WorldWideScience

Sample records for anatomy segmentation algorithm

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

    International Nuclear Information System (INIS)

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

  2. Performance Evaluation of Automatic Anatomy Segmentation Algorithm on Repeat or Four-Dimensional Computed Tomography Images Using Deformable Image Registration Method

    International Nuclear Information System (INIS)

    Purpose: Auto-propagation of anatomic regions of interest from the planning computed tomography (CT) scan to the 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. Methods and Materials: We had previously adopted an image intensity-based deformable registration algorithm to find the correspondence between two images. In the present study, the regions of interest delineated on the planning CT image were mapped onto daily CT or four-dimensional CT images using the same transformation. Postprocessing methods, such as boundary smoothing and modification, were used to enhance the robustness of the algorithm. Auto-propagated contours for 8 head-and-neck cancer patients with a total of 100 repeat CT scans, 1 prostate patient with 24 repeat CT scans, and 9 lung cancer patients with a total of 90 four-dimensional CT images were evaluated against physician-drawn contours and physician-modified deformed contours using the volume overlap index and mean absolute surface-to-surface distance. Results: The deformed contours were reasonably well matched with the daily anatomy on the repeat CT images. The volume overlap index and mean absolute surface-to-surface distance was 83% and 1.3 mm, respectively, compared with the independently drawn contours. Better agreement (>97% and <0.4 mm) was achieved if the physician was only asked to correct the deformed contours. The algorithm was also robust in the presence of random noise in the image. Conclusion: The deformable algorithm might be an effective method to propagate the planning regions of interest to subsequent CT images of changed anatomy, although a final review by physicians is highly recommended

  3. Fusion of motion segmentation algorithms

    OpenAIRE

    Ellis, Anna-Louise

    2008-01-01

    Many algorithms have been developed to achieve motion segmentation for video surveillance. The algorithms produce varying performances under the infinite amount of changing conditions. It has been recognised that individually these algorithms have useful properties. Fusing the statistical result of these algorithms is investigated, with robust motion segmentation in mind.

  4. Automatic segmentation of intra-cochlear anatomy in post-implantation CT

    Science.gov (United States)

    Reda, Fitsum A.; Dawant, Benoit M.; McRackan, Theodore R.; Labadie, Robert F.; Noble, Jack H.

    2013-03-01

    A cochlear implant (CI) is a neural prosthetic device that restores hearing by directly stimulating the auditory nerve with an electrode array. In CI surgery, the surgeon threads the electrode array into the cochlea, blind to internal structures. We have recently developed algorithms for determining the position of CI electrodes relative to intra-cochlear anatomy using pre- and post-implantation CT. We are currently using this approach to develop a CI programming assistance system that uses knowledge of electrode position to determine a patient-customized CI sound processing strategy. However, this approach cannot be used for the majority of CI users because the cochlea is obscured by image artifacts produced by CI electrodes and acquisition of pre-implantation CT is not universal. In this study we propose an approach that extends our techniques so that intra-cochlear anatomy can be segmented for CI users for which pre-implantation CT was not acquired. The approach achieves automatic segmentation of intra-cochlear anatomy in post-implantation CT by exploiting intra-subject symmetry in cochlear anatomy across ears. We validated our approach on a dataset of 10 ears in which both pre- and post-implantation CTs were available. Our approach results in mean and maximum segmentation errors of 0.27 and 0.62 mm, respectively. This result suggests that our automatic segmentation approach is accurate enough for developing customized CI sound processing strategies for unilateral CI patients based solely on postimplantation CT scans.

  5. Enhanced Segment Compression Steganographic Algorithm

    Directory of Open Access Journals (Sweden)

    STRATULAT, M.

    2013-08-01

    Full Text Available Steganography is the science and art of concealing messages using techniques that allow only the sender and receiver to know of the message?s existence and be able to decipher it. In this article, we would like to present a new steganographic technique for concealing digital images: the Enhanced Segment Compression Steganographic Algorithm (ESCSA. We start by mentioning several desired properties that we have taken into consideration for our algorithm. Next, we define some quality metrics with which we can measure how well / to what extent those properties are achieved. A detailed presentation of the component parts of the algorithm follows, accompanied by quantitative analyses of parameters of interest. Finally, we discuss the strengths and weaknesses of our algorithm. In addition, we make a few suggestions regarding possible further refinements of the ESCSA.

  6. 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.

  7. Speech Segmentation Algorithm Based On Fuzzy Memberships

    OpenAIRE

    Luis D. Huerta; Jose Antonio Huesca; Julio C. Contreras

    2010-01-01

    In this work, an automatic speech segmentation algorithm with text independency was implemented. In the algorithm, the use of fuzzy memberships on each characteristic in different speech sub-bands is proposed. Thus, the segmentation is performed a greater detail. Additionally, we tested with various speech signal frequencies and labeling, and we could observe how they affect the performance of the segmentation process in phonemes. The speech segmentation algorithm used is described. During th...

  8. An algorithm for segmenting range imagery

    Energy Technology Data Exchange (ETDEWEB)

    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.

  9. Visual-hint Boundary to Segment Algorithm for Image Segmentation

    CERN Document Server

    Su, Yu

    2010-01-01

    Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.

  10. 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

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

    Science.gov (United States)

    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.

  12. 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.

  13. Analysis of Image Segmentation Algorithms Using MATLAB

    Directory of Open Access Journals (Sweden)

    Deepika Khare

    2012-02-01

    Full Text Available Image segmentation has played an important role in computer vision especially for human tracking. The result of image segmentation is a set of segments that collectively cover the entire image or a set of contours extracted from the image. Its accuracy but very elusive is very crucial in areas as medical, remote sensing and image retrieval where it may contribute to save, sustain and protect human life. This paper presents the analysis and implementation using MATLAB features and one best result can be selected for any algorithm using the subjective evaluation. We considered the techniques under the following five groups: Edge-based, Clustering-based, Region-based, Threshold-based and Graph-based.

  14. Linac design algorithm with symmetric segments

    International Nuclear Information System (INIS)

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

  15. Fusion of Image Segmentation Algorithms using Consensus Clustering

    OpenAIRE

    Ozay, Mete; Vural, Fatos T. Yarman; Kulkarni, Sanjeev R.; Poor, H. Vincent

    2015-01-01

    A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image. The candidate segmentation sets are processed to achieve a consensus segmentation using a stochastic optimization algorithm based on the Filtered Stochastic BOEM (Best One Element Move) method. For this purpose, Filtered Stochastic BOEM is reformulated as a segmentation fusion problem by designing a new distance learning approach. The proposed algorithm...

  16. A Review of Retinal Vessel Segmentation Techniques and Algorithms

    OpenAIRE

    Mohd Imran Khan; Heena Shaikh; Anwar Mohd. Mansuri

    2011-01-01

    Retinal vessel segmentation algorithms are the critical components of circulatory blood vessel Analysis systems. We present a survey of vessel segmentation techniques and algorithms. We put the various vessel segmentation approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the segmentation of blood vessels, neurovascular structure in particular. We have divided vessel segmentation algorithms and techniques into six main...

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

    International Nuclear Information System (INIS)

    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 DCintraobserver = 0.89 ± 0.12, HDintraobserver = 3.6 mm ± 1.5, DCinterobserver = 0.89 ± 0.15, and HDinterobserver = 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

  18. Segmentation of Medical Image using Clustering and Watershed Algorithms

    OpenAIRE

    M. C.J. Christ; R. M. S. Parvathi

    2011-01-01

    Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to...

  19. A Framework for Evaluating Video Object Segmentation Algorithms

    OpenAIRE

    Drelie Gelasca, E.; Karaman, M.; Ebrahimi, T.; Sikora, T.

    2006-01-01

    Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no psychophysical experiments evaluating the quality of different video object segmentation results have be...

  20. 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.

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

    International Nuclear Information System (INIS)

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

  2. A New Image Segmentation Algorithm and It’s Application in lettuce object segmentation

    Directory of Open Access Journals (Sweden)

    Xiaodong Zhang

    2012-07-01

    Full Text Available Lettuce image segmentation which based on computer image processing is the premise of non-destructive testing of lettuce quality. The traditional 2-D maximum entropy algorithm has some faults, such as low accuracy of segmentation, slow speed, and poor anti-noise ability. As a result, it leads to the problems of poor image segmentation and low efficiency. An improved 2-D maximum entropy algorithm is presented in this paper. It redistricts segmented regions and furtherly classifies the segmented image pixels with the method of the minimum fuzzy entropy, and reduces the impact of noise points, as a result the image segmentation accuracy is improved. The improved algorithm is used to lettuce object segmentation, and the experimental results show that the improved segmentation algorithm has many advantages compared with the traditional 2-D maximum entropy algorithm, such as less false interference, strong anti-noise ability, good robustness and validity.  

  3. A Survey of Image Segmentation Algorithms Based On Fuzzy Clustering

    OpenAIRE

    R. Ravindraiah; K. Tejaswini

    2013-01-01

    Medical image segmentation plays a vital role in one of the most challenging fields ofengineering. Imaging modality provides detailed information about anatomy. It is also helpful in the findingof the disease and its progressive treatment. More research and work on it has enhanced more effectivenessas far as the subject is concerned. Different methods are used for medical image segmentation such asClustering methods, Thresholding method, Classifier, Region Growing, Deformable Model, Markov Ra...

  4. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    Energy Technology Data Exchange (ETDEWEB)

    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

  5. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    International Nuclear Information System (INIS)

    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

  6. An optimization algorithm for volumetrically segmented aperture-based IMRT

    International Nuclear Information System (INIS)

    There are few algorithms to create aperture-based IMRT such as Iterative least-square algorithm, Simultaneous projection algorithm (Cimmino's algorithm), Mixed integer programming, etc. In this present work, a Volumetrically Segmented Aperture Optimization (VSAO) algorithm is introduced and its usefulness in generating aperture-based IMRT plans is investigated in different case studies

  7. 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.

  8. 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. PMID:26418417

  9. 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.

  10. A Hierarchical Algorithm for Multiphase Texture Image Segmentation

    OpenAIRE

    Yalin Zheng; Ke Chen

    2012-01-01

    Image segmentation is a fundamental task for many computer vision and image processing applications. There exist many useful and reliable models for two-phase segmentation. However, the multiphase segmentation is a more challenging problem than two phase segmentation, mainly due to strong dependence on initialization of solutions. In this paper we propose a reliable hierarchical algorithm for multiphase texture image segmentation by making full use of two-phase texture models in a fuzzy membe...

  11. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    OpenAIRE

    Chen, Xinjian; Bagci, Ulas

    2011-01-01

    Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images.Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the...

  12. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    Science.gov (United States)

    Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

    2013-03-01

    Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

  13. Gaussian Kernel Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

    Directory of Open Access Journals (Sweden)

    Rehna Kalam

    2016-04-01

    Full Text Available Image processing is an important research area in c omputer vision. clustering is an unsupervised study. clustering can also be used for image segmen tation. there exist so many methods for image segmentation. image segmentation plays an importan t role in image analysis.it is one of the first and the most important tasks in image analysis and computer vision. this proposed system presents a variation of fuzzy c-means algorithm tha t provides image clustering. the kernel fuzzy c-means clustering algorithm (kfcm is derived from the fuzzy c-means clustering algorithm(fcm.the kfcm algorithm that provides ima ge clustering and improves accuracy significantly compared with classical fuzzy c-means algorithm. the new algorithm is called gaussian kernel based fuzzy c-means clustering algo rithm (gkfcmthe major characteristic of gkfcm is the use of a fuzzy clustering approach ,ai ming to guarantee noise insensitiveness and image detail preservation.. the objective of the wo rk is to cluster the low intensity in homogeneity area from the noisy images, using the clustering me thod, segmenting that portion separately using content level set approach. the purpose of designin g this system is to produce better segmentation results for images corrupted by noise, so that it c an be useful in various fields like medical image analysis, such as tumor detection, study of anatomi cal structure, and treatment planning.

  14. 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.

  15. TCP smart framing: a segmentation algorithm to reduce TCP latency

    OpenAIRE

    Mellia, Marco; Meo, Michela; Casetti, Claudio Ettore

    2005-01-01

    TCP Smart Framing, or TCP-SF for short, enables the Fast Retransmit/Recovery algorithms even when the congestion window is small. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP-SF adopts a novel segmentation algorithm: while Classic TCP always tries to send full-sized segments, a TCP-SF source adopts a more flexible segmentation algorithm to try and always have a number of in-flight segments larger than 3 so as to enable Fast ...

