalcification clusters are identified by using the marker extractions of the gradient images obtained by multiscale morphological reconstruction and avoids Oversegmentation vivid in Watershed algorithm. Experimental result show that the microcalcification can be accurately and efficiently detected using the proposed approach.

  2. 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.)

  3. Auxiliary anatomical labels for joint segmentation and atlas registration

    Science.gov (United States)

    Gass, Tobias; Szekely, Gabor; Goksel, Orcun

    2014-03-01

    This paper studies improving joint segmentation and registration by introducing auxiliary labels for anatomy that has similar appearance to the target anatomy while not being part of that target. Such auxiliary labels help avoid false positive labelling of non-target anatomy by resolving ambiguity. A known registration of a segmented atlas can help identify where a target segmentation should lie. Conversely, segmentations of anatomy in two images can help them be better registered. Joint segmentation and registration is then a method that can leverage information from both registration and segmentation to help one another. It has received increasing attention recently in the literature. Often, merely a single organ of interest is labelled in the atlas. In the presense of other anatomical structures with similar appearance, this leads to ambiguity in intensity based segmentation; for example, when segmenting individual bones in CT images where other bones share the same intensity profile. To alleviate this problem, we introduce automatic generation of additional labels in atlas segmentations, by marking similar-appearance non-target anatomy with an auxiliary label. Information from the auxiliary-labeled atlas segmentation is then incorporated by using a novel coherence potential, which penalizes differences between the deformed atlas segmentation and the target segmentation estimate. We validated this on a joint segmentation-registration approach that iteratively alternates between registering an atlas and segmenting the target image to find a final anatomical segmentation. The results show that automatic auxiliary labelling outperforms the same approach using a single label atlasses, for both mandibular bone segmentation in 3D-CT and corpus callosum segmentation in 2D-MRI.

  4. An Algorithm for Feature Points Detection Based on Univalue Segment Assimilating Nucleus%一种基于USAN的特征点检测算法

    Institute of Scientific and Technical Information of China (English)

    杨幸芳; 黄玉美; 李艳; 高峰

    2011-01-01

    The SUSAN(Smallest Univalue Segment Assimilating Nucleus) corner operator is proposed under the assumption that the corners to be detected are L-shaped,which results in SUSAN operator′s limitations in using USAN(Univalue Segment Assimilating Nucleus) region′s size as the criterion.In fact,wrong detections often happen when the USAN region′s size is equal to half of the area of the SUSAN circular mask.A ring-shaped mask was attached within the SUSAN circular mask based on the analysis of essential distinction of various image features and the times of intensity change was used as the criterion to overcome the deficiency of SUSAN operator.In addition,the USAN region is obtained by using a fixed brightness difference threshold,which is disadvantageous for corner detection with different contrast image.Therefore,we propose an iterative calculating method for brightness difference threshold,and calculates the brightness difference threshold of the corresponding SUSAN circular mask at each pixel location by iterative operation whereby to obtain a more pretty USAN region.The proposed algorithm provides double assurance by using the size of USAN region as the first criterion and supplementing the times of brightness change as the second criterion.Experimental results show that the algorithm can accurately and reliably extract various types of corners.%SUSAN角点检测算子的提出是以假设待测角点是L型为前提的,这就造成了SUSAN算子在检测角点时以USAN区域的大小为判据的局限性。实际上,当USAN区域的大小等于SUSAN圆模板面积的一半的时候,常常会出现错误的检测结果。在分析图像各特征点的本质区分的基础上,在SU-SAN圆模板内,附加了一个圆环模板,并以圆环模板上灰度的跳变次数为辅助判据,来弥补SUSAN算子的不足。此外,SUSAN算子USAN区域的划分是基于固定灰度差阈值的,这对于具有不同对比度的图像的角点提取很不利。鉴

  5. A Research on Improving Image Segmentation Algorithm Based on Curve Evolution%曲线演化的图像分割算法改进研究

    Institute of Scientific and Technical Information of China (English)

    曾金发; 曹功坤

    2011-01-01

    This essay has made a research on improving the auto-detection effect for images based on curve evolution.Since the image recognition and accuracy are low, the traditional Chan-vese active contour model (C-V model) can not detect the edge far away from the active contour lines and different from the average gray value.By using the gray weighted average of object and background out of image of evolution curves of level collection and adjusting the weights, the image segmentation algorithm can converge the evolution curve accurately and quickly to the image edge far away from the average gray intensity.The algorithm has the ability of topology changes.It can split fast, overcome the defects that C-V model can not detect the edge, accelerate the convergence speed of image segmentation and improve the sesmentafion effect.%研究提高曲线演化的图像自动检测效果问题.由于图像识别度低、准确度低,传统 Chan-vese 活动轮廓模型(C-V 模型)不能检测到远离活动轮廓线且与平均灰度值相差大的边缘.图像分割算法采用水平集演化曲线外图像的目标和背景的灰度加权平均值,通过调节权重值,使演化曲线能准确快速收敛于远离平均灰度强度的图像边缘上.该算法具备拓扑变化能力,分割速度快,能克服原 C-V 模型不能检测到边缘缺陷,加速图像分割的收敛速度,提高分割效果.

  6. 遗传算法粒在二维最大熵值图像分割中的应用%2-D Maximum Entropy Method of Image Segmentation Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    欧萍; 贺电

    2011-01-01

    研究图像分割,针对从图像中提取用户要求的特征目标,最优阈值的选取是图像准确分割的关键技术.传统二维最大熵值算法的最优阈值采用穷举方式进行寻优,耗时长,分割效率较低,易产生误分割.为了提高图像分割效率和准确性,提出一种遗传算法的二维最大熵值图像分割方法.先对原始图像进行灰度转换,绘制出图像的二维直方图.根据二维直方图信息选取适当灰度值进行初始化,采用遗传算法的初始种群,通过遗传算法选择、交叉和变异操作搜索最优阈值,获得的最优阈值对图像进行分割.实验结果表明,与传统二维最大熵值的图像分割算法相比,方法不仅运算速度加快,提高了分割效率,而且图像分割精度也大大提高.%In the 2-d image segmentation algorithm of maximum entropy value, the optimum threshold selection of image segmentation is the key technique. Traditional 2-d maximum entropy image segmentation algorithms use exhaustive way to find the optimal threshold, which is time-consuming, low efficient, and easy to generate the false division. In order to improve the accuracy and efficiency of image segmentation, this paper puts forward a genetic algorithm of 2-d maximum entropy value for image segmentation. This method firstly carries out gray level transform of the original image and draws the 2-d histogram. Then, according to the 2-d histogram information, appropriate gray value is selected to be initialized, The initial population of genetic algorithm is desinod, and each individual is represented with a mo-dimensional vector. Through the operators of selection, crossover and mutation, the optimal thresholds are searched, wich finally is taken as the optimal threshold of image segmentation. Experimental results show that compared with the maximum entropy with traditional 2-d image segmentation algorithm, this method can improve the computation speed, efficiency, and image

  7. 基于竞选算法的图像多阈值分割技术%Multilevel Thresholding Segmentation Technique Based on Election Campaign Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    涂叙安; 吕文阁

    2016-01-01

    This paper presents a method to get the optimal threshold based on gray-level histogram transform, using the election campaign algorithm as the basis of rapid search the global optimal threshold algorithm. First,select multiple grey value for a group divided gray-level histogram into several parts, this histograms synthesized normal distribution function and multiplied by their weights, add them as a new distribution function. after that,the new function and the original histogram are normalized respectively and in the solution space solving the absolute value of difference between them,the smallest set of grey value as the image segmentation thresholds.%提出了一种基于灰度直方图变换求解最佳阈值的方法,采用竞选算法作为快速搜索全局最优阈值的基础算法。首先,选取多个灰度值为一组,将整个灰度直方图分割成几部分,分别对这些直方图拟合成正态分布函数后加权相加合成一个新的分布函数,然后,将优化分布函数与原直方图分别归一化处理,在解空间内求解它们差值的绝对值的和,取和值最小的那组灰度值作为图像的分割阈值。

  8. Anatomy of Sarcocaulon

    Directory of Open Access Journals (Sweden)

    R. L. Verhoeven

    1983-12-01

    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.

  9. Skull Base Anatomy.

    Science.gov (United States)

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

    2016-02-01

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

  10. On Customer Segmentation Based on K-means Clustering Algorithm%基于K-means聚类算法的客户细分探讨

    Institute of Scientific and Technical Information of China (English)

    缪兵

    2012-01-01

    The paper focuses on importance of K-means clustering algorithm among clustering analyses in the analysis of customer value and provides customer segmentation through analysis of the customer's current and potential values.On this basis,enterprises may identify characteristics of various types of customers in conjunction with industrial features and implement differentiated service strategies and offer better resources and services to the most valuable customers,so as to achieve the goal of profitability as well as customer satisfaction.%重点讨论了聚类分析方法中K-means聚类算法在客户价值分析中的作用,通过对客户的现有价值和潜在价值进行分析,对客户进行细分。在此基础上,企业可结合行业的特征找出各类客户的特点,实行差异化服务策略,让更好的资源和服务提供给最有价值客户,从而达到顾客满意、企业盈利的目的。

  11. 基于 Lab 空间和 K-Means 聚类的叶片分割算法研究%Segmentation Algorithm Based on Blade Lab Space and K-Means Clustering

    Institute of Scientific and Technical Information of China (English)

    邹秋霞; 杨林楠; 彭琳; 郑强

    2015-01-01

    By classifying plant leaves has important significance in the study of plant species identification , classification in plant leaves , leaves of accurate segmentation is a necessary prerequisite to classify .This paper analyzes the contrast between the traditional threshold segmentation of the largest class clustering two variance method and K-Means segmenta-tion algorithm , to achieve segmentation leaves and RGB space conversion to Lab space , and then use two algorithms were split .The results show that the traditional threshold segmentation and K -Means clustering segmentation can not be the target image accurately segmented;in Lab space for a component of threshold segmentation can remove the shadow part , but the segmentation results for binary image;while in Lab space K-Means clustering segmentation , not only can effec-tively eliminate the shaded area in the captured image generated by the process , and after the image segmentation for col-or images , the extraction of texture and color features more convenient and improve the classification accuracy .%对植物叶片进行分类,在植物种类鉴别研究中有着重要的意义,而在植物叶片分类中,对叶片的准确分割是进行分类的必要前提。为此,对比分析了传统阈值分割中的最大类间方差法和 K-Means 聚类两种分割算法,实现对叶片的分割,并将RGB空间转换到 Lab空间,再利用两种算法分别进行分割。结果表明:传统的阈值分割和K-Means 聚类分割无法将目标图像准确地分割出来;在Lab空间对 a 分量进行阈值分割可以去除阴影部分,但是分割结果为二值图像;而在Lab空间进行 K-Means 聚类分割,不仅能够有效地消除在拍摄图像过程中产生的阴影部分,而且分割后的图像为彩色图像,对纹理和颜色特征的提取更加方便,提高了分类识别的准确率。

  12. 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…

  13. Anatomy of the Heart

    Science.gov (United States)

    ... Share this page from the NHLBI on Twitter. Anatomy of the Heart Your heart is located under your ribcage in the center of your chest between your right and left lungs. Its muscular walls beat, or contract, pumping blood ...