  16. Fuzzy Clustering Algorithms for Effective Medical Image Segmentation

    OpenAIRE

    Deepali Aneja; Tarun Kumar Rawat

    2013-01-01

    Medical image segmentation demands a segmentation algorithm which works against noise. The most popular algorithm used in image segmentation is Fuzzy C-Means clustering. It uses only intensity values for clustering which makes it highly sensitive to noise. The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-Means (IFCM), and Type-II Fuzzy C-Means (T2FCM) is presented in this paper. These algorithms are exe...

  17. Improved document image segmentation algorithm using multiresolution morphology

    Science.gov (United States)

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

    2011-01-01

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

  18. Research of the multimodal brain-tumor segmentation algorithm

    Science.gov (United States)

    Lu, Yisu; Chen, Wufan

    2015-12-01

    It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.

  19. COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES

    Directory of Open Access Journals (Sweden)

    A.A. Haseena Thasneem

    2015-05-01

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

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

    Science.gov (United States)

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

    2007-12-01

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

  1. A Review of Retinal Vessel Segmentation Techniques and Algorithms

    Directory of Open Access Journals (Sweden)

    Mohd. Imran Khan

    2011-09-01

    Full Text Available Retinal vessel segmentation algorithms are the critical components of circulatory blood vessel Analysis systems. We present a survey of vessel segmentation techniques and algorithms. We put the various vessel segmentation approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the segmentation of blood vessels, neurovascular structure in particular. We have divided vessel segmentation algorithms and techniques into six main categories: (1 Parallel Multiscale Feature Extraction and Region Growing, (2 a hybrid filtering, (3 Ridge-Based Vessel Segmentation, (4 artificial intelligencebased approaches, (5 neural network-based approaches, and (6 miscellaneous tube-like object detection approaches. Some of these categories are further divided into subcategories.

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

    Directory of Open Access Journals (Sweden)

    Ningning Zhou

    2014-01-01

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

  3. Comparison of fuzzy connectedness and graph cut segmentation algorithms

    Science.gov (United States)

    Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Falcão, A. X.; Miranda, P. A. V.

    2011-03-01

    The goal of this paper is a theoretical and experimental comparison of two popular image segmentation algorithms: fuzzy connectedness (FC) and graph cut (GC). On the theoretical side, our emphasis will be on describing a common framework in which both of these methods can be expressed. We will give a full analysis of the framework and describe precisely a place which each of the two methods occupies in it. Within the same framework, other region based segmentation methods, like watershed, can also be expressed. We will also discuss in detail the relationship between FC segmentations obtained via image forest transform (IFT) algorithms, as opposed to FC segmentations obtained by other standard versions of FC algorithms. We also present an experimental comparison of the performance of FC and GC algorithms. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as influence of the choice of the seeds on the output.

  4. 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.

  5. 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.

  6. 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.

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

    International Nuclear Information System (INIS)

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

  8. Yarn image segmentation using the region growing algorithm

    International Nuclear Information System (INIS)

    This paper is about the development of the image segmentation algorithm for the industrial measurement system. Specifically, the problem of segmentation of textile yarn images is considered. The algorithm developed for yarn hairiness analyzer is introduced. It aims at extracting single fibers protruding from the yarn core. The algorithm is a region growing-based approach where the growth of the region is guided and constrained by the coherence enhancing diffusion filter. Results of the proposed method are presented and compared with the results provided by the traditional clustering approaches and recent, well-established segmentation methods. The comparison proves that the proposed segmentation algorithm provides high quality results and significantly outperforms other methods in number of fibers extracted from the background

  9. Segmentation algorithm for non-stationary compound Poisson processes

    OpenAIRE

    Bence Toth; Fabrizio Lillo; J Doyne Farmer

    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 theref...

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

    OpenAIRE

    Ciesielski, Krzysztof Chris; Miranda, P.A.V.; A. X. Falcão; Udupa, Jayaram K.

    2013-01-01

    We introduce an image segmentation algorithm, called GCsummax, 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 GCsummax 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 GCsum...

  11. 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.

  12. 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.

  13. A Multi-Stage Algorithm for Enhanced XRay Image Segmentation

    Directory of Open Access Journals (Sweden)

    ADITYA A. TIRODKAR

    2011-09-01

    Full Text Available With the ever increasing usage of empirical data collected from X-Ray and other Digital Imaging techniques, it has become imperative that this data be subjected to computer algorithms for speedy and more accurate diagnosis. Segmentation is one of the key techniques that are employed during the pre-processing stages of these algorithms for separating those details from the images that are required for analysis. There are currently a number of widespread techniques for segmentation, in use. Our proposed algorithm is a quick and morequalitatively efficient technique for segmentation that is optimized for X-Ray images. It applies Otsu’s algorithm to provide thresholding values that can be used for contrasting and binarizing the images. Also, an edge detection technique has been applied to better evince observations, allowing more fruitful extraction of information and the algorithm has itself been tested on a set of 40 images.

  14. Multi-agent Remote Sensing Image Segmentation Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Chen

    2014-05-01

    Full Text Available Due to fractal network evolution algorithm (FNEA in the treatment of the high spatial resolution remote sensing image (HSRI using a parallel global control strategies which limited when the objects in each cycle by traversal of and not good use the continuity of homogenous area on the space and lead to problems such as bad image segmentation, therefore puts forward the remote sensing image segmentation algorithm based on multi-agent. The algorithm in the merger guidelines, combining the image spectral and shape information, and by using region merging process of multi-agent parallel control integral, its global merger control strategy can ensure algorithm has the advantages of parallel computing and fully considering the regional homogeneity, and continuity. Finally simulation experiment was performed with FNEA algorithms, experimental results show that the proposed algorithm is better than FNEA algorithm in dividing the overall effect, has a good stability

  15. Autonomous Image Segmentation using Density-Adaptive Dendritic Cell Algorithm

    Directory of Open Access Journals (Sweden)

    Vishwambhar Pathak

    2013-08-01

    Full Text Available Contemporary image processing based applications like medical diagnosis automation and analysis of satellite imagery include autonomous image segmentation as inevitable facility. The research done shows the efficiency of an adaptive evolutionary algorithm based on immune system dynamics for the task of autonomous image segmentation. The recognition dynamics of immune-kernels modeled with infinite Gaussian mixture models exhibit the capability to automatically determine appropriate number of segments in presence of noise. In addition, the model using representative density-kernel-parameters processes the information with much reduced space requirements. Experiments conducted with synthetic images as well as real images recorded assured convergence and optimal autonomous model estimation. The segmentation results tested in terms of PBM-index values have been found comparable to those of the Fuzzy C-Means (FCM for the same number of segments as generated by our algorithm.

  16. An enhanced fast scanning algorithm for image segmentation

    Science.gov (United States)

    Ismael, Ahmed Naser; Yusof, Yuhanis binti

    2015-12-01

    Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical images. It scans all pixels in the image and cluster each pixel according to the upper and left neighbor pixels. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold. Such an approach will lead to a weak reliability and shape matching of the produced segments. This paper proposes an adaptive threshold function to be used in the clustering process of the Fast Scanning algorithm. This function used the gray'value in the image's pixels and variance Also, the level of the image that is more the threshold are converted into intensity values between 0 and 1, and other values are converted into intensity values zero. The proposed enhanced Fast Scanning algorithm is realized on images of the public and private transportation in Iraq. Evaluation is later made by comparing the produced images of proposed algorithm and the standard Fast Scanning algorithm. The results showed that proposed algorithm is faster in terms the time from standard fast scanning.

  17. 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.

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

    Science.gov (United States)

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

    2010-03-01

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

  19. 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.

  20. 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.

  1. Towards an automatic coronary artery segmentation algorithm.

    Science.gov (United States)

    Fallavollita, Pascal; Cheriet, Farida

    2006-01-01

    A method is presented that aims at minimizing image processing time during X-ray fluoroscopy interventions. First, an automatic frame extraction algorithm is proposed in order to extract relevant image frames with respect to their cardiac phase (systole or diastole). Secondly, a 4-step filter is suggested in order to enhance vessel contours. The reciprocal of the enhanced image is used as an alternative speed function to initialize the fast marching method. The complete algorithm was tested on eight clinical angiographic data sets and comparisons with two other vessel enhancement filters (Lorenz and Frangi) are made for the centerline extraction procedure. In order to assess the suitability of our filter the extracted centerline coordinates are compared with the manually traced axis. PMID:17946540

  2. Mammographic images segmentation based on chaotic map clustering algorithm

    International Nuclear Information System (INIS)

    This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI

  3. A novel algorithm for segmentation of brain MR images

    International Nuclear Information System (INIS)

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

  4. 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.

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

    International Nuclear Information System (INIS)

    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.

  6. 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.

  7. 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

  8. A hybrid lung and vessel segmentation algorithm for computer aided detection of pulmonary embolism

    Science.gov (United States)

    Raghupathi, Laks; Lakare, Sarang

    2009-02-01

    Advances in multi-detector technology have made CT pulmonary angiography (CTPA) a popular radiological tool for pulmonary emboli (PE) detection. CTPA provide rich detail of lung anatomy and is a useful diagnostic aid in highlighting even very small PE. However analyzing hundreds of slices is laborious and time-consuming for the practicing radiologist which may also cause misdiagnosis due to the presence of various PE look-alike. Computer-aided diagnosis (CAD) can be a potential second reader in providing key diagnostic information. Since PE occurs only in vessel arteries, it is important to mark this region of interest (ROI) during CAD preprocessing. In this paper, we present a new lung and vessel segmentation algorithm for extracting contrast-enhanced vessel ROI in CTPA. Existing approaches to segmentation either provide only the larger lung area without highlighting the vessels or is computationally prohibitive. In this paper, we propose a hybrid lung and vessel segmentation which uses an initial lung ROI and determines the vessels through a series of refinement steps. We first identify a coarse vessel ROI by finding the "holes" from the lung ROI. We then use the initial ROI as seed-points for a region-growing process while carefully excluding regions which are not relevant. The vessel segmentation mask covers 99% of the 259 PE from a real-world set of 107 CTPA. Further, our algorithm increases the net sensitivity of a prototype CAD system by 5-9% across all PE categories in the training and validation data sets. The average run-time of algorithm was only 100 seconds on a standard workstation.

  9. 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.

  10. FCM Algorithm for Medical Image Segmentation Using HMRF.

    OpenAIRE

    Rajeev V R; Dr Sreeja Mole S S

    2013-01-01

    Clustering of data is a method by which large sets of data are grouped into clusters of smaller sets of similar data. Fuzzy c-means (FCM) clustering algorithm is one of the most commonly used unsupervised clustering technique in the field of medical imaging. Medical image segmentation refers to the segmentation of known anatomic structures from medical images. Fuzzy C-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. Fuzzy logic is a multi...

  11. 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. PMID:23880374

  12. 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.

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Dong Qin

    2014-08-01

    Full Text Available 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 unstability in clustering result of the scale parameter input manually. In addition, we try to excavate available priori information existing in large number of non-generic data and apply semi-supervised algorithm to improve the clustering performance for rare class. We also use added tag data to compute similar matrix and perform clustering through FKCM algorithms. By the simulation of standard dataset and image segmentation, the experiments demonstrate our algorithm has overcome the defects of traditional spectral clustering methods, which are sensitive to outliers and easy to fall into local optimum, and also poor in the convergence rate

  15. 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.

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

    International Nuclear Information System (INIS)

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

  17. An Improved Convexity Based Segmentation Algorithm for Heavily Camouflaged Images

    Directory of Open Access Journals (Sweden)

    Amarjot Singh

    2013-03-01

    Full Text Available The paper proposes an advanced convexity based segmentation algorithm for heavily camouflaged images. The convexity of the intensity function is used to detect camouflaged objects from complex environments. We take advantage of operator for the detection of 3D concave or convex graylevels to exhibit the effectiveness of camouflage breaking based on convexity. The biological motivation behind operator and its high robustness make it suitable for camouflage breaking. The traditional convexity based algorithm identifies the desired targets but in addition also identifies sub-targets due to their three dimensional behavior. The problem is overcome by combining the conventional algorithm with thresholding. The proposed method is able to eliminate the sub-targets leaving behind only the target of interest in the input image. The proposed method is compared with the conventional operator. It is also compared with some conventional edge based operator for performance evaluation.

  18. 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.

  19. 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) ...

  20. Sampling protein conformations using segment libraries and a genetic algorithm

    Science.gov (United States)

    Gunn, John R.

    1997-03-01

    We present a new simulation algorithm for minimizing empirical contact potentials for a simplified model of protein structure. The model consists of backbone atoms only (including Cβ) with the φ and ψ dihedral angles as the only degrees of freedom. In addition, φ and ψ are restricted to a finite set of 532 discrete pairs of values, and the secondary structural elements are held fixed in ideal geometries. The potential function consists of a look-up table based on discretized inter-residue atomic distances. The minimization consists of two principal elements: the use of preselected lists of trial moves and the use of a genetic algorithm. The trial moves consist of substitutions of one or two complete loop regions, and the lists are in turn built up using preselected lists of randomly-generated three-residue segments. The genetic algorithm consists of mutation steps (namely, the loop replacements), as well as a hybridization step in which new structures are created by combining parts of two "parents'' and a selection step in which hybrid structures are introduced into the population. These methods are combined into a Monte Carlo simulated annealing algorithm which has the overall structure of a random walk on a restricted set of preselected conformations. The algorithm is tested using two types of simple model potential. The first uses global information derived from the radius of gyration and the rms deviation to drive the folding, whereas the second is based exclusively on distance-geometry constraints. The hierarchical algorithm significantly outperforms conventional Monte Carlo simulation for a set of test proteins in both cases, with the greatest advantage being for the largest molecule having 193 residues. When tested on a realistic potential function, the method consistently generates structures ranked lower than the crystal structure. The results also show that the improved efficiency of the hierarchical algorithm exceeds that which would be anticipated

  1. Self-adaptive algorithm for segmenting skin regions

    Science.gov (United States)

    Kawulok, Michal; Kawulok, Jolanta; Nalepa, Jakub; Smolka, Bogdan

    2014-12-01

    In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

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

    Science.gov (United States)

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

    2014-03-01

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

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

    International Nuclear Information System (INIS)

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J. [Department of Radiation Oncology, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States); Department of Bioinformatics, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States); Department of Radiation Oncology, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States); Department of Computer Science, Rutgers, State University of New Jersey, Piscataway, New Jersey 08854 (United States); Department of Radiation Oncology, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States)

    2010-03-15

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

  5. Page Segmentation using XY Cut Algorithm in OCR Systems - A Review

    OpenAIRE

    Sukhvir Kaur; P.S. Mann; Sukhwinder Kaur

    2013-01-01

    Page segmentation is an important field to analyse patterns from the OCR Systems. In this paper we tried to present how page segmentation is done on the OCR systems. We discussed the XY Cut page segmentation algorithm. The result of XY Cut page segmentation is evaluated on scanned OCR document.

  6. 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 ...

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

    OpenAIRE

    Rommelse, J.R.; H.X. Lin; Chan, T.F.

    2004-01-01

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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Categorizing segmentation quality using a quantitative quality assurance algorithm

    International Nuclear Information System (INIS)

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

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

    OpenAIRE

    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-operative planning. Methods Individual voxels were classified based on a set of automatically extracted image features. Minimum-cost graph cuts were computed on the classification results. The graph...

  14. Anatomy of the ostia venae hepaticae and the retrohepatic segment of the inferior vena cava.

    OpenAIRE

    Camargo, A M; Teixeira, G G; Ortale, J R

    1996-01-01

    In 30 normal adult livers the retrohepatic segment of inferior vena cava had a length of 6.7 cm and was totally encircled by liver substance in 30% of cases. Altogether 442 ostia venae hepaticae were found, averaging 14.7 per liver and classified as large, medium, small and minimum. The localisation of the openings was studied according to the division of the wall of the retrohepatic segment of the inferior vena cava into 16 areas.

  15. Anatomy of the ostia venae hepaticae and the retrohepatic segment of the inferior vena cava.

    Science.gov (United States)

    Camargo, A M; Teixeira, G G; Ortale, J R

    1996-02-01

    In 30 normal adult livers the retrohepatic segment of inferior vena cava had a length of 6.7 cm and was totally encircled by liver substance in 30% of cases. Altogether 442 ostia venae hepaticae were found, averaging 14.7 per liver and classified as large, medium, small and minimum. The localisation of the openings was studied according to the division of the wall of the retrohepatic segment of the inferior vena cava into 16 areas. PMID:8655416

  16. Using Quadtree Algorithm for Improving Fuzzy C-means Method in Image Segmentation

    Directory of Open Access Journals (Sweden)

    Zahra Ghorbanzad

    2012-11-01

    Full Text Available Image segmentation is an essential processing step for much image application and there are a large number of segmentation techniques. A new algorithm for image segmentation called Quad tree fuzzy c-means (QFCM is presented I this work. The key idea in our approach is a Quad tree function combined with fuzzy c-means algorithm. In this article we also discuss the advantages and disadvantages of other image segmenting methods like: k-means, c-means, and blocked fuzzy c-means. Different experimental results on several images in this article show that the proposed method significantly increases the accuracy and speed of image segmentation

  17. 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.

  18. Adaptation of the MARACAS algorithm for carotid artery segmentation and stenosis quantification on CT images

    OpenAIRE

    Zuluaga, M. A.; Orkisz, M.; Delgado, E. J. F.; Doré, V.; Pinzón, A. M.; Hoyos, M. H.

    2010-01-01

    This paper describes the adaptations of MARACAS algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The MARACAS algorithm, which is based on an elastic model and on a multi-scale eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT images and the bifurcations. The algorithms implemented in this new version are c...

  19. 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...

  20. Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term

    OpenAIRE

    Yong Yang; Shuying Huang

    2012-01-01

    To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering algorithm, a novel extended FCM algorithm for image segmentation is presented in this paper. The algorithm is developed by modifying the objective function of the standard FCM algorithm with a penalty term that takes into account the influence of the neighboring pixels on the centre pixels. The penalty term acts as a regularizer in this algorithm, which is inspired from the neighborhood expectation maximization...

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

    Directory of Open Access Journals (Sweden)

    Zhihua Cai

    2014-09-01

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

  2. An Efficient Character Segmentation Based on VNP Algorithm

    OpenAIRE

    S. Chitrakala; Srivardhini Mandipati; S. Preethi Raj; Gottumukkala Asisha

    2012-01-01

    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 segmentat...

  3. 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.

  4. An efficient Video Segmentation Algorithm with Real time Adaptive Threshold Technique

    Directory of Open Access Journals (Sweden)

    Yasira Beevi C P

    2009-12-01

    Full Text Available Automatic video segmentation plays an important role in real-time MPEG-4 encoding systems. This paper presents a video segmentation algorithm for MPEG-4 camera system with change detection, background registration techniques and real time adaptive thresholdtechniques. This algorithm can give satisfying segmentation results with low computation load. Besides, it has shadow cancellation mode, which can deal with light changing effect and shadow effect. Furthermore, this algorithm also implemented real time adaptive threshold techniques by which the parameters can be decided automatically.

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

    International Nuclear Information System (INIS)

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

  6. AN INTELLIGENT SEGMENTATION ALGORITHM FOR MICROARRAY IMAGE PROCESSING

    Directory of Open Access Journals (Sweden)

    P.Rajkumar

    2013-06-01

    Full Text Available Microarray technology consists of an array of thousands of microscopic spots of DNA oligonucleotides attached to a solid surface. It is a very powerful technique for analyzing gene expressions as well as to explore the underlying genetic causes of many human diseases. There are numerous applications of this technology, including environmental health research, drug design and discovery, clinical diagnosis and treatment and in cancer detection. The spots, which represent genes in microarray experiment contains the quantitative information that needs to be extracted accurately. For this process, preprocessing of microarray plays an essential role and it is also influential in future steps of the analysis. The three microarray preprocessing steps include gridding, segmentation and quantification. The first step is gridding, refers to the identification of the centre coordinates of each spot. The second step is segmentation, refers to the process of separating foreground and background fluorescence intensities. Segmentation is very important step as it directly affects the accuracy of gene expression analysis in the data mining process that follows. Accurate segmentation is one of the vital steps in microarray image processing. A novel method for segmentation of microarray image is proposed which accurately segment the spots from background when compared with adaptive threshold, combined global and local thresholdand fuzzy c-means clustering methods. Experimental results show that our proposed method provides better segmentation and improved intensity values than the above existing methods.

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

    International Nuclear Information System (INIS)

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

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

    Science.gov (United States)

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

    2012-12-01

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

  9. 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.

  10. 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.

  11. Accuracy of patient specific organ-dose estimates obtained using an automated image segmentation algorithm

    Science.gov (United States)

    Gilat-Schmidt, Taly; Wang, Adam; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

    2016-03-01

    The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas approach was also investigated. We hypothesize that the auto-segmentation algorithm is sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT scans were expertly segmented into nine regions. A leave-one-out validation study was performed, where every case was automatically segmented with each of the remaining cases used as the expert atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with median error for each organ region below 2%. In the spinal canal region, the median error was 7% across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case atlas reduced the variation in the dose estimates and additional improvements may be possible with more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated segmentation algorithm to provide accurate organ dose estimates.

  12. 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.

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

    Science.gov (United States)

    Hassan, M. H.

    1989-03-01

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

  14. A New Image Threshold Segmentation based on Fuzzy Entropy and Improved Intelligent Optimization Algorithm

    OpenAIRE

    Yong-sheng Wang

    2014-01-01

    Image segmentation is one of the key techniques in the field of image understanding and computer vision. To determine the optimal threshold in image segmentation, an effective image threshold segmentation method based on fuzzy logic is presented. A new kind of fuzzy entropy is defined, that is not only related to the membership, but also related to probability distribution. According to the maximum entropy criterion, the improved particle swarm optimization algorithm based on chaos bee colony...

  15. New deterministic algorithms for counting pairs of intersecting segments and off-line triangle range searching

    OpenAIRE

    Pellegrini, M.

    1995-01-01

    We describe a new method for decomposing planar sets of segments and points. Using this method we obtain new efficient {\\em deterministic} algorithms for counting pairs of intersecting segments, and for answering off-line triangle range queries. In particular we obtain the following results: \

  16. Automatic tuning of MST segmentation of mammograms for registration and mass detection algorithms

    OpenAIRE

    Mariusz Bajger; Fei Ma; Bottema, Murk J.

    2009-01-01

    A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms.

  17. Automatically Gradient Threshold Estimation of Anisotropic Diffusion for Meyer’s Watershed Algorithm Based Optimal Segmentation

    Directory of Open Access Journals (Sweden)

    Mithun Kumar PK

    2014-11-01

    Full Text Available Medical image segmentation is a fundamental task in the medical imaging field. Optimal segmentation is required for the accurate judgment or appropriate clinical diagnosis. In this paper, we proposed automatically gradient threshold estimator of anisotropic diffusion for Meyer’s Watershed algorithm based optimal segmentation. The Meyer’s Watershed algorithm is the most significant for a large number of regions separations but the over segmentation is the major drawback of the Meyer’s Watershed algorithm. We are able to remove over segmentation after using anisotropic diffusion as a preprocessing step of segmentation in the Meyer’s Watershed algorithm. We used a fixed window size for dynamically gradient threshold estimation. The gradient threshold is the most important parameter of the anisotropic diffusion for image smoothing. The proposed method is able to segment medical image accurately because of obtaining the enhancement image. The introducing method demonstrates better performance without loss of any clinical information while preserving edges. Our investigated method is more efficient and effective in order to segment the region of interests in the medical images indeed.

  18. 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. PMID:23956693

  19. 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.

  20. 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

    2015-12-21

    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. PMID:26584044

  1. 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

  2. Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

    Institute of Scientific and Technical Information of China (English)

    Baoping Wang; Yang Fang; Chao Sun

    2014-01-01

    An image segmentation algorithm of the restrained fuzzy Kohonen clustering network (RFKCN) based on high-dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional (3-D) feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg-mentation. The proposed algorithm ful y takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3-D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal-yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.

  3. 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.

  4. 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

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

    Czech Academy of Sciences Publication Activity Database

    Beneš, Miroslav; Zitová, Barbara

    -, - (2014), s. 1-21. ISSN 0022-2720 R&D Projects: GA ČR GAP103/12/2211 Institutional support: RVO:67985556 Keywords : image segmentation * performance evaluation * microscopic images Subject RIV: JC - Computer Hardware ; Software Impact factor: 2.331, year: 2014 http://library.utia.cas.cz/separaty/2014/ZOI/zitova-0434809-DOI.pdf

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

    International Nuclear Information System (INIS)

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

  7. DDoS Defense Algorithm Based on Multi-Segment Timeout Technology

    Institute of Scientific and Technical Information of China (English)

    DU Ruizhong; YANG Xiaohui; MA Xiaoxue; HE Xinfeng

    2006-01-01

    Through the analysis to the DDoS(distributed denial of service) attack, it will conclude that at different time segments, the arrive rate of normal SYN (Synchronization) package are similar, while the abnormal packages are different with the normal ones. Toward this situation a DDoS defense algorithm based on multi-segment timeout technology is presented, more than one timeout segment are set to control the net flow. Experiment results show that in the case of little flow, multi-segment timeout has the ability dynamic defense, so the system performance is improved and the system has high response rate.