  14. Keypoint Transfer Segmentation.

    Science.gov (United States)

    Wachinger, C; Toews, M; Langs, G; Wells, W; Golland, P

    2015-01-01

    We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm's robustness enables the segmentation of scans with highly variable field-of-view.

  15. 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.

  16. A Novel Method for Image Segmentation Based on Improved OTSU and Improved Genetic Algorithm%一种结合改进OTSU法和改进遗传算法的图像分割方法

    Institute of Scientific and Technical Information of China (English)

    李贤阳; 黄婵

    2012-01-01

    In order to automatically determine the optimal threshold in image segmentation, a novel method for image segmentation based on improved OTSU and improved genetic algorithm was presented in this paper. This improved genetic algorithm can be used to globally optimize 2-mension OTSU image segmentation functions, and can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population for rapidly converging to get the optimal thresholds in image segmentation. It overcomes the shortcomings in traditional genetic algorithm. The theoretically analysis and simulate experiments show that the range of the thresholds is more stable and it less time consuming and better satisfies the request of real-time processing in image segmentation, compared with 2-mension OTSU image segmentation and genetic algorithm based image segmentation.%为了自动确定图像分割的最佳阈值,提出了一种结合改进OTSU法和改进遗传算法的图像分割方法,即利用这种改进遗传算法对二维OTSU图像分割函数进行全局优化,该方法能够根据个体适应度大小和群体的分散程度自动调整遗传控制参数,从而能够在保持群体多样性的同时加快收敛速度,最后得到图像分割的最佳阈值,克服了传统遗传算法的收敛性差、易早熟等问题.在理论分析和仿真数据实验中,与二维OTUS图像分割法和基于基本遗传算法的图像分割法相比,使用该方法得出的阈值范围更加稳定,阈值计算时间有极大的降低,更能满足图像处理的实时性要求.

  17. 一种自适应分裂与合并的运动目标聚类分割算法%An Adaptive Splitting and Merging Clustering Algorithm of the Moving Target Segmentation

    Institute of Scientific and Technical Information of China (English)

    张琨; 王翠荣

    2014-01-01

    For the issue of multiple moving targets’ segmentation in intelligent monitoring system, an adaptive splitting and merging clustering algorithm of the moving target segmentation is proposed. First, it uses the time-domain information for foreground image segmentation through the sample variance background modeling algorithm, thus obtains the foreground image containing multiple moving targets. It defines pixel space connectivity rate and designs a perpendicular split method for the initial cluster adaptive splitting and merging. Without pre-set number of initial cluster, the self-organized iterative clustering segmentation algorithm can complete multiple moving targets segmentation. Experimental results show that the proposed algorithm is suitable for multiple moving targets’ segmentation, and the segmentation results are consistent with the human visual judgment. The use of space connectivity information improves the iterative algorithm convergence speed, thus it has good real-time.%针对智能监控系统中多个运动目标进行图像分割这一问题,该文提出一种自适应分裂与合并的多运动目标聚类分割算法。该算法首先利用视频图像的时域信息,通过样本方差进行背景建模,分割出包含多个运动目标的前景图像。然后定义了像素点的空间连通率,并设计一种利用中垂线分割法,对初始聚类进行自适应分裂与合并。在无需事先设定聚类分割数目的条件下,自组织迭代聚类算法能完成多运动目标的分割。实验结果证明该算法对多运动目标分割效果好,分割结果与人眼视觉的判断一致。利用空间连通信息使得算法迭代收敛速度快,具有良好的实时性。

  18. Efficient segmentation by sparse pixel classification

    DEFF Research Database (Denmark)

    Dam, Erik B; Loog, Marco

    2008-01-01

    Segmentation methods based on pixel classification are powerful but often slow. We introduce two general algorithms, based on sparse classification, for optimizing the computation while still obtaining accurate segmentations. The computational costs of the algorithms are derived......, and they are demonstrated on real 3-D magnetic resonance imaging and 2-D radiograph data. We show that each algorithm is optimal for specific tasks, and that both algorithms allow a speedup of one or more orders of magnitude on typical segmentation tasks....

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

    Science.gov (United States)

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

    2014-10-01

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

  20. Bayberry image segmentation based on homomorphic filtering and K-means clustering algorithm%基于同态滤波和K均值聚类算法的杨梅图像分割

    Institute of Scientific and Technical Information of China (English)

    徐黎明; 吕继东

    2015-01-01

    is the key step for accurate fruit positioning and successful picking. The primary task of fruit identification and picking is to separate fruit from complicated background of branches, trunk and sky by image segmentation. It is hard to accurately segment colorized bayberry image because there are fruits with low brightness or uneven illumination in nature scenes. In this study, RGB (red, green and blue) color space was transformed into HSV (hue, saturation and value) space. After that, the luminance component of image was strengthened by dynamic Butterworth homomorphism filter transfer function. Then, it was restored to RGB color space for colorized image illumination compensation and shadow removal. The bayberry image after shadow removal included red bayberry, green leave and white sky. Each pixel of colorized bayberry image to be segmented was considered as one point of data set X. These pixels were classified into red, green and white. According to the characteristics of the componentsa andb in Lab color space, RGB color space was transformed into CIELAB space. The K-means clustering algorithm was used for image segmentation, and the parameterK was selected as 3. In order to verify the effectiveness of the proposed algorithm, 15 bayberry images were selected from 100 images affected by different degrees of shadow under different growth conditions and uneven illumination conditions. Firstly,in order to prove effectiveness of illumination compensation, theK-means clustering algorithm was used to conduct image segmentation experiments before and after illumination compensation to shadow removal. Secondly, in order to validate segmentation effectiveness of images after illumination compensation based on different methods, this study applied adaptive 2*R-G-B grey threshold andK-means clustering segmentation algorithms to compare their effects of shadow removal. Thirdly, homomorphism filter algorithm was compared with linear enhancement and histogram equalization

  1. Automatic segmentation of choroidal thickness in optical coherence tomography.

    Science.gov (United States)

    Alonso-Caneiro, David; Read, Scott A; Collins, Michael J

    2013-01-01

    The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye's normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

  2. Contrast of Three Image Segmentation Algorithms for Wood Surface Defects%3种不同模型对木材表面缺陷图像分割算法的比较∗

    Institute of Scientific and Technical Information of China (English)

    白雪冰; 许景涛; 宋恩来; 陈凯

    2016-01-01

    木材表面缺陷会严重影响木材的质量和使用价值,因此对木材表面缺陷图像分割的研究有利于提高木材的利用率。本文分别对红皮云杉含有虫眼、活节、死节3种典型木材缺陷的图像采用改进的C-V模型、改进的GVF Snake模型和改进的GAC模型进行分割试验,对3种改进算法的复杂程度、分割时间、分割结果的完整性以及抗噪性进行对比和分析。结果表明,改进的GAC模型算法较为优越,其分割算法简单,运行时间短,缺陷分割效果较好,抗噪性强。而改进的C-V模型算法、改进的GVF Snake模型算法的分割效果和抗噪性最差,不宜作为3种木材表面缺陷图像的分割算法。%Since the surface defect of the wood can seriously affect the quality and the use value of the wood, the research of image segmentation algorithm for wood surface defects can increase the utilization rate of wood. This pa-per respectively research three typical wood defects with wormhole, living scab and dead scab. For segmentation test, the improved C-V model, the improved GVF Snake model and the improved GAC model were used, and the complexity of the three improved algorithms, the time required for segmentation, the integrity of segmentation re-sults, and the noise performance of the algorithm were analyzed through the test. The results showed that the im-proved GAC model was superior to other methods since its segmentation algorithm was simple, its running time was shorter, its defect segmentation results were complete and its noise immunity performance was significant. The im-age segmentation result and noise immunity of the improved C-V model and the improved GVF Snake model were not significant.

  3. Automatic segmentation of the caudate nucleus from human brain MR images.

    Science.gov (United States)

    Xia, Yan; Bettinger, Keith; Shen, Lin; Reiss, Allan L

    2007-04-01

    We describe a knowledge-driven algorithm to automatically delineate the caudate nucleus (CN) region of the human brain from a magnetic resonance (MR) image. Since the lateral ventricles (LVs) are good landmarks for positioning the CN, the algorithm first extracts the LVs, and automatically localizes the CN from this information guided by anatomic knowledge of the structure. The face validity of the algorithm was tested with 55 high-resolution T1-weighted magnetic resonance imaging (MRI) datasets, and segmentation results were overlaid onto the original image data for visual inspection. We further evaluated the algorithm by comparing automated segmentation results to a "gold standard" established by human experts for these 55 MR datasets. Quantitative comparison showed a high intraclass correlation between the algorithm and expert as well as high spatial overlap between the regions-of-interest (ROIs) generated from the two methods. The mean spatial overlap +/- standard deviation (defined by the intersection of the 2 ROIs divided by the union of the 2 ROIs) was equal to 0.873 +/- 0.0234. The algorithm has been incorporated into a public domain software program written in Java and, thus, has the potential to be of broad benefit to neuroimaging investigators interested in basal ganglia anatomy and function.

  4. 基于三维指数灰度熵的快速图像分割算法%Fast image segmentation algorithm based on three-dimensional exponential gray entropy

    Institute of Scientific and Technical Information of China (English)

    张书真; 黄光亚

    2013-01-01

    针对现有阈值分割法通常只考虑图像直方图的统计信息,而忽略了图像目标和背景类内灰度分布的均匀性,提出指数灰度熵分割算法,并推广得到三维指数灰度熵分割算法。给出了一维指数灰度熵阈值法及三维指数灰度熵阈值法的原理,在三维直方图上,将降维处理和优化搜索策略相结合,得到最优分割阈值。理论证明,阈值搜索复杂度由原来的O(L3)降至O(L12)。实验结果表明,与现有的多种阈值法相比,所提算法抗噪性能更强、分割效果更优,且运算时间大为减少。%In view of the existing threshold segmentation methods which usually only consider the statistical information from image histogram, while ignoring the gray distribution uniformity of the image target class and the background class, one-dimensional exponential gray entropy segmentation algorithm is put forward and three-dimensional exponential gray entropy segmentation algorithm is deduced. The principles of one-dimensional exponential gray entropy algorithm and three-dimensional exponential gray entropy algorithm are presented. The optimum segmentation threshold is got by combining dimension reduction and optimal search strategy on the three-dimensional histogram. The search complexity is reduced from O(L3) to O(L1/2) in theory. Experimental results show that, compared with other existing threshold algorithms, the proposed algorithm has better anti-noise performance, segmentation effect and its operation time is reduced greatly.