  8. 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.

  9. Optree: a learning-based adaptive watershed algorithm for neuron segmentation.

    Science.gov (United States)

    Uzunbaş, Mustafa Gökhan; Chen, Chao; Metaxas, Dimitris

    2014-01-01

    We present a new algorithm for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. Our method selects a collection of nodes from the watershed mergng tree as the proposed segmentation. This is achieved by building a onditional random field (CRF) whose underlying graph is the merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our algorithm outperforms state-of-the-art methods. Both the inference and the training are very efficient as the graph is tree-structured. Furthermore, we develop an interactive segmentation framework which selects uncertain regions for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally. PMID:25333106

  10. 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.

  11. New CSC Segment Builder Algorithm with MC TeV Muons in CMS Experiment

    CERN Document Server

    Voytishin, Nikolay

    2016-01-01

    The performance of the new Cathode Strip Chamber segment builder algorithm with simulated TeV muons is considered. The comparison of some of the main reconstruction characteristics is made. Some case study events are visualized in order to illustrate the improvement that the new algorithm gives to the reconstruction process.

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

    International Nuclear Information System (INIS)

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

  13. Genetic algorithm based deliverable segments optimization for static intensity-modulated radiotherapy

    International Nuclear Information System (INIS)

    The static delivery technique (also called step-and-shoot technique) has been widely used in intensity-modulated radiotherapy (IMRT) because of the simple delivery and easy quality assurance. Conventional static IMRT consists of two steps: first to calculate the intensity-modulated beam profiles using an inverse planning algorithm, and then to translate these profiles into a series of uniform segments using a leaf-sequencing tool. In order to simplify the procedure and shorten the treatment time of the static mode, an efficient technique, called genetic algorithm based deliverable segments optimization (GADSO), is developed in our work, which combines these two steps into one. Taking the pre-defined beams and the total number of segments per treatment as input, the number of segments for each beam, the segment shapes and weights are determined automatically. A group of interim modulated beam profiles quickly calculated using a conjugate gradient (CG) method are used to determine the segment number for each beam and to initialize segment shapes. A modified genetic algorithm based on a two-dimensional binary coding scheme is used to optimize the segment shapes, and a CG method is used to optimize the segment weights. The physical characters of a multileaf collimator, such as the leaves interdigitation limitation and leaves maximum over-travel distance, are incorporated into the optimization. The algorithm is applied to some examples and the results demonstrate that GADSO is able to produce highly conformal dose distributions using 20-30 deliverable segments per treatment within a clinically acceptable computation time

  14. 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.

  15. A MULTILEVEL AUTOMATIC THRESHOLDING FOR IMAGE SEGMENTATION USING GENETIC ALGORITHM AND DWT

    Directory of Open Access Journals (Sweden)

    Rakesh Kumar

    2012-01-01

    Full Text Available In this paper, An Automatic Multilevel Thresholding Method for Image segmentation is proposed based on Discrete Wavelet Transforms and Genetic Algorithm. We have combined Genetic Algorithm with DWT to make Segmentation faster and adequate results. First the length of the histogram is reduced by using DWT. Using this Reduced Histogram, the number of Thresholds and Threshold Value are determined by Genetic Algorithm. The Thresholds are then projected in original Space. From the analysis of results, it can be concluded that the proposed method is fast and accurate.

  16. Color tongue image segmentation using fuzzy Kohonen networks and genetic algorithm

    Science.gov (United States)

    Wang, Aimin; Shen, Lansun; Zhao, Zhongxu

    2000-04-01

    A Tongue Imaging and Analysis System is being developed to acquire digital color tongue images, and to automatically classify and quantify the tongue characteristics for traditional Chinese medical examinations. An important processing step is to segment the tongue pixels into two categories, the tongue body (no coating) and the coating. In this paper, we present a two-stage clustering algorithm that combines Fuzzy Kohonen Clustering Networks and Genetic Algorithm for the segmentation, of which the major concern is to increase the interclass distance and at the same time decrease the intraclass distance. Experimental results confirm the effectiveness of this algorithm.

  17. 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.

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

    International Nuclear Information System (INIS)

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

  19. 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.

  20. Region-Based Segmentation: Fuzzy Connectedness, Graph Cut and Related Algorithms

    Science.gov (United States)

    Ciesielski, Krzysztof Chris; Udupa, Jayaram K.

    In this chapter, we will review the current state of knowledge on regionbased digital image segmentation methods. More precisely, we will concentrate on the four families of such algorithms: (a) The leading theme here will be the framework of fuzzy connectedness (FC) methods. (b) We will also discuss in detail the family of graph cut (GC) methods and their relations to the FC family of algorithms. The GC methodology will be of special importance to our presentation, since we will emphasize the fact that the methods discussed here can be formalized in the language of graphs and GCs. The other two families of segmentation algorithms we will discuss consist of (c) watershed (WS) and (d) the region growing level set (LS) methods. Examples from medical image segmentation applications with different FC algorithms are also included.

  1. Performance characterization of clustering algorithms for colour image segmentation

    OpenAIRE

    Ilea, Dana E.; Whelan, Paul F.; Ghita, Ovidiu

    2006-01-01

    This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied o...

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

    Directory of Open Access Journals (Sweden)

    Zoran N. Milivojevic

    2011-09-01

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

  3. A Study Of Image Segmentation Algorithms For Different Types Of Images

    Directory of Open Access Journals (Sweden)

    Krishna Kant Singh

    2010-09-01

    Full Text Available In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels.Image segmentation is typically used to locate objects and boundaries (lines, curves, etc. in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection. Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture.Due to the importance of image segmentation a number of algorithms have been proposed but based on the image that is inputted the algorithm should be chosen to get the best results. In this paper the author gives a study of the various algorithms that are available for color images,text and gray scale images.

  4. 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.

  5. 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

    2016-01-01

    On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radia-tion 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

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

    Science.gov (United States)

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

    2015-05-01

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

  7. A New Image Threshold Segmentation based on Fuzzy Entropy and Improved Intelligent Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Yong-sheng Wang

    2014-04-01

    Full Text Available Image segmentation is one of the key techniques in the field of image understanding and computer vision. To determine the optimal threshold in image segmentation, an effective image threshold segmentation method based on fuzzy logic is presented. A new kind of fuzzy entropy is defined, that is not only related to the membership, but also related to probability distribution. According to the maximum entropy criterion, the improved particle swarm optimization algorithm based on chaos bee colony is used to determine the optimal parameters of membership function to automatically determine the optimal threshold segmentation. The experiment results show that proposed algorithm based on fuzzy entropy and chaos bee colony particle swarm optimization has good performance.

  8. Automated Algorithm for Carotid Lumen Segmentation and 3D Reconstruction in B-mode images

    OpenAIRE

    Jorge M. S. Pereira; João Manuel R. S. Tavares

    2011-01-01

    The B-mode image system is one of the most popular systems used in the medical area; however it imposes several difficulties in the image segmentation process due to low contrast and noise. Although these difficulties, this image mode is often used in the study and diagnosis of the carotid artery diseases.In this paper, it is described the a novel automated algorithm for carotid lumen segmentation and 3-D reconstruction in B- mode images.

  9. Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

    OpenAIRE

    Muhammad, Azam Sheikh; Lavesson, Niklas; Davidsson, Paul; Nilsson, Mikael

    2009-01-01

    Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we select...

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

    OpenAIRE

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

    2012-01-01

    In many studies, the estimation of the apparent diffusion coefficient (ADC) of lesions in visceral organs in diffusion-weighted (DW) magnetic resonance images requires an accurate lesion-segmentation algorithm. To evaluate these lesion-segmentation algorithms, region-overlap measures are used currently. However, the end task from the DW images is accurate ADC estimation, and the region-overlap measures do not evaluate the segmentation algorithms on this task. Moreover, these measures rely on ...

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

    Science.gov (United States)

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

    2015-11-30

    Layer boundary (base and top) detection is a basic problem in lidar data processing, the results of which are used as inputs of optical properties retrieval. However, traditional algorithms not only require manual intervention but also rely heavily on the signal-to-noise ratio. Therefore, we propose a robust and automatic algorithm for layer detection based on a novel algorithm for lidar signal segmentation and representation. Our algorithm is based on the lidar equation and avoids most of the limitations of the traditional algorithms. Testing of the simulated and real signals shows that the algorithm is able to position the base and top accurately even with a low signal to noise ratio. Furthermore, the results of the classification are accurate and satisfactory. The experimental results confirm that our algorithm can be used for automatic detection, retrieval, and analysis of lidar data sets. PMID:26698806

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

    International Nuclear Information System (INIS)

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

  13. Urinary stone size estimation: a new segmentation algorithm-based CT method

    Energy Technology Data Exchange (ETDEWEB)

    Liden, Mats; Geijer, Haakan [Oerebro University, School of Health and Medical Sciences, Oerebro (Sweden); Oerebro University Hospital, Department of Radiology, Oerebro (Sweden); Andersson, Torbjoern [Oerebro University, School of Health and Medical Sciences, Oerebro (Sweden); Broxvall, Mathias [Oerebro University, Centre for Modelling and Simulation, Oerebro (Sweden); Thunberg, Per [Oerebro University, School of Health and Medical Sciences, Oerebro (Sweden); Oerebro University Hospital, Department of Medical Physics, Oerebro (Sweden)

    2012-04-15

    The size estimation in CT images of an obstructing ureteral calculus is important for the clinical management of a patient presenting with renal colic. The objective of the present study was to develop a reader independent urinary calculus segmentation algorithm using well-known digital image processing steps and to validate the method against size estimations by several readers. Fifty clinical CT examinations demonstrating urinary calculi were included. Each calculus was measured independently by 11 readers. The mean value of their size estimations was used as validation data for each calculus. The segmentation algorithm consisted of interpolated zoom, binary thresholding and morphological operations. Ten examinations were used for algorithm optimisation and 40 for validation. Based on the optimisation results three segmentation method candidates were identified. Between the primary segmentation algorithm using cubic spline interpolation and the mean estimation by 11 readers, the bias was 0.0 mm, the standard deviation of the difference 0.26 mm and the Bland-Altman limits of agreement 0.0{+-}0.5 mm. The validation showed good agreement between the suggested algorithm and the mean estimation by a large number of readers. The limit of agreement was narrower than the inter-reader limit of agreement previously reported for the same data. (orig.)

  14. 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.

  15. Control algorithm for the petal-shape segmented-mirror telescope with 18 mirrors

    Science.gov (United States)

    Shimono, Atsushi; Iwamuro, Fumihide; Kurita, Mikio; Moritani, Yuki; Kino, Masaru; Maihara, Toshinori; Izumiura, Hideyuki; Yoshida, Michitoshi

    2012-09-01

    A 3.8 m segmented telescope is planned to be built at the Okayama Astrophysical Observatory by the joint program among Kyoto university, Nagoya university, NAOJ, and Nano-Optonics Energy Inc. This is the world’s first optical-infrared telescope whose primary mirror is composed of “petal-shaped” segment mirrors. To investigate the best layout of the displacement sensors as well as to study the control algorithm, we have developed a simulation software for the segmented petaloid mirrors. This simulator calculates the vertical position differences between the segments at the 60 displacement sensors based on the three-dimensional movements of the 54 actuators, and enables us to test the control algorithms under various conditions including random noise on the displacement sensors, random movement errors of the actuators, and unexpected lateral shifts of the segments. The outputs of the simulator are not only the phase error of the primary mirror but also the PSF image, taking the structure function of the optical surfaces into account. Using a singular value decomposition method, we found that the 18 petal-shaped segments are controllable within the required displacement errors of 15 nm under the following three conditions: 1) the displacement measurement sensors are placed in staggered fashion between segments, 2) the displacement measurement sensors are axisymmetrically placed with respect to the optical axis, and 3) the relative lateral shift and rotation of each segment are less than 500 μm and 0.05 degree, respectively. In this report, the control algorithm, requirements for the layout of the displacement measurement sensors, and the simulated performance will be presented.

  16. An audio-based sports video segmentation and event detection algorithm

    OpenAIRE

    Baillie, M.; Jose, J.M.

    2004-01-01

    In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection....

  17. Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images

    Science.gov (United States)

    LeAnder, Robert; Chowdary, Myneni Sushma; Mokkapati, Swapnasri; Umbaugh, Scott E.

    2008-03-01

    Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss. Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss. Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms. Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools software environment. Another set of fifteen images were derived from the first fifteen and contained ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms. Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM), Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10% higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2 outperformed Algorithm 1 in terms of visual clarity, FOM

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

    International Nuclear Information System (INIS)

    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

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

    Science.gov (United States)

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

    2016-04-01

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

  20. 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.

  1. 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.

  2. 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.

  3. Segmentation of Touching Hand written Telugu Characters by using Drop Fall Algorithm

    Directory of Open Access Journals (Sweden)

    Adabala Venkata Srinivasa Rao

    2012-11-01

    Full Text Available Recognition of Indian language scripts is a challenging problem. Work for the development of complete OCR systems for Indian language scripts is still in infancy. Complete OCR systems have recently been developed for Devanagri and Bangla scripts. Research in the field of recognition of Telugu script faces major problems mainly related to the touching and overlapping of characters. Segmentation of touching Telugu characters is a difficult task for recognizing individual characters.  In this paper, the proposed algorithm is for the segmentation of   touching Hand written Telugu characters. The proposed method using Drop-fall algorithm is based on the moving of a marble on either side of the touching characters for selection of the point from where the cutting of the fused components should take place. This method improvers the segmentation accuracy higher than the existing one.

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

    International Nuclear Information System (INIS)

    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

  5. 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 and a...

  6. 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)

  7. 3D segmentation of medical images using a fast multistage hybrid algorithm

    International Nuclear Information System (INIS)

    In this paper, we propose a fast multistage hybrid algorithm for 3D segmentation of medical images. We first employ a morphological recursive erosion operation to reduce the connectivity between the object to be segmented and its neighborhood; then the fast marching method is used to greatly accelerate the initial propagation of a surface front from the user defined seed structure to a surface close to the desired boundary; a morphological reconstruction method then operates on this surface to achieve an initial segmentation result; and finally morphological recursive dilation is employed to recover any structure lost in the first stage of the algorithm. This approach is tested on 60 CT or MRI images of the brain, heart and urinary system, to demonstrate the robustness of this technique across a variety of imaging modalities and organ systems. The algorithm is also validated against datasets for which ''truth'' is known. These measurements revealed that the algorithm achieved a mean ''similarity index'' of 0.966 across the three organ systems. The execution time for this algorithm, when run on a 550 MHz Dual PIII-based PC runningWindows NT, and extracting the cortex from brain MRIs, the cardiac surface from dynamic CT, and the kidneys from 3D CT, was 38, 46 and 23 s, respectively. (orig.)

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

    International Nuclear Information System (INIS)

    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)

  9. 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.

  10. 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.

  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

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

  12. Brain tumor segmentation in MR slices using improved GrowCut algorithm

    Science.gov (United States)

    Ji, Chunhong; Yu, Jinhua; Wang, Yuanyuan; Chen, Liang; Shi, Zhifeng; Mao, Ying

    2015-12-01

    The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.

  13. Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier

    CERN Document Server

    Delogu, P; Kasae, P; Retico, A

    2008-01-01

    The computer-aided diagnosis system we developed for the mass characterization is mainly based on a segmentation algorithm and on the neural classification of several features computed on the segmented mass. Mass segmentation plays a key role in most computerized systems. Our technique is a gradient-based one, showing the main characteristic that no free parameters have been evaluated on the dataset used in this analysis, thus it can directly be applied to datasets acquired in different conditions without any ad-hoc modification. A dataset of 226 masses (109 malignant and 117 benign) has been used in this study. The segmentation algorithm works with a comparable efficiency both on malignant and benign masses. Sixteen features based on shape, size and intensity of the segmented masses are analyzed by a multi-layered perceptron neural network. A feature selection procedure has been carried out on the basis of the feature discriminating power and of the linear correlations interplaying among them. The comparison...

  14. A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them

    Science.gov (United States)

    Ciesielski, Krzysztof Chris; Udupa, Jayaram K.; Falcão, A. X.; Miranda, P. A. V.

    2012-02-01

    We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the lp norm, 1 graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC). The norm-based description of GGC provides more elegant and mathematically better recognized framework of our earlier results from [18, 19]. Moreover, it allows precise theoretical comparison of GGC representable algorithms with the algorithms discussed in a recent paper [22] (min-cut/max-flow graph cut, random walker, shortest path/geodesic, Voronoi diagram, power watershed/shortest path forest), which optimize, via lp norms, the intermediate segmentation step, the labeling of scene voxels, but for which the final object need not optimize the used lp energy function. Actually, the comparison of the GGC representable algorithms with that encompassed in the framework described in [22] constitutes the main contribution of this work.

  15. 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.

  16. An effective method for segmentation of MR brain images using the ant colony optimization algorithm.

    Science.gov (United States)

    Taherdangkoo, Mohammad; Bagheri, Mohammad Hadi; Yazdi, Mehran; Andriole, Katherine P

    2013-12-01

    Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area. PMID:23563793

  17. Novel real-time volumetric tool segmentation algorithm for intraoperative microscope integrated OCT (Conference Presentation)

    Science.gov (United States)

    Viehland, Christian; Keller, Brenton; Carrasco-Zevallos, Oscar; Cunefare, David; Shen, Liangbo; Toth, Cynthia; Farsiu, Sina; Izatt, Joseph A.

    2016-03-01

    Optical coherence tomography (OCT) allows for micron scale imaging of the human retina and cornea. Current generation research and commercial intrasurgical OCT prototypes are limited to live B-scan imaging. Our group has developed an intraoperative microscope integrated OCT system capable of live 4D imaging. With a heads up display (HUD) 4D imaging allows for dynamic intrasurgical visualization of tool tissue interaction and surgical maneuvers. Currently our system relies on operator based manual tracking to correct for patient motion and motion caused by the surgeon, to track the surgical tool, and to display the correct B-scan to display on the HUD. Even when tracking only bulk motion, the operator sometimes lags behind and the surgical region of interest can drift out of the OCT field of view. To facilitate imaging we report on the development of a fast volume based tool segmentation algorithm. The algorithm is based on a previously reported volume rendering algorithm and can identify both the tool and retinal surface. The algorithm requires 45 ms per volume for segmentation and can be used to actively place the B-scan across the tool tissue interface. Alternatively, real-time tool segmentation can be used to allow the surgeon to use the surgical tool as an interactive B-scan pointer.

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

    International Nuclear Information System (INIS)

    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

  19. The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

    Science.gov (United States)

    Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

    2015-05-01

    Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-01-01

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

  1. Efficient algorithms for analyzing segmental duplications with deletions and inversions in genomes

    Directory of Open Access Journals (Sweden)

    Mozes Shay

    2010-01-01

    Full Text Available Abstract Background Segmental duplications, or low-copy repeats, are common in mammalian genomes. In the human genome, most segmental duplications are mosaics comprised of multiple duplicated fragments. This complex genomic organization complicates analysis of the evolutionary history of these sequences. One model proposed to explain this mosaic patterns is a model of repeated aggregation and subsequent duplication of genomic sequences. Results We describe a polynomial-time exact algorithm to compute duplication distance, a genomic distance defined as the most parsimonious way to build a target string by repeatedly copying substrings of a fixed source string. This distance models the process of repeated aggregation and duplication. We also describe extensions of this distance to include certain types of substring deletions and inversions. Finally, we provide a description of a sequence of duplication events as a context-free grammar (CFG. Conclusion These new genomic distances will permit more biologically realistic analyses of segmental duplications in genomes.

  2. 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.

  3. 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.

  4. 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.

  5. 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. de; Santos, R.; Castro, P.; Azevedo, E.

    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...

  6. INFARCT DETECTION IN BRAIN MRI USING IMPROVED SEGMENTATION ALGORITHM AND VOLUME VISUALIZATION

    OpenAIRE

    Praveen Kumar E; Sumithra M G; Sunil Kumar P

    2013-01-01

    In the present days, for the human body anatomical study and for the treatment planning medicalscience very much depend on the medical imaging technology and medical images. Specifically for thehuman brain, MRI widely prefers and using for the imaging. But by nature medical images are complex andnoisy.This leads to the necessity of processes that reduces difficulties in analysis and improves quality ofoutput.This paper discuss about an improved segmentation algorithm for infarct detection in ...

  7. 3-D segmentation algorithm of small lung nodules in spiral CT images

    OpenAIRE

    Diciotti S; Picozzi G; Falchini M; Mascalchi M; Villari N; Valli G

    2008-01-01

    Abstract—Computed tomography (CT) is the most sensitive imaging technique for detecting lung nodules, and is now being evaluated as a screening tool for lung cancer in several large samples studies all over the world. In this report, we describe a semiautomaticmethod for 3-D segmentation of lung nodules in CT images for subsequent volume assessment. The distinguishing features of our algorithm are the following. 1) The user interaction process. It allows the introduction ...

  8. 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.

  9. Application of centerline detection and deformable contours algorithms to segmenting the carotid lumen

    Science.gov (United States)

    Hachaj, Tomasz; Ogiela, Marek R.

    2014-03-01

    The main contribution of this article is to evaluate the utility of different state-of-the-art deformable contour models for segmenting carotid lumen walls from computed tomography angiography images. We have also proposed and tested a new tracking-based lumen segmentation method based on our evaluation results. The deformable contour algorithm (snake) is used to detect the outer wall of the vessel. We have examined four different snakes: with a balloon, distance, and a gradient vector flow force and the method of active contours without edges. The algorithms were evaluated on a set of 32 artery lumens-16 from the common carotid artery (CCA)-the internal carotid artery section and 16 from the CCA-the external carotid artery section-in order to find the optimum deformable contour model for this task. Later, we evaluated different values of energy terms in the method of active contours without edges, which turned out to be the best for our dataset, in order to find the optimal values for this particular segmentation task. The choice of particular weights in the energy term was evaluated statistically. The final Dice's coefficient at the level of 0.939±0.049 puts our algorithm among the best state-of-the-art methods for these solutions.

  10. Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

    Science.gov (United States)

    Bernard, Olivier; Bosch, Johan G; Heyde, Brecht; Alessandrini, Martino; Barbosa, Daniel; Camarasu-Pop, Sorina; Cervenansky, Frederic; Valette, Sebastien; Mirea, Oana; Bernier, Michel; Jodoin, Pierre-Marc; Domingos, Jaime Santo; Stebbing, Richard V; Keraudren, Kevin; Oktay, Ozan; Caballero, Jose; Shi, Wei; Rueckert, Daniel; Milletari, Fausto; Ahmadi, Seyed-Ahmad; Smistad, Erik; Lindseth, Frank; van Stralen, Maartje; Wang, Chen; Smedby, Orjan; Donal, Erwan; Monaghan, Mark; Papachristidis, Alex; Geleijnse, Marcel L; Galli, Elena; D'hooge, Jan

    2016-04-01

    Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions. PMID:26625409

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. A variable fluence step clustering and segmentation algorithm for step and shoot IMRT

    International Nuclear Information System (INIS)

    A step and shoot sequencer was developed that can be integrated into an IMRT optimization algorithm. The method uses non-uniform fluence steps and is adopted to the constraints of an MLC. It consists of a clustering, a smoothing and a segmentation routine. The performance of the algorithm is demonstrated for eight mathematical profiles of differing complexity and two optimized profiles of a clinical prostate case. The results in terms of stability, flexibility, speed and conformity fulfil the criteria for the integration into the optimization concept. The performance of the clustering routine is compared with another previously published one (Bortfeld et al 1994 Int. J. Radiat. Oncol. Biol. Phys. 28 723-30) and yields slightly better results in terms of mean and maximum deviation between the optimized and the clustered profile. We discuss the specific attributes of the algorithm concerning its integration into the optimization concept. (author)

  16. A Marker Controlled Watershed Algorithm with Priori Shape Information for Segmentation of Clustered Nuclei

    Directory of Open Access Journals (Sweden)

    M. Mohideen Fatima Alias Niraimathi

    2011-02-01

    Full Text Available Microscopy cell image analysis is a fundamental tool for biological research. This analysis is used in studies of different aspects of cell cultures. Visual inspection of individual cells is very time consuming, insufficient to detect or describe delicate changes in cellular morphology. The main challenges in segmenting nuclei in histometry are due to the fact that the specimen is a 2-D section of a 3-D tissue sample. The 2-D sectioning can result in partially imaged nuclei, sectioning of nuclei at odd angles, and damage due to the sectioning process. Furthermore, sections have finite thickness resulting in aggregating or overlapping nuclei in planar images. Hence a set of image objects that differ considerably from the ideal of round blob-like shapes occur. Their sizes and shapes in images can be irregular. The classic methodology for cell detection is image segmentation, which is a fundamental and difficult problem in computer vision. Image segmentation is a fundamental and difficult problem in computer vision. The difficulty in automatic segmentation of images of cells is often uneven due to auto fluorescence from the tissue and fluorescence from out-of-focus objects. This unevenness makes the separation of foreground and background a non-trivial task. The intensity variations within the nuclei further complicate the segmentation as the nuclei may be split into more than one object, leading to over-segmentation. Due to the cell nuclei are often clustered, make it difficult to separate the individual nuclei. Hence an automatic segmentation of cell nuclei is an essential step in image histometry and cytometry. This paper presents a robust method to segment clustered overlapping or aggregating nuclei cells using priori information of shape markers and marking function in a watershed-like algorithm. The shape markers are extracted using adaptive H-minima transform and prior information about the usual shape of normal/pathological nuclei cells. A

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

    Science.gov (United States)

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

    2010-03-01

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

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

    International Nuclear Information System (INIS)

    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

  19. 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.