  5. 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....... The results show clearly an improved segmentation performance for the full polarimetric algorithm compared to single channel approaches....

  6. An interactive segmentation method based on superpixel

    DEFF Research Database (Denmark)

    Yang, Shu; Zhu, Yaping; Wu, Xiaoyu

    2015-01-01

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

  7. Research on Word Segmentation Algorithm of Chinese Agriculture Web Page Besed on Dictionary and Omni-word Segmentation%基于词典和全切分的中文农业网页分词算法的研究

    Institute of Scientific and Technical Information of China (English)

    白涛; 张太红; 吴乃宁

    2014-01-01

    This paper proposes a kind of Chinese word segmentation algorithm besed on dictionary and omni-word segmentation in view of special requirements of agricultural vertical search.Firstly,the algo-rithm segments pretreated web pages preliminarily with mechanical participle based on dictionary,and then calculates probability of omni-word segmentation of unknown string based on Bayesian method,and ascer-tains the most reasonable segmentation through calculating maximum credibility of unknown string,and improves dictionary.Fourteen groups selected randomly from 1 .2 million Chinese agricultural web pages constituted the test set.Result showed that the recall ratio of proposed algorithms was higher than FMM’s and BMM’s,and average of F1 reached 88%.%针对农业垂直搜索中中文分词要求的特殊性,提出一5基于词典和全切分的中文分词算法。该算法首先对经过预处理的网页进行基于词典的机械式切分,对未识别的字串再进行基于贝叶斯(Bayes)方法的全切分概率计算,通过计算字串的最大切分可信度确定最合理的切分,并更新词典。实验从120万张农业中文网页中随机抽取14组生成测试集,测试结果表明,该算法与正向最大匹配算法(FMM)和逆向最大匹配算法(RMM)相比具有更高的召回率,F1测度平均达到88%。

  8. 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.

  9. Exercises in anatomy: cardiac isomerism.

    Science.gov (United States)

    Anderson, Robert H; Sarwark, Anne E; Spicer, Diane E; Backer, Carl L

    2014-01-01

    It is well recognized that the patients with the most complex cardiac malformations are those with so-called visceral heterotaxy. At present, it remains a fact that most investigators segregate these patients on the basis of their splenic anatomy, describing syndromes of so-called asplenia and polysplenia. It has also been known for quite some time, nonetheless, that the morphology of the tracheobronchial tree is usually isomeric in the setting of heterotaxy. And it has been shown that the isomerism found in terms of bronchial arrangement correlates in a better fashion with the cardiac anatomy than does the presence of multiple spleens, or the absence of any splenic tissue. In this exercise in anatomy, we use hearts from the Idriss archive of Lurie Children's Hospital in Chicago to demonstrate the isomeric features found in the hearts obtained from patients known to have had heterotaxy. We first demonstrate the normal arrangements, showing how it is the extent of the pectinate muscles in the atrial appendages relative to the atrioventricular junctions that distinguishes between morphologically right and left atrial chambers. We also show the asymmetry of the normal bronchial tree, and the relationships of the first bronchial branches to the pulmonary arteries supplying the lower lobes of the lungs. We then demonstrate that diagnosis of multiple spleens requires the finding of splenic tissue on either side of the dorsal mesogastrium. Turning to hearts obtained from patients with heterotaxy, we illustrate isomeric right and left atrial appendages. We emphasize that it is only the appendages that are universally isomeric, but point out that other features support the notion of cardiac isomerism. We then show that description also requires a full account of veno-atrial connections, since these can seemingly be mirror-imaged when the arrangement within the heart is one of isomerism of the atrial appendages. We show how failure to recognize the presence of such isomeric

  10. Prototypes-Extraction Spectral Clustering Ensemble Algorithm Applied to Remote Sensing Image Segmentation%应用于遥感图像分割的原型提取谱聚类集成算法

    Institute of Scientific and Technical Information of China (English)

    赵凤; 刘汉强; 范九伦; 潘晓英

    2012-01-01

    Aiming at the huge data amount and pixel complex ownership of remote sensing images,a prototypes-extraction spectral clustering algorithm for remote sensing image segmentation was proposed.Firstly,the generalized fuzzy c-means algorithm was adopted to perform an over-segmentation of the image,and the obtained clustering prototypes were regarded as the representative points of segmentation regions to reduce the data amount of original image.Secondly,the similarity matrix between the representative points was constructed,and then the spectral graph partitioning method was utilized to cluster the representative points.Eventually,based on the clustering result of representative points,the image pixels were reclassified to obtain the final image segmentation results.There are three parameters in the prototypes-extraction spectral clustering algorithm.In order to overcome the parameter sensitivity and inherent randomness of this method,an ensemble strategy was further introduced into the method and its ensemble algorithm is presented.The segmentation experiments on artificial texture and remote sensing images show that this proposed ensemble method behaves well in segmentation performance.%针对遥感图像数据量大、类别归属复杂的特点,提出了一种用于遥感图像分割的原型提取谱聚类算法。该算法首先采用广义模糊c-均值聚类算法对遥感图像进行过分割,将得到的聚类中心作为每个分割区域的代表点;然后,通过构造代表点之间的相似性矩阵,利用谱图划分方法对代表点进行聚类;最后,根据代表点的聚类结果对图像像素点进行重新归类来获得遥感图像的最终分割结果。此算法涉及到3个参数,为了克服算法对于参数的敏感性和内在的随机性,进一步引入集成策略,给出了原型提取谱聚类的集成算法。

  11. 基于感兴趣区域函数优化的静脉图像分割算法%Vein Image Segmentation Algorithm Based on Function Optimization in Regions of Interest

    Institute of Scientific and Technical Information of China (English)

    贾旭; 崔建江; 薛定宇; 潘峰

    2012-01-01

    A near infrared dorsal hand vein image segmentation algorithm is proposed based on function optimization about entropy and gradient in local regions of interest. The noise is removed by applying compressed sensing theory in vein image firstly. Then, the Bandelet transform is used to extract the regions of interest including vein information, and the established function about entropy and gradient is constrained and optimized in these regions so that the vein information and the background can be separated. Finally, the segmentation results in all the regions of interest are fused, and the whole vein image segmentation process is accomplished. The experimental results show that the proposed algorithm makes the acquired segmentation image reserve belter vein features than other segmentation algorithms. In addition, the proposed algorithm has good reference value in the segmentation of finger vein and palm vein images with texture features.%提出一种基于局部感兴趣区域中熵与梯度函数优化的近红外手背静脉图像分割算法,该算法首先基于压缩感知理论对图像进行去噪.其次,通过条带波变换提取存在静脉信息的感兴趣区域,在这些区域中对建立的关于熵和梯度的函数进行约束与优化,实现静脉与背景分离.最后,融合所有区域的分割结果,完成静脉图像的分割.实验表明在处理近红外静脉图像分割问题时,该算法相对其它算法能保留更完整的静脉特征.此外,该算法对于具有纹理特征的指静脉、掌静脉图像的分割具有较好的借鉴价值.

  12. An improved gray level co-occurrence fingerprint image segmentation algorithm%改进灰度共生矩阵的指纹图像分割算法

    Institute of Scientific and Technical Information of China (English)

    黄敏; 刘云坚

    2016-01-01

    针对灰度共生矩阵对指纹图像分割过程中人工选取阈值不精确、繁琐等缺点,提出了一种采用自适应阈值分割的灰度共生矩阵的指纹图像分割算法。首先,用整幅指纹图像的对比度方差值的均值 Mv 作阈值对图像进行初分割;然后,不断调整 Mv ,通过试验验证当指纹区域对比度方差值的均值 Pv 与 Mv 的比值在一个特定的区间(即 Pv/Mv ∈[1.5,2])时,才能获得最好的分割效果,由此获得灰度共生矩阵的自适应分割阈值 Mv ,从而精确地分割出指纹图像的有效区域。试验结果表明,相比于已有的分割算法,该算法在分割错误率和耗时方面均较优,并且分割更准确。%In the process of fingerprint image segmentation,matrix artificial selection of the threshold is not accurate,tedious.A fingerprint image segmentation algorithm is proposed by improved gray level co-occurrence matrix.Firstly,Mv is the mean value of the contrast variance for fingerprint images,Pv is the mean value of the contrast variance between the fingerprint region.Mv is adopted as the image segmentation threshold.Then,Mv is adj usted continuously,when the Pv/Mv is between 1.5 and 2 the best segmentation results is ob-tained through experiment.The adaptive segmentation threshold of gray level co-occurrence matrix is obtained.Experimental results show that this algorithm is superior to the segmen-tation error rate,time consuming and more accurate compared with the existing segmenta-tion algorithm.

  13. Remote Sensing Image Segmentation Algorithm Based on an Improved Multi Granularity Principle%改进的多粒度原理在遥感图像分割算法研究

    Institute of Scientific and Technical Information of China (English)

    金显华; 赵元庆

    2012-01-01

    In view of the traditional image segmentation of remote sensing image due to the computational complex and other reasons, resulting in low resolution image segmentation, clarity is not high at the same time, when the image information in a very large amount of image segmentation, is time consuming and defects. In order to improve image segmentation, this paper proposes an improved multi granularity theory and wavelet algorithm combining image segmentation of remote sensing image. The method firstly uses the wavelet transform to the image histogram of the radian of multiscale wavelet transform, and then uses decomposition operation, granular synthesis technology on the decomposition of the image after synthesis. The experimental results show that the proposed algorithm effectively, improve the segmentation effect, the segmented image edge effect is distinct, proved the feasibility and effectiveness of the proposed algorithm.%针对传统的遥感图像分割算法由于计算复杂等原因,造成图像的分割分辨率低,清晰度不高,当图像中的信息量非常大时,对图像分割非常耗时等问题缺陷,为了有效地分割图像,提出了一种改进的多粒度原理和小波算法相结合的遥感图像分割算法;该方法首先采用小波变换对图像的弧度直方图进行小波多尺度变换,并进行分解操作,然后采用粒度合成技术对分解后的图像进行合成;文中采用的是256×256的SAR图像来进行实验对比,结果表明,提出的算法有效地改善了分割效果,分割出的图像边缘效果明显清晰,证明了该算法的可行性和有效性.

  14. Image Segmentation Algorithm Based on VPRS-PSO Method%基于变精度粗糙集和粒子群的图像分割方法

    Institute of Scientific and Technical Information of China (English)

    张雪峰; 商丽丽

    2011-01-01

    To the problem of the image segmentation using other algorithms with more time-consuming, an algorithm is presented by u-sing the PSD and the variable precision rough set theory. The proposed image segmentation algorithm divides the image according to some rules to find the boundary of the image. Then the boundaies of the sub-images are calculated by using the variable precision rough set model. The best β and the corresponding grey value are obtained by using the the particle swarm optimization, which is taken as the optimal segmentation threshold to segment the image. The eeffeciness of the algorithm is verified by the Matlab simulation to the test images and the simulation results are compared with other related aricles.The result shows that the algorithm can effectively reduce the consumption of lime and the segmentation result is satisfactory.%针对其他算法分割图像耗时较多的问题,结合粒子群优化算法和变精度粗糙集理论提出了一种新的分割算法.利用粗糙集理论将图像按照一定的规则进行划分,找出图像的边界.利用变精度粗糙集理论对图像子块的边界进行计算,利用粒子群优化算法寻找最佳的β及其对应的灰度值,并对图像进行分割.通过对测试图像进行Matlab仿真,验证了算法的效果,并与其他相关文献的仿真结果做了类比,试验表明该算法相对于其他算法可以有效地减少消耗时间,并且分割效果也令人满意.

  15. Market Segmentation of Air Passenger Transport Based on Joint Algorithm of Hierarchy Clustering and FCM%基于层次聚类 FCM 算法的航空客运市场细分

    Institute of Scientific and Technical Information of China (English)

    王悦; 曾小舟; 傅骏

    2015-01-01

    运用层次聚类法和FCM算法,从Kotler四维顾客价值角度构建航空客运市场细分量表,形成施测问卷,获取样本数据。将因子分析与基于层次聚类的FCM算法相结合,获得4类差距明显的子市场,并验证了市场细分的有效性。研究结果表明,基于层次聚类的FCM算法细分航空客运市场能够获得较为满意的结果,也验证了该混合算法的合理性、有效性和可操作性,其中细分量表与基于层次聚类的FCM算法可作为航空客运主体细分市场的依据和方法。%Combining the strengths of the hierarchy clustering method and the fuzzy c -means ( FCM) algorithm, the practi-cal issue of the market segmentation ( MS) of the air passenger transport was analyzed .The market segmentation scale for the air passenger transport was constructed from the Kotler's four-dimensional customer value ( CV) .On the basis of the scale , a ques-tionnaire was formed and the correspondent data were collected .Combining the factor analysis and the joint algorithm of hierarchy clustering and FCM , four segmented markets with significant differences were finally obtained .The mean value analysis and the variance analysis were applied to verify the effectiveness of the result .The results demonstrate that by applying the joint algorithm of hierarchy clustering and FCM , combining Kotler's four-dimensional CV , the market of air passenger transport could be desira-bly segmented.In turn, the results also confirm the feasibility and reasonableness of the joint algorithm .The market segmentation scale and the joint algorithm can be utilized for the MS for the operational subjects in civil aviation .

  16. Learning anatomy enhances spatial ability.

    NARCIS (Netherlands)

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

    2013-01-01

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

  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. OpenCV分水岭算法的改进及其在细胞分割中的应用%Improvement on watershed algorithm of OpenCV and its application in cell image segmentation

    Institute of Scientific and Technical Information of China (English)

    张羽; 徐端全

    2012-01-01

    分水岭算法是一种基于形态学的图像分割算法,能快速准确地确定连通区域的边界.将基于标记的分水岭算法用于细胞图像的分割,较好地解决了粘连细胞的分割问题.在该细胞分割算法的实现过程中,发现了OpenCV分水岭算法实现的缺陷,通过对相关代码的分析,发现该缺陷存在的原因是算法流程中对相邻像素相对关系的描述存在问题.将OpenCV分水岭算法中对相邻像素取差的绝对值,改为对相邻像素取差值,对该算法进行了改进.实验证明,改进后的OpenCV分水岭算法对细胞图像的分割效果明显好于直接使用OpenCV分水岭算法得到的结果.该方法在不影响分割速度的情况下,提高了OpenCV分水岭算法分割的准确度.%Watershed is an image segmentation algorithm based on morphology, which can determine the boundary of connected section efficiently and effectively. Application of marker watershed algorithm in cell image segmentation leads to the solution of adhesion cell segmentation. When implementing this algorithm, a flaw of the watershed algorithm in OpenCV has been found. The cause of this bug is the wrong description of the difference between two adjacent pixels. An improvement of OpenCV watershed was proposed by replacing the absolute difference between adjacent pixels with the difference between them. The experiment shows that the improved OpenCV watershed algorithm has a better segmentation performance than that of original algorithm with same processing speed.

  19. Research on the application of distant preserving level set algorithm in car door lock image segment%距离保持水平集在汽车门锁图像分割中的应用研究

    Institute of Scientific and Technical Information of China (English)

    王瑶; 安伟; 尤丽华

    2015-01-01

    This paper focus on the research of image segment algorithm based on distant preserving level set, which is used in rocker distance measurement in the car door lock assembly detection system, which is based on machine vision. The algorithm regard profile as a sign function, and the zero horizontal contour of a sign function is in correspondence with the actual outline, more over, the algorithm add a internal energy functional to the energy functional, which represents the difference between the two functions. Experiments show that compared with the general image segmentation algorithm based on edge detection, the algorithm can solve the problem of image segmentation in car door lock's rocker outside arc, and improve the accuracy of car door lock auto detection system. Moreover, the algorithm can also be applied to other similar industrial product image segmentation.%针对基于机器视觉的汽车门锁装配尺寸检测系统中摇臂距离的检测问题,研究了距离保持水平集的图像分割算法。该算法是将轮廓表示为一个符号函数,符号函数的零水平面与实际轮廓相对应,并在能量泛函中加入了内部能量泛函,表示两个函数之间的差异。实验表明,与一般的基于边缘检测的图像分割算法相比较,该算法能够很好的解决汽车门锁图像中摇臂外弧线分割问题,提高了汽车门锁检测系统的精度。同时,该算法也可应用到其他类似工业装配件局部图像分割中。

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

    Directory of Open Access Journals (Sweden)

    Yee Kwo

    2011-06-01

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

  1. Meteorological Data Compression Algorithm Based on Time-Series Segmentation%基于时间序列分段的气象数据压缩算法

    Institute of Scientific and Technical Information of China (English)

    程敏; 翁宁泉; 刘庆; 孙刚; 陈小威

    2014-01-01

    It is not only inefficient to use the raw time series of meteorological parameter such as temperature refractive index structure parameter, wind speed and temperature to make short-term prediction, query similarity and classify and cluster time series, but also affects accuracy and reliability of data mining of time series. This article proposes a simple and fast method which based on the election of extrema point and tendency turning point to make the piecewise linear representation of time series. The method can extract the main pattern of series effectively, and reduce the dependency of threshold. It has the characteristic of small cost of computing, efficient and convenient and strong commonality. Then based on that, the experiments on temperature refractive index structure parameter and other kinds of meteorological parameter are implemented and conduct the comparison analysis between the method and another kind of sequence segmentation algorithm. The result shows that the method proposed is capable of reflecting the pattern of time series effectively and accurately.%直接采用风速、温湿压等气象参数原始时间序列对其进行短期预测、相似匹配、分类聚类等数据挖掘工作不但效率低下,而且会影响时间序列数据挖掘的准确性和可靠性。提出了一种简单快速的基于特征点的筛选算法对时间序列进行分段线性表示。对气象参数等时间序列进行实验,并就计算性能和拟合误差与另外一种序列分段算法进行了对比分析,结果表明该方法能有效地提取序列的主要形态,同时降低对于阈值的依赖,具有计算代价小、快速方便、通用性强等特点,在气象数据压缩上具有较好的应用前景。

  2. 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…

  3. Leaf anatomy and photosynthesis

    NARCIS (Netherlands)

    Berghuijs, H.N.C.

    2016-01-01

    Keywords: CO2 diffusion, C3 photosynthesis, mesophyll conductance, mesophyll resistance, re-assimilation, photorespiration, respiration, tomato Herman Nicolaas Cornelis Berghuijs (2016). Leaf anatomy and photosynthesis; unravelling the CO2 diffusion pathway in C3 leaves. PhD thesis. Wageningen Unive

  4. 基于蛙跳算法与Otsu法的图像多阈值分割技术%Multilevel thresholding segmentation based on shuffled frog leaping algorithm and Otsu method

    Institute of Scientific and Technical Information of China (English)

    康杰红; 马苗

    2012-01-01

    为了快速准确地确定多阈值图像分割中的最佳阈值,提出了一种基于蛙跳算法与Otsu法相结合的多阈值图像分割方法.该方法将多阈值求解看作一种多变量的组合求解优化问题,利用多阈值Otsu法设计分割目标函数,将新兴的仿生学优化求解算法——蛙跳算法引入到图像分割技术中,通过蛙跳算法中全局搜索和局部搜索相结合的搜索机制并行求解多个阈值.实验结果表明,该方法与基于人工鱼群算法的图像多阈值分割方法相比,明显提高了图像分割速度和分割质量.%In order to obtain a group of satisfying thresholds in image segmentation quickly and accurately, this paper proposed a method based on shuffled frog leaping (SFL) algorithm and Otsu method for multilevel thresholding image segmentation. The method regarded the group of thresholds as a group of potential solutions to a certain objective function, and employed the extended Otsu method to be the fitness function for SFL algorithm. And then, the powerful searching ability of SFL algorithm was used to locate the thresholds in parallel, which combines the global search in the whole swarm and local searches in subswarms. Experimental results showed that compared with the method based on artificial fish swarm (AFS) algorithm, the suggested method obviously im- proved the performance of image segmentation in speed and quality.

  5. Improved container code segmentation algorithm based on mathematical morphology.%基于数学形态学的集装箱箱号分割改进算法

    Institute of Scientific and Technical Information of China (English)

    谭伟; 方超; 杜建洪

    2011-01-01

    This paper has advanced an improved algorithm based on mathematical morphology and histogram projection. In the locating section, an improved algorithm based on edge detection and mathematical morphology is used to connect the container code area into a whole connecting region,which can fix the size of the morphologic structure adaptively and solve the problem of vertical array of the container characters.In the segmentation section, mathematical morphology is employed to remove noise, and histogram projection is used to segment each character horizontally and vertically. The exit resuits indicate that this algorithm is easy and available with little prior information and the overall segmentation accuracy can achieve 93.33%,which proves the validity of the algorithm.%提出了基于数学形态学和直方图投影的集装箱箱号分割改进算法.在箱号定位阶段,运用基于边缘检测和数学形态学的改进算法,能自适应确定形态学结构元素的大小,将箱号区域连通成一个区域,并能解决集装箱文字纵向排列的问题.字符分割阶段用数学形态学方法消除干扰边缘和噪声,通过投影直方图法完成行与列分割.实验表明,该算法简单可行,只需较少的集装箱先验信息,并且整箱分割正确率达到93.33%,证明了算法的有效性.