  20. 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...

  1. Anatomy-Based Algorithms for Detecting Oral Cancer Using Reflectance and Fluorescence Spectroscopy

    OpenAIRE

    McGee, Sasha; Mardirossian, Vartan; Elackattu, Alphi; Mirkovic, Jelena; Pistey, Robert; Gallagher, George; Kabani, Sadru; Yu, Chung-Chieh; Wang, Zimmern; Badizadegan, Kamran; Grillone, Gregory; Feld, Michael S.

    2009-01-01

    OBJECTIVES: We used reflectance and fluorescence spectroscopy to noninvasively and quantitatively distinguish benign from dysplastic/malignant oral lesions. We designed diagnostic algorithms to account for differences in the spectral properties among anatomic sites (gingiva, buccal mucosa, etc). METHODS: In vivo reflectance and fluorescence spectra were collected from 71 patients with oral lesions. The tissue was then biopsied and the specimen evaluated by histopathology. Quantitative par...

  2. 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.

  3. Improved MR Brain Image Segmentation Using Adaptive Gabor Filtering Scheme with Fuzzy C-Means Algorithm

    Directory of Open Access Journals (Sweden)

    P. Hari Krishnan

    2014-08-01

    Full Text Available Image segmentation is the foremost process in medical image processing. It aids the diagnostic and clinical analysis of MRI (Magnetic Resonance Imaging images that were acquired through the most complex procedures of medical diagnostics. The earliest soft computing techniques in segmenting images were carried out through Fuzzy C-Means (FCM and similar extensions of various clustering algorithms. In this paper, we introduced an innovative method that uses Gabor energy filter with adaptive features to pre-extract the information of various regions of a brain image, obtained either from a MRI or CT scanner. The noise-reduced image with blurred features was then made to undergo modifications by applying unsupervised learning methods such as FCM technique, whose output has efficient exclusion of certain strength of noise elements resulting in better classified pixels.

  4. 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

  5. 浅谈中文切词算法%A Tentative Study on Chinese Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    黎佳

    2013-01-01

    如何高效率的获取满足个性化的需求成为了新时代的一个热门话题,搜索引擎在一定程度上体现了这一点。然而在搜索引擎中,内部分词算法机制是关键环节,它的目的在于选取好的关键字。一个好的分词算法会降低用户搜索信息的时间和难度,大大提高查询信息的效率。然而目前有很多分词算法,它们的性能和效率各不相同,本文的主要研究目的是探讨目前几种比较流行分词器算法的工作机制,根据它们自身的不同特点,在准确率和召回率这两个方面来比较它们的性能,并进一步研究它们是如何处理用户关键字的。%How to efifcient access to meet the personalized needs have become a hot topic in the new era, the search engine in a certain extent, a relfection of this. However, in the search engine, the internal segmentation algorithm mechanism is the key link, it is to choose best keywords. A good segmentation algorithm can reduce the time and dififculty for users to search for information, improve the efifciency of query information greatly. However, there are a lot of word segmentation algorithms, their performance and efifciency are different, the main purpose of this study is to investigate the mechanism of several popular word segmentation algorithms, and compare the performance in the precision rate and recall rate based on different characteristics of their own, and further study on how they dispose user key.

  6. Full Object Boundary Detection by Applying Scale Invariant Features in A Region Merging Segmentation Algorithm

    Directory of Open Access Journals (Sweden)

    Reza Oji

    2012-10-01

    Full Text Available Object detection is a fundamental task in computer vision and has many applications in image processing.This paper proposes a new approach for object detection by applying scale invariant feature transform(SIFT in an automatic segmentation algorithm. SIFT is an invariant algorithm respect to scale, translationand rotation. The features are very distinct and provide stable keypoints that can be used for matching anobject in different images. At first, an object is trained with different aspects for finding best keypoints. Theobject can be recognized in the other images by using achieved keypoints. Then, a robust segmentationalgorithm is used to detect the object with full boundary based on SIFT keypoints. In segmentationalgorithm, a merging role is defined to merge the regions in image with the assistance of keypoints. Theresults show that the proposed approach is reliable for object detection and can extract object boundarywell.

  7. 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.

  8. DICOM Image Retrieval Using Novel Geometric Moments and Image Segmentation Algorithm

    Directory of Open Access Journals (Sweden)

    Amol Bhagat

    2013-09-01

    Full Text Available The Medical image database is growing day by day [1]. Most of the medical images are stored in DICOM (Digital Imaging and Communications in Medicine format. There are various categories of medical images such as CT scan, X- Ray, Ultrasound, Pathology, MRI, Microscopy, etc [2]. Physicians compare previous and current medical images associated patients to provide right treatment. Medical Imaging is a leading role in modern diagnosis. Efficient image retrieval tools are needed to retrieve the intended images from large growing medical image databases. Such tools must provide more precise retrieval results with less computational complexity. This paper compares the various techniques for DICOM medical image retrieval and shows that the proposed geometric and image segmentation based image retrieval algorithm performs better as compared to other algorithms.

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

    Science.gov (United States)

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

    2016-03-01

    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.

  10. 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.

  11. Binarization of MRI with Intensity Inhomogeneity using K- Means Clustering for Segmenting Hippocampus

    OpenAIRE

    T. Genish; Somasundaram, K

    2013-01-01

    Medical image segmentation plays a crucial role in identifying the shape and structure of human anatomy. The most widely used image segmentation algorithms are edge-based and typically rely on the intensity inhomogeneity of the image at the edges, which often fail to provide accurate segmentation results. This paper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structure in medial temporal lobe) from MRI with intensity inhomogeneity without ruining it...

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

    Directory of Open Access Journals (Sweden)

    Peter Peer

    2007-09-01

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

  13. Local pixel value collection algorithm for spot segmentation in two-dimensional gel electrophoresis research.

    Science.gov (United States)

    Peer, Peter; Corzo, Luis Galo

    2007-01-01

    Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC). The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC. PMID:18274608

  14. 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.

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

    Science.gov (United States)

    Peterson, Harold; Koshak, William J.

    2009-01-01

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

  16. 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...

  17. PHEW: a parallel segmentation algorithm for three-dimensional AMR datasets - application to structure detection in self-gravitating flows

    CERN Document Server

    Bleuler, Andreas; Carassou, Sébastien; Martizzi, Davide

    2014-01-01

    We introduce PHEW (Parallel HiErarchical Watershed), a new segmentation algorithm to detect structures in astrophysical fluid simulations, and its implementation into the adaptive mesh refinement (AMR) code ramses. PHEW works on the density field defined on the adaptive mesh, and can thus be used on the gas density or the dark matter density after a projection of the particles onto the grid. The algorithm is based on a "watershed" segmentation of the computational volume into dense regions, followed by a merging of the segmented patches based on the saddle point topology of the density field. PHEW is capable of automatically detecting connected regions above the adopted density threshold, as well as the entire set of substructures within. Our algorithm is fully parallel and uses the MPI library. We describe in great detail the parallel algorithm and perform a scaling experiment which proves the capability of phew to run efficiently on massively parallel systems.

  18. 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 ...

  19. 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 ...

  20. 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 ...

  1. Anatomy & Physiology

    Science.gov (United States)

    ... Surveillance Modules » Anatomy & Physiology Cancer Registration & Surveillance Modules Anatomy & Physiology Intro to the Human Body Body Functions & Life Process Anatomical Terminology Review ...

  2. 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.

  3. 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

  4. 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. PMID:23674455

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

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

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

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

    OpenAIRE

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

    2016-01-01

    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 algo...

  7. KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on K-Means Clustering and Graph Cut

    Directory of Open Access Journals (Sweden)

    Binmei Liang

    2014-01-01

    Full Text Available For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combines K-means clustering with graph cut. In the first stage, K-means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function, and the image is segmented according to a minimum cost multiway cut. A large number of performance evaluations are carried out, and the experimental results indicate the proposed approach is effective compared to other existing image segmentation algorithms on the Berkeley image database.

  8. 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.

  9. 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. PMID:25248211

  10. 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.

  11. 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.

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

    Science.gov (United States)

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

    2016-07-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Muehlenbruch, Georg; Das, Marco; Hohl, Christian; Wildberger, Joachim E.; Guenther, Rolf W.; Mahnken, Andreas H. [University Hospital (RWTH) Aachen, Department of Diagnostic Radiology, Aachen (Germany); Rinck, Daniel; Flohr, Thomas G. [Siemens Medical Solutions, Forchheim (Germany); Koos, Ralf; Knackstedt, Christian [University Hospital (RWTH) Aachen, Department of Cardiology, Aachen (Germany)

    2006-05-15

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

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

    International Nuclear Information System (INIS)

    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

  15. 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

  16. KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on K-Means Clustering and Graph Cut

    OpenAIRE

    Binmei Liang; Jianzhou Zhang

    2014-01-01

    For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combines K-means clustering with graph cut. In the first stage, K-means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constr...

  17. 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

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

    International Nuclear Information System (INIS)

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

  19. 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.

  20. 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

  1. 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 ...

  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. 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 ...

  4. 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....

  5. Hand Anatomy

    Science.gov (United States)

    ... Topics A-Z Videos Infographics Symptom Picker Hand Anatomy Hand Safety Fireworks Safety Lawnmower Safety Snowblower safety ... Topics A-Z Videos Infographics Symptom Picker Hand Anatomy Hand Safety Fireworks Safety Lawnmower Safety Snowblower safety ...

  6. Heart Anatomy

    Science.gov (United States)

    ... Incredible Machine Bonus poster (PDF) The Human Heart Anatomy Blood The Conduction System The Coronary Arteries The ... of the Leg Vasculature of the Torso Heart anatomy illustrations and animations for grades K-6. Heart ...

  7. 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。

  8. 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.

  9. Improving performance of computer-aided detection of pulmonary embolisms by incorporating a new pulmonary vascular-tree segmentation algorithm

    Science.gov (United States)

    Wang, Xingwei; Song, XiaoFei; Chapman, Brian E.; Zheng, Bin

    2012-03-01

    We developed a new pulmonary vascular tree segmentation/extraction algorithm. The purpose of this study was to assess whether adding this new algorithm to our previously developed computer-aided detection (CAD) scheme of pulmonary embolism (PE) could improve the CAD performance (in particular reducing false positive detection rates). A dataset containing 12 CT examinations with 384 verified pulmonary embolism regions associated with 24 threedimensional (3-D) PE lesions was selected in this study. Our new CAD scheme includes the following image processing and feature classification steps. (1) A 3-D based region growing process followed by a rolling-ball algorithm was utilized to segment lung areas. (2) The complete pulmonary vascular trees were extracted by combining two approaches of using an intensity-based region growing to extract the larger vessels and a vessel enhancement filtering to extract the smaller vessel structures. (3) A toboggan algorithm was implemented to identify suspicious PE candidates in segmented lung or vessel area. (4) A three layer artificial neural network (ANN) with the topology 27-10-1 was developed to reduce false positive detections. (5) A k-nearest neighbor (KNN) classifier optimized by a genetic algorithm was used to compute detection scores for the PE candidates. (6) A grouping scoring method was designed to detect the final PE lesions in three dimensions. The study showed that integrating the pulmonary vascular tree extraction algorithm into the CAD scheme reduced false positive rates by 16.2%. For the case based 3D PE lesion detecting results, the integrated CAD scheme achieved 62.5% detection sensitivity with 17.1 false-positive lesions per examination.

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

    Science.gov (United States)

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

    2016-05-01

    Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges. PMID:26891066

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

    Directory of Open Access Journals (Sweden)

    Li Ma

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

    Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM). PMID:26649068

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

    Science.gov (United States)

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

    2015-01-01

    Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM). PMID:26649068

  14. A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images

    OpenAIRE

    Santos, AMF; dos santos, rm; castro, pmac; Azevedo, E.; Sousa, L. de; tavares, jmrs

    2013-01-01

    A novel algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the image contrast characteristics of the lumen and bifurcation of the carotid artery in relation to other tissues and structures for their identification. The relevant ultrasound data regarding the artery presented in the input image is identified using morphologic operators and processed by an anisotropic diffusion filter for speckle noise rem...

  15. A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs

    OpenAIRE

    Dael, van, P.; Lebotsa, S.; Herremans, E.; Verboven, P.; Sijbers, J.; Opara, U.L.; Cronje, P.J.; Nicolaï, B.M.

    2016-01-01

    Abstract: Oranges and lemons can be affected by the physiological disorders granulation and endoxerosis respectively, decreasing their commercial value. X-ray radiographs provide images of the internal structure of citrus on which the disorders can be discerned. An image processing algorithm is proposed to detect these disorders on X-ray projection images and classify samples as being affected or not. The method automatically segments healthy and affected tissue, calculates a set of image fea...

  16. Development, Implementation and Evaluation of Segmentation Algorithms for the Automatic Classification of Cervical Cells

    Science.gov (United States)

    Macaulay, Calum Eric

    Cancer of the uterine cervix is one of the most common cancers in women. An effective screening program for pre-cancerous and cancerous lesions can dramatically reduce the mortality rate for this disease. In British Columbia where such a screening program has been in place for some time, 2500 to 3000 slides of cervical smears need to be examined daily. More than 35 years ago, it was recognized that an automated pre-screening system could greatly assist people in this task. Such a system would need to find and recognize stained cells, segment the images of these cells into nucleus and cytoplasm, numerically describe the characteristics of the cells, and use these features to discriminate between normal and abnormal cells. The thrust of this work was (1) to research and develop new segmentation methods and compare their performance to those in the literature, (2) to determine dependence of the numerical cell descriptors on the segmentation method used, (3) to determine the dependence of cell classification accuracy on the segmentation used, and (4) to test the hypothesis that using numerical cell descriptors one can correctly classify the cells. The segmentation accuracies of 32 different segmentation procedures were examined. It was found that the best nuclear segmentation procedure was able to correctly segment 98% of the nuclei of a 1000 and a 3680 image database. Similarly the best cytoplasmic segmentation procedure was found to correctly segment 98.5% of the cytoplasm of the same 1000 image database. Sixty-seven different numerical cell descriptors (features) were calculated for every segmented cell. On a database of 800 classified cervical cells these features when used in a linear discriminant function analysis could correctly classify 98.7% of the normal cells and 97.0% of the abnormal cells. While some features were found to vary a great deal between segmentation procedures, the classification accuracy of groups of features was found to be independent of the

  17. [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. PMID:26964234

  18. 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...

  19. 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.

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

    International Nuclear Information System (INIS)

    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.)

  1. 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.)

  2. Multi-scale criteria for the evaluation of image segmentation algorithms

    OpenAIRE

    Sylvie Philipp-Foliguet; Laurent Guigues

    2008-01-01

    This paper deals with evaluation of image segmentation methods. We start with a state-of-the art of the evaluation criteria, involving a reference segmentation or not. Based on an analysis of the main existing criteria, we propose new criteria, when no ground-truth is available. These criteria, based on an energetic formalism, take into account both the complexity of the segmented image, through the boundary length and the goodness-of-fit of an underlying model with the initial data. The main...

  3. Various Versions of K-means Clustering Algorithm for Segmentation of Microarray Image

    OpenAIRE

    D.Rama Krishna; J Harikiran; Dr.P.V.Lakshmi; Dr.K.V.Ramesh

    2013-01-01

    A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. The segmentation step of microarray i...

  4. Morphologic segmentation algorithms for extracting individual surface features from areal surface topography maps

    International Nuclear Information System (INIS)

    Areal segmentation, i.e. the partitioning of areal surface topography data into regions, has recently attracted significant research interest in surface metrology. In particular morphologic segmentation, i.e. partitioning into Maxwellian hills and dales—currently the only segmentation approach endorsed by ISO specification standards—has shown potential for capturing the salient traits of a surface, so that its surface texture can be better encoded by parameters. However, recent developments in dimensional metrology applied to structured surfaces with features of dimensions on the order of micrometres (micro-electromechanical system, microfluidics, etc), and many other studies aimed at characterizing individual features in unstructured surfaces (scratches, bumps, holes, etc), are showing the importance of segmentation for extracting localized features from areal data. In this work, morphologic segmentation is applied to a selected set of case studies of industrial relevance, involving structured, semi-structured and unstructured surfaces, where the main goal is not the assessment of surface texture, but the extraction of individual surface features. The examples are designed to provide an overview of the main advantages and issues when applying morphologic segmentation in a comprehensive set of application scenarios. (paper)

  5. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

    Science.gov (United States)

    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-08-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter. PMID:26736679

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

    Science.gov (United States)

    Lu, Chia-Feng; Guo, Wan-Yuo; Chang, Feng-Chi; Huang, Shang-Ran; Chou, Yen-Chun; Wu, Yu-Te

    2013-01-01

    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. PMID:23894386

  7. MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.

    Science.gov (United States)

    Doshi, Jimit; Erus, Guray; Ou, Yangming; Resnick, Susan M; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D; Furth, Susan; Davatzikos, Christos

    2016-02-15

    Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images. PMID:26679328

  8. Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm

    Science.gov (United States)

    Lu, Chia-Feng; Guo, Wan-Yuo; Chang, Feng-Chi; Huang, Shang-Ran; Chou, Yen-Chun; Wu, Yu-Te

    2013-01-01

    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. PMID:23894386

  9. 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.

  10. 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

    2016-04-01

    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.

  11. An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery

    Directory of Open Access Journals (Sweden)

    Aaron Judah

    2014-04-01

    Full Text Available A critical step in object-oriented geospatial analysis (OBIA is image segmentation. Segments determined from a lower-spatial resolution image can be used as the context to analyse a corresponding image at a higher-spatial resolution. Due to inherent differences in perceptions of a scene at different spatial resolutions and co-registration, segment boundaries from the low spatial resolution image need to be adjusted before being applied to the high-spatial resolution image. This is a non-trivial task due to considerations such as noise, image complexity, and determining appropriate boundaries, etc. An innovative method was developed in the study to solve this. Adjustments were executed for each boundary pixel based on the minimization of an energy function characterizing local homogeneity. It executed adjustments based on a structure which rewarded movement towards edges, and superior changes towards homogeneity. The developed method was tested on a set of Quickbird, ASTER and a lower resolution, resampled, Quickbird image, over a study area in Ontario, Canada. Results showed that the adjusted-segment boundaries obtained from the lower resolution imagery aligned well with the features in the Quickbird imagery.

  12. Comparative evaluation of image segmentation algorithms for microscopic cross-section samples

    Czech Academy of Sciences Publication Activity Database

    Beneš, Miroslav; Zitová, Barbara

    Prague : Czechoslovak Microscopy Society, 2014. s. 4183-4184. [18th International Microscopy Congress . 07.09.2014-12.09.2014, Prague] R&D Projects: GA ČR GAP103/12/2211 Institutional support: RVO:67985556 Keywords : image segmentation * performance evaluation Subject RIV: JD - Computer Applications, Robotics http:// library .utia.cas.cz/separaty/2014/ZOI/benes-0432047.pdf

  13. 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

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  15. 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....

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

    Science.gov (United States)

    Abdullah, Muhammad; Barman, Sarah A.

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Luosha Xiao

    2013-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

    Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. PMID:27190713

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

    Science.gov (United States)

    Chen, Biqing

    2011-12-01

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

  20. SAR IMAGE SEGMENTATION WITH UNKNOWN NUMBER OF CLASSES COMBINED VORONOI TESSELLATION AND RJMCMC ALGORITHM

    OpenAIRE

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

    2016-01-01

    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 distribut...

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

    International Nuclear Information System (INIS)

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

  2. A Clustering Algorithm for Ecological Stream Segment Identification from Spatially Extensive Digital Databases

    Science.gov (United States)

    Brenden, T. O.; Clark, R. D.; Wiley, M. J.; Seelbach, P. W.; Wang, L.

    2005-05-01

    Remote sensing and geographic information systems have made it possible to attribute variables for streams at increasingly detailed resolutions (e.g., individual river reaches). Nevertheless, management decisions still must be made at large scales because land and stream managers typically lack sufficient resources to manage on an individual reach basis. Managers thus require a method for identifying stream management units that are ecologically similar and that can be expected to respond similarly to management decisions. We have developed a spatially-constrained clustering algorithm that can merge neighboring river reaches with similar ecological characteristics into larger management units. The clustering algorithm is based on the Cluster Affinity Search Technique (CAST), which was developed for clustering gene expression data. Inputs to the clustering algorithm are the neighbor relationships of the reaches that comprise the digital river network, the ecological attributes of the reaches, and an affinity value, which identifies the minimum similarity for merging river reaches. In this presentation, we describe the clustering algorithm in greater detail and contrast its use with other methods (expert opinion, classification approach, regular clustering) for identifying management units using several Michigan watersheds as a backdrop.

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

    OpenAIRE

    2014-01-01

    Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions an...

  4. An image segmentation method based on accelerated Dijkstra algorithm%一种基于加速Dijkstra算法的图像分割技术

    Institute of Scientific and Technical Information of China (English)

    戴虹

    2011-01-01

    An optimal path searching algorithm called " Dijkstra Algorithm" is used for image segmentation. An accelerated Dijkstra Algorithm is presented to reduce the calculation work of the classical Dijkstra algorithm and to accelerate its operating speed. Live-Wire image segmentation method based on the accelerated Dijkstra algorithm is presented to sketch the object' s contour of interest in an image and area filling method is used to segment the object. The experimental results show that the algorithm can run image segmentation successfully and has good anti-noise ability, in addition, the algorithm has less interactive times than that of the manual segmentation method and run faster than the original live-Wire algorithm.%利用最短路径搜索算法中的Dijkstra算法进行图像分割.提出一种加速Dijkstra算法减小经典Dijkstra算法的运算量,以加快其运行速度.提出基于加速Dijkstra算法的Live-Wire图像分割方法勾画出一幅图像中感兴趣目标的轮廓并采用边界填充分割该目标.实验结果表明该算法能正确地进行图像分割,抗噪声性能好,与手工分割法相比交互次数较少,与原Live-Wire分割算法相比运行时间较短.

  5. 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.

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

    Czech Academy of Sciences Publication Activity Database

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

    2014-01-01

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

  7. 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.

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

    International Nuclear Information System (INIS)

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

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

    International Nuclear Information System (INIS)

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

  10. A novel method for retinal exudate segmentation using signal separation algorithm.

    Science.gov (United States)

    Imani, Elaheh; Pourreza, Hamid-Reza

    2016-09-01

    Diabetic retinopathy is one of the major causes of blindness in the world. Early diagnosis of this disease is vital to the prevention of visual loss. The analysis of retinal lesions such as exudates, microaneurysms and hemorrhages is a prerequisite to detect diabetic disorders such as diabetic retinopathy and macular edema in fundus images. This paper presents an automatic method for the detection of retinal exudates. The novelty of this method lies in the use of Morphological Component Analysis (MCA) algorithm to separate lesions from normal retinal structures to facilitate the detection process. In the first stage, vessels are separated from lesions using the MCA algorithm with appropriate dictionaries. Then, the lesion part of retinal image is prepared for the detection of exudate regions. The final exudate map is created using dynamic thresholding and mathematical morphologies. Performance of the proposed method is measured on the three publicly available DiaretDB, HEI-MED and e-ophtha datasets. Accordingly, the AUC of 0.961 and 0.948 and 0.937 is achieved respectively, which are greater than most of the state-of-the-art methods. PMID:27393810

  11. 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...

  12. 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...... arise. In short, I expose the regulatory anatomy of the policy landscape....

  13. 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.