  6. 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.

    2017-01-01

    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. PMID:28186181

  7. 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.

  8. Segmentation of anatomical branching structures based on texture features and conditional random field

    Science.gov (United States)

    Nuzhnaya, Tatyana; Bakic, Predrag; Kontos, Despina; Megalooikonomou, Vasileios; Ling, Haibin

    2012-02-01

    This work is a part of our ongoing study aimed at understanding a relation between the topology of anatomical branching structures with the underlying image texture. Morphological variability of the breast ductal network is associated with subsequent development of abnormalities in patients with nipple discharge such as papilloma, breast cancer and atypia. In this work, we investigate complex dependence among ductal components to perform segmentation, the first step for analyzing topology of ductal lobes. Our automated framework is based on incorporating a conditional random field with texture descriptors of skewness, coarseness, contrast, energy and fractal dimension. These features are selected to capture the architectural variability of the enhanced ducts by encoding spatial variations between pixel patches in galactographic image. The segmentation algorithm was applied to a dataset of 20 x-ray galactograms obtained at the Hospital of the University of Pennsylvania. We compared the performance of the proposed approach with fully and semi automated segmentation algorithms based on neural network classification, fuzzy-connectedness, vesselness filter and graph cuts. Global consistency error and confusion matrix analysis were used as accuracy measurements. For the proposed approach, the true positive rate was higher and the false negative rate was significantly lower compared to other fully automated methods. This indicates that segmentation based on CRF incorporated with texture descriptors has potential to efficiently support the analysis of complex topology of the ducts and aid in development of realistic breast anatomy phantoms.

  9. Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.

    Directory of Open Access Journals (Sweden)

    Xiaoying Tang

    Full Text Available This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.

  10. Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.

    Science.gov (United States)

    Tang, Xiaoying; Oishi, Kenichi; Faria, Andreia V; Hillis, Argye E; Albert, Marilyn S; Mori, Susumu; Miller, Michael I

    2013-01-01

    This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.

  11. 带肝中静脉的活体右半肝移植供者Ⅳ段肝静脉分型对术后残肝淤血和再生的影响%The effect of segment Ⅳ hepatic vein's anatomy on remnant liver congestion and regeneration in right lobe liver graft donors with inclusion of the MHV

    Institute of Scientific and Technical Information of China (English)

    蒋文涛; 马楠; 王洪海; 张骊; 郭庆军; 潘澄; 邓永林; 郑虹; 朱志军

    2013-01-01

    Objective To investigate the effect of segment Ⅳ hepatic vein's type on the early remnant liver congestion and regeneration in right lobe living-related liver graft donors (LDLT) with the inclusion of middle hepatic vein (MHV).Methods Between October 2008 and April 2010,44 LDLT with MHV were performed.According to the type of Nakamura,we classified the segment Ⅳ hepatic vein by means of IQQA-MSCT and verified in operartion.We measured the volume of remnant liver by means of IQQA-MSCT and judged the congestion of segment Ⅳ through postoperative CT scan.Results IQQAMSCT was an effective method to construct and sort segment Ⅳ hepatic vein,which was verified by operartion.The ratio of serious segment Ⅳ congestion was 3.8% in type Ⅰ,40.0% in type Ⅱ,37.5% in type Ⅲ,and the difference was significant (x2 =9.004,P =0.007).Two weeks post operation,the volume of segments Ⅰ-Ⅲ in type Ⅰ was smaller than in type Ⅱ (F =7.977,P =0.01) and type Ⅲ (F =7.977,P =0.032),the volume of segment Ⅳ in type Ⅰ was bigger than in type Ⅱ (F =6.541,P =0.005) and type Ⅲ (F =6.541,P =0.014) conversely.The regeneration rate of segment Ⅳ in type Ⅰ was bigger than in type Ⅱ (F =4.14,P =0.027) and type Ⅲ (F =4.14,P =0.04),on the contrary,the regeneration rate of segments Ⅰ-Ⅲ in type Ⅰ was smaller than in in type Ⅱ (F =5.577,P =0.005) and type Ⅲ (F =5.577,P =0.047).But the regeneration rate of remnant liver was not different between the three groups (F =1.831,P =0.173).Conclusions IQQA-MSCT was an effective method to evaluate the donor in LDLT.The type of segment Ⅳ hepatic vein affected the remnant liver's congestion and regeneration.The segment Ⅳ hepatic vein's anatomy was significantly related with the postoperative congestion and regeneration of the remnant liver,which was compensated by the regeneration of segments Ⅰ-Ⅲ.%目的 了解带肝中静脉活体右半肝移植供者Ⅳ段肝静脉分型对术后残肝淤血

  12. Chinese Word Segmentation Cognitive Model Based on Maximum Likelihood Optimization EM Algorithm%极大似然优化EM算法的汉语分词认知模型

    Institute of Scientific and Technical Information of China (English)

    赵越; 李红

    2016-01-01

    针对标准EM算法在汉语分词的应用中还存在收敛性能不好、分词准确性不高的问题,本文提出了一种基于极大似然估计规则优化EM算法的汉语分词认知模型,首先使用当前词的概率值计算每个可能切分的可能性,对切分可能性进行“归一化”处理,并对每种切分进行词计数,然后针对标准EM算法得到的估计值只能保证收敛到似然函数的一个稳定点,并不能使其保证收敛到全局最大值点或者局部最大值点的问题,采用极大似然估计规则对其进行优化,从而可以使用非线性最优化中的有效方法进行求解达到加速收敛的目的。仿真试验结果表明,本文提出的基于极大似然估计规则优化EM算法的汉语分词认知模型收敛性能更好,且在汉语分词的精确性较高。%In view of bad convergence and inaccurate word segmentation of standard EM algorithm in Chinese words segmentation, this paper put forward a cognitive model based on optimized EM algorithm by maximum likelihood estimation rule. Firstly, it uses the probability of current word to calculate the possibility of each possible segmentation and normalize them. Each segmentation is counted by words. Standard EM algorithm cannot make sure converging to a stable point of likelihood function, and converging to a global or local maximum point. Therefore, the maximum likelihood estimation rule is adopted to optimize it so as to use an effective method in nonlinear optimization and accelerate the convergence. the simulation experiments show that the optimized EM algorithm by maximum likelihood estimation rule has better convergence performance in the Chinese words cognitive model and more accurate in the words segmentation.

  13. Optimally segmented magnetic structures

    DEFF Research Database (Denmark)

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

    ], or are applicable only to analytically solvable geometries[4]. In addition, some questions remained fundamentally unanswered, such as how to segment a given design into N uniformly magnetized pieces.Our method calculates the globally optimal shape and magnetization direction of each segment inside a certain......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...... designarea with an optional constraint on the total amount of magnetic material. The method can be applied to any objective functional which is linear respect to the field, and with any combination of linear materials. Being based on an analytical-optimization approach, the algorithm is not computationally...

  14. 基于改进遗传算法的图像分割技术研究%Research on image segmentation based on Improved Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    李丽丽; 曹永军; 陈再励

    2016-01-01

    At present,in the image segmentation with many types of segmentation method, which used the most common is to detect edges is based segmentation method based on region segmentation segmentation method based on the two,in the process of its application in practice also formed the corresponding threshold value type and region tracking type and edge detection etc.several main types.Based on this.In this paper, according to the threshold value segmentation method are introduced,and demonstrate the validity of the experiment by.%目前,在图像分割中有着众多类型的分割方法,其中应用最为常见的是以边缘检测为基础的分割法和基于区域分割基础上的分割法两种,在其实践应用过程中也形成了相应的阂值型、区域跟踪型以及边缘检测型等几种主要类型方法。在此基础上,本文针对阂值型分割方法进行了改进,并基于实验对其有效性进行了论证。

  15. Automated segmentation of cardiac visceral fat in low-dose non-contrast chest CT images

    Science.gov (United States)

    Xie, Yiting; Liang, Mingzhu; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

    2015-03-01

    Cardiac visceral fat was segmented from low-dose non-contrast chest CT images using a fully automated method. Cardiac visceral fat is defined as the fatty tissues surrounding the heart region, enclosed by the lungs and posterior to the sternum. It is measured by constraining the heart region with an Anatomy Label Map that contains robust segmentations of the lungs and other major organs and estimating the fatty tissue within this region. The algorithm was evaluated on 124 low-dose and 223 standard-dose non-contrast chest CT scans from two public datasets. Based on visual inspection, 343 cases had good cardiac visceral fat segmentation. For quantitative evaluation, manual markings of cardiac visceral fat regions were made in 3 image slices for 45 low-dose scans and the Dice similarity coefficient (DSC) was computed. The automated algorithm achieved an average DSC of 0.93. Cardiac visceral fat volume (CVFV), heart region volume (HRV) and their ratio were computed for each case. The correlation between cardiac visceral fat measurement and coronary artery and aortic calcification was also evaluated. Results indicated the automated algorithm for measuring cardiac visceral fat volume may be an alternative method to the traditional manual assessment of thoracic region fat content in the assessment of cardiovascular disease risk.

  16. 室内惯性/视觉组合导航地面图像分割算法%Floor segmentation algorithm for indoor vision/inertial integrated navigation

    Institute of Scientific and Technical Information of China (English)

    汪剑鸣; 王曦; 王胜蓓; 李士心; 冷宇

    2011-01-01

    Navigation is a key technology for autonomous robots, which makes them movable in an unknown environment. To tackle the difficulty of building indoor navigation map for inertial navigation systems, a new map building method for inertial/visual navigation is proposed. By limiting robot's movement within the floor areas, the global navigation map is generated from a bird-view image. An algorithm of automatic floor segmentation is proposed, which employs principal component analysis to implement dimension reduction for local color features and adopts clustering analysis to realize floor segmentation automatically. Finally, an indoor bird-view image database is built to evaluate the algorithm. The algorithm gets the worst performance, 75% averaged accurate segmentation rate, on the fourth group images because illumination reflection is found in the images. Average accurate segmentation rates on other groups are around 85%. Thus, the preprocessing algorithms, such as illumination refection detection, can help to improve the performance of the algorithm.%导航技术是机器人实现自主移动的关键技术之一.针对惯性导航创建全局导航地图困难等问题,提出一种新的惯性/视觉组合导航室内全局地图创建方法.规定机器人只能在地面区域中移动,并利用室内俯视图像建立全局地图,提出一种俯视图像地面区域的自动分割算法.首先,利用主元分析算法对图像的局部颜色特征进行降维;其次,利用聚类算法对地面区域进行自动分割;最后,建立了室内俯视图像数据库并对算法的性能进行了验证.由于第四组图像中包含反光区域,算法的分割结果较差,平均正确分辨率为75%.算法在其他各组的平均正确分割率为85%左右.为提高算法的性能,可在应用本算法前利用反光区域检测算法对图像进行预处理.

  17. Method of image segmentation based on fast global K-means algorithm and region merging.%融合快速全局K-means与区域合并的图像分割

    Institute of Scientific and Technical Information of China (English)

    王虹; 覃刘波

    2012-01-01

    提出一种融合快速全局K-means与区域合并的图像分割方法.该方法利用中值滤波方法对图像去噪;运用快速全局K-means算法对图像的颜色空间进行聚类分析;结合区域合并准则,对初始分割合并得到最终的分割结果.实验表明,与同类算法比较,该方法的分割结果在图像细节方面能够很好地满足人的主观视觉.%In this paper, a method of image segmentation is presented, which based on fast global K-means and region merging. Medial filter is used to remove the noise of target image. The initial segmented result is obtained by using fast global K-means clustering algorithm in the color space. A region merging strategy is used to merge the initial regions with the goal of forming the final segmentation result. The simulation results indicate that compared with other methods, the segmentation result is well consistent with human perception, especially in image details.

  18. An Improved Medical Image Segmentation Algorithm Based on Visual Perception Model%一种基于视觉感知的复合医学图像分割算法

    Institute of Scientific and Technical Information of China (English)

    刘再涛; 魏本征; 柳澄

    2011-01-01

    To improve the visual effect of the medical image segmentation, an improved medical image segmentation algorithm was presented based on the visual perception delaminated characters. Based the character of medical image, the local region features were described firstly, and then the local texture feature was designed consistently with visual perception of different image region. The Fuzzy-ART neural network as pixels classifier was used to segment the medical image based on the layer feature functions. The experiment results showed that this method effectively solved the local information and distribution of image local features, and achieved a good segmentation and visual effect.%为提高医学图像分割的视觉效果,依据人类视觉感知的分层特性,提出了一种新的复合医学图像分割方法.该方法通过提取医学图像的底层特征,利用Fuzzy-ART神经网络作为像素的分类器,对医学图像进行连续两次分割.实验结果表明,该医学图像分割方法能有效地解决局部信息与整体分布边缘淡化等相关问题,达到良好的分割视觉效果.

  19. 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...

  20. Adaptive Segmentation for Scientific Databases

    NARCIS (Netherlands)

    Ivanova, M.G.; Kersten, M.L.; Nes, N.J.

    2008-01-01

    In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate)

  1. Adaptive segmentation for scientific databases

    NARCIS (Netherlands)

    Ivanova, M.; Kersten, M.L.; Nes, N.

    2008-01-01

    In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate)

  2. 基于改进谱聚类与粒子群优化的图像分割算法%Image Segmentation Algorithm Based on Improved Spectral Clustering and Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

      本文提出了一种基于改进谱聚类与粒子群优化的图像分割算法。该算法利用双树复小波变换系数,求得能量均值构造相似性矩阵,充分利用了待聚类数据所包含的空间邻近信息和特征相似性信息。在谱映射的过程中,采用了Nystr迸m逼近策略,降低了谱聚类算法的复杂度和内存消耗,然后在进行K均值聚类时使用粒子群优化算法。最后,通过对医学图像和遥感图像分割验证了新算法的有效性。%A image segmentation algorithm based on improved spectral clustering and particle swarm optimization is proposed .Similarity matrix is constructed by the mean of dual -tree complex wavelet transform coefficients in this dissertation so as to make full use of the spatial adjacency information and feature similarity information included in the data .To efficiently apply the algorithm to image segmentation ,Nyström approximation strategy is used in the course of spectral mapping to reduce the computation complexity and memory consumption .And then we tentatively adopt particle swarm optimization algorithm to optimize the K - means clustering in the spectral clustering algorithm .Experimental results on medical images and remote sensing images verify the validity of the proposed algorithm .

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

    Science.gov (United States)

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

    2007-09-01

    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 assessment of the response to therapy and in the planning of the delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children's Hospital. False-negative error rates of less than 12% were obtained in eight of 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist.

  4. The Automatic Image Segmentation Method Based on Fast FCM and Random Walk Algorithm%基于快速FCM与随机游走算法的图像自动分割方法

    Institute of Scientific and Technical Information of China (English)

    许健才; 张良均; 余燕团

    2016-01-01

    在图像分割中,针对 FCM 算法存在聚类数目需要预先给定、收敛速度慢等缺点,本文把快速模糊 C 均值聚类算法和随机游走算法相结合,具体方法为先采用快速模糊 C 均值聚类算法对图像进行预分割,以便获得聚类中心的位置,然后将该中心作为随机游走的种子点,再进行图像分割,实验结果得到了较为满意的预期效果,证明该方法是可行的。本文的研究为快速 FCM 实现自适应性和开发图形图像预处理系统提供了技术支持与理论依据。%As far as image segmentation, the defeat of the number of clusters for FCM algorithm is reeded to be improued. In this paper, the fast fuzzy C-means clustering and random walk algorithm are combined to solve the problem of image segmentation. Firstly, the fast FCM for image pre-segmentation to obtain the number of clusters and cluster central location as the seed points of random walk firstly. Then, for image segmentation, experimental results show that this method is feasible, and get a more satisfactory desired purpose. Results of this study achieve self-adaptive and fast FCM develop graphical image preprocessing system provides technical support and theoretical basis.

  5. Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation.

    Science.gov (United States)

    Peters, J; Ecabert, O; Meyer, C; Kneser, R; Weese, J

    2010-02-01

    Segmentation of medical images can be achieved with the help of model-based algorithms. Reliable boundary detection is a crucial component to obtain robust and accurate segmentation results and to enable full automation. This is especially important if the anatomy being segmented is too variable to initialize a mean shape model such that all surface regions are close to the desired contours. Several boundary detection algorithms are widely used in the literature. Most use some trained image appearance model to characterize and detect the desired boundaries. Although parameters of the boundary detection can vary over the model surface and are trained on images, their performance (i.e., accuracy and reliability of boundary detection) can only be assessed as an integral part of the entire segmentation algorithm. In particular, assessment of boundary detection cannot be done locally and independently on model parameterization and internal energies controlling geometric model properties. In this paper, we propose a new method for the local assessment of boundary detection called Simulated Search. This method takes any boundary detection function and evaluates its performance for a single model landmark in terms of an estimated geometric boundary detection error. In consequence, boundary detection can be optimized per landmark during model training. We demonstrate the success of the method for cardiac image segmentation. In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme. We also illustrate how the Simulated Search can be used to identify suitable classes of features when addressing a new segmentation task. Finally, we show that the Simulated Search enables multi-modal heart segmentation using a single algorithmic framework. On computed tomography and magnetic resonance images, average segmentation errors (surface-to-surface distances) for the four chambers and

  6. Authenticity in Anatomy Art.

    Science.gov (United States)

    Adkins, Jessica

    2017-01-12

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

  7. [Anatomy: the bodily order].

    Science.gov (United States)

    Kruse, Maria Henriqueta Luce

    2004-01-01

    In this essay I try to show the source of the knowledge that determines a certain view that the healthcare team, particularly the nursing team, has developed on the body, especially the sick body. I understand that this knowledge determines ways of caring for the hospitalized bodies. Based on texts by Foucault I analyze the subject of Anatomy. I present a brief history of its construction as a field of knowledge since Versalius until today, when we find plastinated and digitized bodies. I highlight the cadaver as the student's first contact with a human body and observe that the illustrations contained in Anatomy books privilege male and white bodies. I characterize the body as a radically historical invention and observe that we are culturally trained to perceive it, in an organized way, from given viewpoints and by using certain lenses.

  8. 基于Matlab的二维Renyi熵图像分割研究%Research on Image Segmentation Algorithm Based on the Two-dimension Entropy

    Institute of Scientific and Technical Information of China (English)

    刘会英; 徐才云; 于进杰

    2015-01-01

    Image segmentation is the base of image target recognition.The image segmentation method based on Two-dimension entropy is studied in this paper.The method is realized in Matlab.Image segmentation experiments are carried on with different parameters.Experiment results shows that the segmentation method can combine the spatial information of image which lead to a good segmentation precision.%阈值分割是准确实现图像目标识别的前提。文章对基于二维Renyi熵的图像分割方法进行研究,给出了相应算法的Matlab实现,并对不同参数条件下的图像分割效果进行了实验分析。实验结果表明:该分割方法充分考虑图像像素的空域信息,具有良好的分割精度。

  9. 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.)

  10. [Anatomy of the skull].

    Science.gov (United States)

    Pásztor, Emil

    2010-01-01

    The anatomy of the human body based on a special teleological system is one of the greatest miracles of the world. The skull's primary function is the defence of the brain, so every alteration or disease of the brain results in some alteration of the skull. This analogy is to be identified even in the human embryo. Proportions of the 22 bones constituting the skull and of sizes of sutures are not only the result of the phylogeny, but those of the ontogeny as well. E.g. the age of the skeletons in archaeological findings could be identified according to these facts. Present paper outlines the ontogeny and development of the tissues of the skull, of the structure of the bone-tissue, of the changes of the size of the skull and of its parts during the different periods of human life, reflecting to the aesthetics of the skull as well. "Only the human scull can give me an impression of beauty. In spite of all genetical colseness, a skull of a chimpanzee cannot impress me aesthetically"--author confesses. In the second part of the treatise those authors are listed, who contributed to the perfection of our knowledge regarding the skull. First of all the great founder of modern anatomy, Andreas Vesalius, then Pierre Paul Broca, Jacob Benignus Winslow are mentioned here. The most important Hungarian contributors were as follow: Sámuel Rácz, Pál Bugát or--the former assistant of Broca--Aurél Török. A widely used tool for measurement of the size of the skull, the craniometer was invented by the latter. The members of the family Lenhossék have had also important results in this field of research, while descriptive anatomy of the skull was completed by microsopical anatomy thanks the activity of Géza Mihálkovits.

  11. Orbita - Anatomy, development and deformities; Orbita - Anatomie, Entwicklung und Fehlbildungen

    Energy Technology Data Exchange (ETDEWEB)

    Hartmann, K.M.; Reith, W. [Universitaetsklinikum des Saarlandes, Klinik fuer Diagnostische und Interventionelle Neuroradiologie, Homburg/Saar (Germany); Golinski, M. [Universitaetsklinikum des Saarlandes, Homburg/Saar (Germany); Schroeder, A.C. [Universitaetsklinikum des Saarlandes, Klinik fuer Augenheilkunde, Homburg/Saar (Germany)

    2008-12-15

    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.) [German] Die Entwicklung der Strukturen der menschlichen Orbita ist sehr komplex. Ihre Kenntnis erleichtert jedoch das Verstaendnis von Anatomie und Fehlbildungen. In dieser Uebersicht wird zunaechst auf die embryonale Entwicklung eingegangen, bevor die ''normale'' radiologische Anatomie bei verschiedenen Untersuchungstechniken und die haeufigsten Fehlbildungen thematisiert werden. (orig.)

  12. 基于直方图预处理与BF算法的含噪图像分割%Noise-polluted image segmentation based on histogram preprocessing and BF algorithm

    Institute of Scientific and Technical Information of China (English)

    柳新妮; 马苗

    2014-01-01

    Image noise may have a direct influence on the quality of image segmentation. In order to distinguish targets from noise-polluted image quickly and accurately, this paper proposes a method based on histogram preprocessing and BF algorithm for noisy image segmentation. In this method, discrete wavelet transform is used to suppress the noise in the image firstly. Secondly, the histogram feature of the denoised image is analyzed to shrink the distribution range of the optimal threshold. Then, two-dimensional Otsu is selected as the segmentation objective function, and bacterial foraging algorithm is employed to find the optimal threshold in parallel. Experimental results show that this method performs better than some other methods based on swarm intelligence like genetic algorithm, artificial fish swarm algorithm as far as convergence speed, stability and segmentation effect are concerned.%图像中的噪声会直接影响图像分割质量,为快速、准确地识别含噪图像中的目标,提出一种基于直方图预处理与BF算法的含噪图像分割方法。该方法通过小波变换抑制图像中的噪声,分析增强图像的直方图特点以缩小分割阈值的分布范围,以二维最大类间方差为原则设计分割目标函数,利用BF算法快速搜索最优分割阈值。实验结果表明,该方法在收敛速度、稳定性和分割效果三个方面均优于基于遗传算法、人工鱼群算法等其他群体智能的分割方法。

  13. Segmentation: Identification of consumer segments

    DEFF Research Database (Denmark)

    Høg, Esben

    2005-01-01

    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...... and analysed possible segments in the market. Results show that the statistical model used identified two segments - a segment of so-called "fish lovers" and another segment called "traditionalists". The "fish lovers" are very fond of eating fish and they actually prefer fish to other dishes...

  14. 对数似然图像分割的快速主动轮廓跟踪算法%Fast active contour tracking algorithm based on log-likelihood image segmentation

    Institute of Scientific and Technical Information of China (English)

    杨华; 陈善静; 曾凯; 张红

    2012-01-01

    针对跟踪目标尺度变化问题,提出了基于灰度对数似然图像分割的快速主动轮廓跟踪算法.改进的主动轮廓跟踪算法将根据以目标与背景的颜色差异而建立的对数似然图对图像进行阈值分割和数学形态学处理,再将Kalman滤波器结合到主动轮廓跟踪算法进行目标跟踪.改进的主动轮廓跟踪算法对目标分割准确,轮廓特征显著,跟踪效果稳定,算法能很好地适应跟踪目标尺度变化.通过Kalman滤波器对目标位置点的预测减少了主动轮廓跟踪算法收敛的迭代次数,使算法的运算效率提高了33%左右.%A fast active contour tracking(ACT) algorithm based on log-likelihood image segmentation has been proposed to solve the scale change problem in the process of target tracking. The algorithm adopts the log-likelihood image segmentation method, which segments images according to their log-likelihood images built based on the color difference between target and background, and the mathematical morphology method, and tracks the target with conventional ACT algorithm combined with Kalman filter. It tracks the target precisely with distinct contour features and stable tracking performance, and can well adapt to the target scale change. The Kalman filter adopted reduces the number of iterations for algorithm convergence through its forecast of the target position, and thus the fast ACT algorithm is about 33% more efficient than the conventional one.

  15. Algorithm of Segmentation of the Watershed for Brain MRI Images is Improved by Anisotropic Diffusion PDE%各向异性PDE改进分水岭脑MRI图像分割算法

    Institute of Scientific and Technical Information of China (English)

    苗加庆

    2015-01-01

    In this paper discusses principles of the algorithm of the anisotropic diffusion PDE (partial differential equation) noise reduction. An image segmentation algorithm proposed of the anisotropic diffusion PDE noise reduction and watershed algorithm combining. First, that original image reduces noise by the anisotropic diffusion PDE; secondly, that use algorithms of morphological processing image of noise reduction after; finally, that extracted image boundary by morphological knowledge. We use geometric features of the image, and remove non-target area, and using watershed transformation segmentation of the image, and verify the advantages of the method by MRI image of brain. The results further validate the feasibility.%详细论述了各向异性扩散的PDE(偏微分方程)降噪算法原理,提出一种各向异性扩散的PDE降噪和分水岭算法相结合的脑MRI医学图像分割算法。采用各向异性扩散的PDE降低原始图像噪声,然后利用形态学算法对降噪后的图像进行形态学处理,通过形态学知识提取图像边界。利用图像的几何特征,去除非目标区域,再采用分水岭变换进行图像分割,并通过脑MRI图像验证了此方法的优势。实验结果进一步验证了其可行性。

  16. Image Segmentation with Multi-Threshold of Gray-Level & Gradient-Magnitude Entropy Based on Genetic Algorithm%基于遗传算法的灰度-梯度熵多阈值图像分割

    Institute of Scientific and Technical Information of China (English)

    贺建峰; 符增; 易三莉; 相艳; 崔锐

    2015-01-01

    Due to considering the gray level spatial distribution information, some image segmentation technologies based on entropy threshold can enhance the thresholding segmentation performance. However, they still cannot dis-tinguish image edges and noise well. Even though GLGM (gray-level&gradient-magnitude) entropy can effectively solve the problem, it cannot segment effectively multi-objective and complex image. So, this paper proposes image segmentation with multi-threshold of GLGM entropy based on genetic algorithm. In the proposed method, integral figure is introduced in order to make threshold searching dimension from original O(9 ´ L) to O(L) , and the single threshold segmentation of GLGM entropy is further extended to multi-threshold segmentation. Lastly, the real-code-GA is used to search the best thresholds. The simulation results show that this method can be effectively applied for the multi-threshold segmentation of complex images.%一些基于熵的阈值图像分割技术考虑了空间信息,从而能够提高阈值分割的性能,但是仍然不能较好地区分边缘和噪声。尽管灰度-梯度(gray-level & gradient-magnitude,GLGM)熵算法能有效地解决以上问题,但是针对多目标和复杂图像却不能有效地分割。为此,提出了一种基于遗传算法(genetic algorithm,GA)的GLGM熵多阈值快速分割方法。该方法应用积分图思想将GLGM熵算法阈值搜索空间从O(9´ L)降到O(L),并将GLGM熵算法从单阈值拓展到多阈值。最后应用基于实数编码的遗传算法搜索GLGM熵多阈值的最佳阈值。仿真结果表明,该方法能够实现图像的快速多阈值分割,适合复杂图像分割。

  17. Neuro-Fuzzy Phasing of Segmented Mirrors

    Science.gov (United States)

    Olivier, Philip D.

    1999-01-01

    A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.

  18. Segmentation of MRI Volume Data Based on Clustering Method

    Directory of Open Access Journals (Sweden)

    Ji Dongsheng

    2016-01-01

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

  19. 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.

  20. 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,…

  1. Automatic Blind Syllable Segmentation for Continuous Speech

    OpenAIRE

    Villing, Rudi; Timoney, Joseph; Ward, Tomas

    2004-01-01

    In this paper a simple practical method for blind segmentation of continuous speech into its constituent syllables is presented. This technique which uses amplitude onset velocity and coarse spectral makeup to identify syllable boundaries is tested on a corpus of continuous speech and compared with an established segmentation algorithm. The results show substantial performance benefit using the proposed algorithm.

  2. 结合粒子群算法优化归一割的图像阈值分割方法%Image threshold segmentation approach of normalized cut and particle swarm optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    任爱红

    2012-01-01

    In order to get the optimal threshold in image segmentation quickly .based on the graph theory, gray-scale similar matrix takes the place of pixel-level weight matrix, normalized cut criterion is regarded as the optimization function. Using particle swarm optimization algorithm to find the best threshold in gray-scale space. Experiments show that the method is not only less computational costs, but also get a satisfactory segmentation result. The thresholds is more stable and consume less time greatly and better satisfies the request of real-time processing in image segmentation by using this new method.%为了快速得到图像分割的最佳阈值,依据图论知识,利用灰度级相似矩阵代替像素级权值矩阵,将归一化切割准则作为优化函数.利用粒子群优化算法代替穷举法优化归一化划分准则,提出粒子群算法优化归一割的图像阈值分割方法.实验表明在分割性能上有较大的提高,在分割速度上也有较大的改进,能够满足实时性要求.

  3. A Method of Compressed Domain Video Shot Segmentation Based on DCE Algorithm%基于DCE算法的压缩域视频镜头分割方法

    Institute of Scientific and Technical Information of China (English)

    黄永锋; 熊泽东; 王绍宇

    2012-01-01

    Shot segmentation is structural base for video retrieval. An efficient video shot segmentation method is proposed. Firstly, the features are extracted from I frame of video stream in the compressed domain, and the curve of feature information is drawn. Then, the discrete curve evolution (DCE) algorithm is used to evolved the curve. Finally, video shots are segmented and key frames are extracted by the key points of the curve. The experimental results show that the method is efficient and robust, because it takes fully into account the timing features of video encoding.%镜头分割是视频检索的结构化基础,为此提出一种高效的视频镜头分割方法.首先,在压缩域中提取视频流中Ⅰ帧携带的特征信息,并生成帧间特征分布曲线;然后,利用离散曲线演化(DCE)算法对预处理过的曲线进行分析与演进;最后,分割出视频镜头并提取关键帧,试验结果表明:该方法充分考虑了视频编码的时序特点,具有较好的分割效果,鲁棒性强.

  4. Data Condensation Based Spectral Clustering Algorithm for Medical Image Segmentation%医学图像分割中基于数据浓缩的谱聚类算法

    Institute of Scientific and Technical Information of China (English)

    丁阳; 钱鹏江

    2012-01-01

    基于传统Parzen窗密度估计函数的均值漂移谱聚类算法的时间复杂度不低于O(N2),不适合医学图像分割的实际需求.为此,通过压缩集密度估计和吸引盆均匀抽样两重数据浓缩策略以降低原MSSC的高时间开销问题,从而提出新的基于数据浓缩的谱聚类算法.实验结果表明,该算法能有效降低时间开销,较好地适应医学图像分割的要求.%The time complexity of the Parzen Window(PW) based Mean Shift Spectral Clustering(MSSC) algorithm is not less than OQf), which means that it is impractical for medical image segmentation. In is paper, the problem of heavy time cost of original MSSC is solved by using two strategies of data condensation: reduced set density estimator and random sampling from every attraction basin, and the novel Data Condensation Based Spectral Clustering(DCBSC) algorithm is proposed. Compared with MSSC, the time cost of DCBSC is decreased effectively, and the practicability of DCBSC for medical image segmentation is improved accordingly.

  5. 基于 C4.5算法的民航客户价值细分研究%Customer Value Segmentation Based on C4 .5 Algorithm in Aviation Market

    Institute of Scientific and Technical Information of China (English)

    张劲松; 江波

    2014-01-01

    With the development of the air-passenger transport market ,customer segmentation plays an in-creasingly important role in the marketing of airlines .In view of the airport passenger behavior data ,this paper uses C4 .5 decision tree of data mining for market segmentation and obtains the decision tree of cus-tomer value .In addition ,it also analyzes the characteristics of value customers for airlines based on the e-valuation to classification results of decision tree .At last ,this paper contrastively analyzes the frequently used classification algorithm with C4 .5 and indicates that C4 .5 algorithm can produce the excellent effect in airline customer value segmentation .%随着民航旅客运输的发展,客户细分在航空公司市场营销中发挥着越来越重要的作用。针对调研的机场候机旅客行为数据,采用数据挖掘中的决策树C4.5算法对民航客户进行价值细分。在生成的旅客价值细分决策树的基础上,对决策树分类结果进行了评价,并分析了航空公司价值旅客的主要特征。通过与常用的数据分类算法的综合对比分析,表明C4.5算法在民航客户价值细分中具有相对良好的分类效果。

  6. 一种新的基于双层PCNN的自适应图像分割算法%New adaptive algorithm for image segmentation using the dual-level PCNN model

    Institute of Scientific and Technical Information of China (English)

    严春满; 郭宝龙; 马义德; 张旭

    2011-01-01

    A novel adaptive algorithm for image segmentation based on the dual-level pulse coupled neural networks(PCNN) is proposed.For the dual-level PCNN,the first level is based on the simplified PCNN model to obtain the region seeds;the next level adopts the region growing strategy,and recruits the pixels which have similar gray level to the seeds to achieve the growth of the regions.The sensitive parameters of the PCNN can be tuned adaptively,which can overcome the limitation of the parameter setting.Moreover,the region growing strategy strengthens the region characteristics of PCNN.Experimental results show that the proposed algorithm can improve the region connectivity and the edge regularity of the segmented image,and the advantages of PCNN for image segmentation are developed.%提出一种新的基于双层脉冲耦合神经网络(PCNN)的自适应图像分割算法。双层PCNN的前级以简化PCNN模型为基础,获得区域生长的种子;后级采用区域生长机制,征募区域内灰度相似像素,完成前级种子的生长。新算法PCNN的关键参数可自适应更新,避免了传统PCNN参数设置难的问题;区域生长机制强化了PCNN的区域特性。实验结果表明,新算法所得分割图像的区域连通性及边缘规整性得到进一步提高,发挥了PCNN应用于图像分割的优越性。

  7. 一种去光照干扰的运动目标图像分割算法%A Segmentation Algorithm of Moving Target Image Anti Light Interference

    Institute of Scientific and Technical Information of China (English)

    王维哲; 李娜

    2015-01-01

    The segmentation of multi scale fuzzy image processing, computer vision is to solve many problems of the founda⁃tion. Traditional image segmentation algorithm using local feature matching method based on wavelet transform, can effec⁃tively remove the interference of light, it is not good for the movement of the target image segmentation effect. This paper presents a moving object image to extract the fuzzy image edge energy feature based on light interference to segmentation method. Calculation of interference of light after the moving target image amplitude and frequency components, using mixed function control curve generation method of moving target image time series, the edge energy characteristic calcula⁃tion of object image is calculated for each scale, feature of image region non homomorphic block matching segmentation, the final generation of gray histogram binary equilibrium coefficient, the realization of accurate segmentation of moving tar⁃get image, removed the interference of light. The simulation results show that, the algorithm has a good segmentation result, better anti-interference ability, the quality of image segmentation is better.%对模糊图像的多尺度分割,是解决许多计算机视觉处理问题的基础。传统的图像分割算法采用基于小波变换的局部特征匹配方法,无法有效去除光照的干扰,对运动目标图像的分割效果不好。提出一种基于模糊图像边缘能量特征提取的运动目标图像的去光照干扰分割方法。计算去光照干扰后的运动目标图像振幅分量和频率分量,采用混合函数控制曲线方法生成运动目标图像时间序列,计算每个尺度下计算运动目标图像的边缘能量特征,进行图像区域特征的非同态块匹配分割,最终生成灰度直方图二进制均衡系数,实现了运动目标图像的准确分割,去除了光照干扰。仿真结果表明,该算法具有分割结果准确,抗干扰

  8. 基于改进谱聚类的合成孔径雷达溢油图像分割算法%Segmentation algorithm of SAR oil spill image based on improved spectral clustering

    Institute of Scientific and Technical Information of China (English)

    张君; 薄华; 王晓峰

    2011-01-01

    为了解决传统谱聚类算法对大尺寸海洋图像难以进行有效计算的问题,提出一种改进的谱聚类算法.采用分块方法将原始图像分割成多个子图,同时结合随机采样算法利用采集的样本估计全局样本,在保证分割精度基础上大大降低计算复杂度,有效地处理高维图像,针对随机采样的不稳定性,采用多次采样聚类并结合大多数投票的方法,得出最终的分割结果.仿真结果显示,改进算法可以有效降低计算复杂度,并保证聚类算法计算复杂度的减少与图像大小成正比,分块方法和多次聚类结果的融合可以大大提高溢油目标分割的精度.%In order to solve the problem that classical spectrum clustering algorithm can not calculate effectively for the large sea images, an improved spectral clustering algorithm is proposed. The partition method is used to divide the original image into multiple sub-images, and the collected samples are employed to estimate global samples combining with the random sampling algorithm. Thus, the computational complexity is greatly reduced on the basis of guaranteeing segmentation accuracy, and high dimensional images are processed effectively. Multiple sampling clustering combining with the majority voting method is used to obtain the final segmentation results in view of the instability of the random sampling. The simulation results show that the improved algorithm can reduce the computational complexity efficiently, and guarantee the decrease of computational complexity proportional to the size of images. Meanwhile, the hybrid of partition method and multiple clustering can make the segmentation of oil spill target reach high precision.

  9. 基于NSCT-Gabor特征和脉冲耦合神经网络的 SAR图像分割%A Segmentation Algorithm for SAR Images Based on NSCT-Gabor Characteristics and PCNN

    Institute of Scientific and Technical Information of China (English)

    吴俊政; 严卫东; 倪维平; 边辉; 张晗

    2015-01-01

    针对SAR图像目标的精确分割问题,利用非下采样轮廓波变换( NSCT )和Gabor滤波器分别提取图像特征,然后采用脉冲耦合神经网络( PCNN)对目标区域进行增强,提出了一种分割算法。分别对图像进行NSCT分解和Ga-bor滤波,对NSCT域的高、低频子带系数构造一个特征图,对Gabor滤波的不同尺度构造对应的特征图,对所获取的各个特征图用PCNN进行目标增强,最后对增强的特征图进行合理合并与分割。利用MSTAR SAR数据库中各种干扰强度下的图像进行了实验,结果表明,相比于模糊C均值、马尔可夫随机场等常见的分割算法,所提出的算法分割结果更为准确,同时受噪声干扰更小。%A segmentation algorithm was proposed by using Nonsubsampled Contourlet Transform ( NSCT ) and Gabor filter to extract characteristics of images respectively and using Pulse Coupled Neural Networks (PCNN) to enhance the target areas.Characteristic figures were constructed for the high and low frequencies of NSCT and corresponding characteristic figures were also constructed for the Gabor filters .All the characteristic figures were enhanced by PCNN .Then,the enhanced figures were integrated and segmented reasonably .Images in MSTAR SAR data library under different jamming intensities were selected for experiment .The results indicated that:Compared with the common algorithms such as FCM and the algorithm based on Markov random field,the proposed algorithm can realize more accurate segmentation for SAR images and has strong immunity from interferences .

  10. Spectrum allocation genetic algorithm of chromosome segment crossing and recombining%染色体片段交叉重组的频谱分配遗传算法

    Institute of Scientific and Technical Information of China (English)

    郭霖; 陈志刚; 曾锋

    2016-01-01

    传统的遗传算法在解决认知无线电频谱分配问题时,没有考虑染色体中来自于不同频谱的基因所表达的遗传特性是不同的,而不加区别地对染色体进行交叉会降低其进化效率。针对此问题,依据遗传特性把染色体分成不同的片段,将染色体交叉设定在每一个片段内,并加入了染色体片段重组过程,用来提高染色体进化的效率。然后从系统公平性的角度设计了自适应的变异概率,让接入率较低的染色体获得更大的变异机会,以提高系统的公平性。最后与遗传算法(genetic algorithm,GA)和量子遗传算法(quantum genetic algorithm,QGA)进行了仿真对比实验,结果表明该算法的收敛速度更快,且同时获得了较高的系统效益以及用户接入率。%Traditional genetic algorithm in solving the problem of cognitive radio spectrum allocation,without considering the genetic characteristics of genes expressed from the chromosome in different spectrum is different,and indiscriminate cross-chro-mosome evolution will reduce its efficiency.To solve this problem,this paper based on genetic characteristics divided the chro-mosome into different segments,chromosome crossover would be set within each segment,and joined the chromosome segment restructuring process,to improve the efficiency of chromosomal evolution,and from the perspective of fairness design adaptive mutation probability,so that the lower rate of chromosomal gain access to greater mutating opportunities to improve the fairness of the system.Finally with genetic algorithm and quantum genetic algorithm for the simulation experiments comparing,the re-sults show that this algorithm converges faster,and also wins the higher system benefit and user access ratio.

  11. Image segmentation algorithm combined PCNN with maximal correlative criterion%PCNN和最大相关准则相结合的图像分割方法

    Institute of Scientific and Technical Information of China (English)

    邓成锦; 聂仁灿; 周冬明; 赵东风

    2011-01-01

    脉冲耦合神经网络(PCNN)是有着生物学背景的新一代人工神经网络,在图像分割方面体现了优异的性能.PCNN模型在参数估计和阈值迭代方面的问题还有待解决.将一维最大相关准则和二维最大相关准则相结合来估计神经元参数,实现了图像分割的自动化并降低了运算的复杂性.仿真结果表明,该方法在分割图效果和运算复杂度方面都得到了提高,具有较好的实用性.%Pulse Coupled Neural Network(PCNN) is a new generation which has a biological background of artificial neural network, reflects excellent performance in the image segmentation. But the problems of PCNN model parameter estimation and threshold iteration are not been resolved. This paper combines one dimension maximal correlative criterion and two dimension maximal correlative criterion to estimate the neuron parameters,achieves the automation of image segmentation and reduces the complexity of computing. Simulation results show that the proposed method results in the segmentation map and computational complexity compared with the related literature have been improved,and has better usability.

  12. 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.

  13. 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 limite