  14. 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.…

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

    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

    为了满足水果采摘机器人对图像分割算法实时性和自适应性的要求,在传统演化算法的基础上,提出了一种基于蜂王交配结合精英选择、截断选择分阶段的改进演化算法对水果图像进行分割。在设计选择策略时,将迭代过程划分为前中后3个阶段,分别采用蜂王交配算法、精英选择策略和截断选择策略来进行适应值的选择,这样既保证了种群的多样性,又克服了传统演化算法局部最优、收敛过快的缺点。试验结果表明,该文提出的水果图像演化分割算法无论从稳定性、分割效果,还是全局最优收敛速度上,都明显优于传统演化算法,分割的阈值稳定在3个像素之内;与Otsu算法、贝叶斯分类算法、K均值聚类算法、模糊C均值算法等其他算法相比,水果图像演化分割算法分割效果最好,对同一幅图像进行分割得到的分割识别面积参考值最大,而且运行速度最快,平均运行时间为0.08735s,远少于其余4种算法;并能用于柑橘、荔枝、苹果等各种水果的图像分割,具有一定的通用性,达到水果采摘机器人视觉实时识别的要求,为水果图像分割及其实时获取提供了一种新的基础算法。%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

  16. 对中文分词歧义消除算法的研究%Research on the Algorithm of Eliminating Word Segmentation Ambiguity

    Institute of Scientific and Technical Information of China (English)

    谷瑞

    2015-01-01

    介绍中文分词算法的理论知识,通过介绍歧义存在的种类,分析分词结果出现歧义的必然性.提出改进"退一字组合法",实现歧义消除.在保持切分速度的前提下,提高切分的精度.为搜索引擎建立索引奠定良好的基础.%Having introduced the theory of Chinese word segmentation algorithm,this paper proposes to improve"the back one word combination"by analyzing the categories of word segmentation ambiguity and its inevitability in order to eliminate it and enhance the precision of segmentation while maintaining the segmentation rate. As a result, it lays solid foundation for search engines to establish indexes.

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

    International Nuclear Information System (INIS)

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

  18. 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.

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

    International Nuclear Information System (INIS)

    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

  20. Normal Female Reproductive Anatomy

    Science.gov (United States)

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

  1. 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 ...

  2. Normal Pancreas Anatomy

    Science.gov (United States)

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

  3. Spinal angiography. Anatomy, technique and indications

    International Nuclear Information System (INIS)

    Spinal angiography is a diagnostic modality requiring detailed knowledge of spinal vascular anatomy. The cervical spinal cord is supplied by the vertebral arteries while segmental arteries which are preserved from fetal anatomy, supply the thoracic and lumbar regions. As spinal angiography carries the risk of paraplegia the indications have to be considered very carefully. Nevertheless, spinal angiography should be performed if there is reason to suspect a spinal vascular malformation from magnetic resonance imaging (MRI). (orig.)

  4. 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

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

    Science.gov (United States)

    Xie, Yuchen; Tao, Xiaodong

    2011-03-01

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

  6. Cu and Gd co-doped BaCeO3 proton conductors: Experimental vs SEM image algorithmic-segmentation results

    International Nuclear Information System (INIS)

    Graphical abstract: - Highlights: • Novel algorithmic evaluation method of image resolution using SEM data. • Microstructure description and electrical conductivity evaluation via algorithmic segmentation of SEM images. • The method permits to divide the volume resistance and grain-boundary resistance at any temperature range. • Total conductivity and mean grain size of the ceramics as a function of Cu content. - Abstract: Segmentation algorithms for the quantitative description of the surface microstructure and the evaluation of the grain-boundary conductivity of ceramic materials surface using SEM microphotographs analysis was developed and applied in the present work for first time in barium cerate based solid solutions. To this purpose novel polycrystalline ceramic materials based on Cu and Gd co-doped BaCeO3 proton conductors exhibiting high proton conductivity have been prepared and characterized. The influence of copper oxide as a dopant on the microstructure and the electrical properties of BaCe0.9–xGd0.1CuxO3–δ (0≤x≤0.1) have been examined in detail. It is shown that the dependence of the electrical conductivity is correlated with the average oxide grain diameter variation in air at various temperatures. Moreover, it was found that the algorithmic segmentation evaluation results are in good agreement with the experimental ones, verifying the considerable contribution of the grain-boundary conductivity to the electrical transport of Cu and Gd co-doped BaCeO3 proton conductors

  7. Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

    OpenAIRE

    Minakshi Sharma; Dr.Saourabh Mukherjee

    2013-01-01

    Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and ...

  8. AMELIORATE FUZZY C-MEANS: AN AMELIORATE FUZZY C-MEANS CLUSTERING ALGORITHM FOR CT-LUNG IMAGE SEGMENTATION

    OpenAIRE

    J. Bridget Nirmala; S.Gowri

    2013-01-01

    Effective and efficient image segmentation acts as a preliminary stage for the computer-aided diagnosis of medical images. For image segmentation, many FCM-based clustering techniques have been proposed. Regrettably, the existing FCM technique does not generate accurate and standardized segmentation results. This is due to the noise present in the image as well as the random initialization of membership values for pixels. To address this issue, this study has enhanced the existing FCM techniq...

  9. Expectation Maximization Segmentation

    OpenAIRE

    Bergman, Niclas

    1998-01-01

    This report reviews the Expectation Maximization EM algorithm and applies it to the data segmentation problem yielding the Expectation Maximization Segmentation EMS algorithm The EMS algorithm requires batch processing of the data and can be applied to mode switching or jumping linear dynamical state space models The EMS algorithm consists of an optimal fusion of fixed interval Kalman smoothing and discrete optimization. The next section gives a short introduction to the EM algorithm with som...

  10. Discuss on the two algorithms of line-segments and dot-array for region judgement of the sub-satellite purview

    Science.gov (United States)

    Nie, Hao; Yang, Mingming; Zhu, Yajie; Zhang, Peng

    2015-04-01

    When satellite is flying on the orbit for special task like solar flare observation, it requires knowing if the sub-satellite purview was in the ocean area. The relative position between sub-satellite point and the coastline is varying, so the observation condition need be judged in real time according to the current orbital elements. The problem is to solve the status of the relative position between the rectangle purview and the multi connected regions formed by the base data of coastline. Usually the Cohen-Sutherland algorithm is adopted to get the status. It divides the earth map to 9 sections by the four lines extended the rectangle sides. Then the coordinate of boundary points of the connected regions in which section should be confirmed. That method traverses all the boundary points for each judgement. In this paper, two algorithms are presented. The one is based on line-segments, another is based on dot-array. And the data preprocessing and judging procedure of the two methods are focused. The peculiarity of two methods is also analyzed. The method of line-segments treats the connected regions as a set of series line segments. In order to solve the problem, the terminals' coordinates of the rectangle purview and the line segments at the same latitude are compared. The method of dot-array translates the whole map to a binary image, which can be equal to a dot array. The value set of the sequence pixels in the dot array is gained. The value of the pixels in the rectangle purview is judged to solve the problem. Those two algorithms consume lower soft resource, and reduce much more comparing times because both of them do not need traverse all the boundary points. The analysis indicates that the real-time performance and consumed resource of the two algorithms are similar for the simple coastline, but the method of dot-array is the choice when coastline is quite complicated.

  11. 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.

  12. Segmentation algorithm of muskmelon fruit with complex background%复杂背景下甜瓜果实分割算法

    Institute of Scientific and Technical Information of China (English)

    王玉德; 张学志

    2014-01-01

    为解决复杂背景下甜瓜果实与背景图像分割的问题,该文提出了一种融合颜色特征和纹理特征的图像分割算法。首先,把采集到的甜瓜果实图像从RGB色彩空间分别转换到CIELAB和HSV色彩空间,应用a*b*分量建立角度模型,根据甜瓜果实的颜色特点选取阈值并对图像作二值化处理;为降低光照分布不均匀对图像分割的影响,采用HSV空间的HS颜色分量对果实图像进行阈值分割。在以上2种色彩空间分割的基础上,融合角度模型分割和 HS 阈值分割的结果,得到基于颜色特征的分割结果。然后,再按照图像的纹理特征对图像进行分割处理,融合按照颜色特征和纹理特征的分割结果。最后,为解决分割结果中的分割误差和边缘毛刺问题,以颜色特征分割的果实区域为限定条件,对按照融合特征分割的果实区域进行约束性区域生长,得到最终的图像分割结果。为了对该文提出算法的分割效果进行检验,采用超绿阈值分割算法和归一化差异指数算法(NDI)对试验图像进行分割,3种算法的平均检出率分别为83.24%、43.12%、99.09%。对比3种分割算法的检出率和误检率,可以看出,该文提出的算法试验结果明显优于超绿阈值分割算法和归一化差异指数(NDI)分割算法。%In order to solve the problem of muskmelon fruit image segmentation under a complex background, an algorithm of image segmentation based on fusing color feature and texture feature was proposed in this paper. First, the collected muskmelon fruit images were transformed from RGB color space to CIELAB color space and HSV space respectively. According to the color characteristics of muskmelon fruit, the collected images were binarized using the threshold of angle model that was set up in using a*b*components in CIELAB color space. To reduce the influence of the uneven illumination distribution of

  13. 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.

  14. Investigation of Exact Truncated Data Image Reconstruction Algorithm on Parallel PI-Line Segments in Fan-Beam Scans

    Institute of Scientific and Technical Information of China (English)

    LI Liang; CHEN Zhiqiang; KANG Kejun; ZHANG Li

    2007-01-01

    Some novel reconstruction algorithms have recently been proposed to solve the problem of reconstruction from transverse truncated projections of fan-beam scans. This paper introduced and reformulated the exact backprojection-filtration (BPF)-type reconstruction algorithm for fan-beam scans based on Zou and Pan's work. Subsequently, a legible and implementary BPF algorithm for region-of-interest (ROI)reconstruction is presented using projection data truncated not only in angle-scanning but also in the transverse direction. The algorithm can be widely used for fan-beam full-scans, short-scans, or super-short-scans.The algorithm uses less projection data than the preceding super-short-scan. The algorithm is implemented using the Shepp-Logan phantom and some primary results are presented. Some new discoveries and implications of ROI reconstruction from truncated data are discussed, which suggests that the BPF algorithm can be used in the ROI reconstruction from truncated projections.

  15. Binarization of MRI with Intensity Inhomogeneity Using K-Means Clustering for Segmenting Hippocampus

    Directory of Open Access Journals (Sweden)

    T.Genish

    2013-03-01

    Full Text Available Medical image segmentation plays a crucial role in identifying the shape and structure of human anatomy.The most widely used image segmentation algorithms are edge-based and typically rely on the intensityinhomogeneity of the image at the edges, which often fail to provide accurate segmentation results. Thispaper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structurein medial temporal lobe from MRI with intensity inhomogeneity without ruining its boundary andstructure. The image is pre-processed using a noise filter and morphology based operations. An optimalintensity threshold is then computed using K-means clustering technique. Our method has been validatedon human brain axial MRI and found to give satisfactory performance in the presence of intensityinhomogeneity. The proposed method works well even for weak edge. Our method can be used to detectboundary for accurate segmentation of hippocampus.

  16. Binarization of MRI with Intensity Inhomogeneity using K- Means Clustering for Segmenting Hippocampus

    Directory of Open Access Journals (Sweden)

    K. Somasundaram

    2013-02-01

    Full Text Available Medical image segmentation plays a crucial role in identifying the shape and structure of human anatomy. The most widely used image segmentation algorithms are edge-based and typically rely on the intensity inhomogeneity of the image at the edges, which often fail to provide accurate segmentation results. This paper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structure in medial temporal lobe from MRI with intensity inhomogeneity without ruining its boundary and structure. The image is pre-processed using a noise filter and morphology based operations. An optimal intensity threshold is then computed using K-means clustering technique. Our method has been validated on human brain axial MRI and found to give satisfactory performance in the presence of intensity inhomogeneity. The proposed method works well even for weak edge. Our method can be used to detect boundary for accurate segmentation of hippocampus.

  17. 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.

  18. 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...

  19. 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…

  20. 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 ...

  1. 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.

  2. 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

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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. (paper)

  4. 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.

  5. An automatic 2D CAD algorithm for the segmentation of the IMT in ultrasound carotid artery images

    OpenAIRE

    Ilea, Dana E.; Whelan, Paul F.; Brown, C.; Stanton, A

    2009-01-01

    Common carotid intima-media thickness (IMT) is a reliable measure of early atherosclerosis - its accurate measurement can be used in the process of evaluating the presence and tracking the progression of disease. The aim of this study is to introduce a novel unsupervised Computer Aided Detection (CAD) algorithm that is able to identify and measure the IMT in 2D ultrasound carotid images. The developed technique relies on a suite of image processing algorithms that embeds a statistical model t...

  6. Thread Pool Size Adaptive Adjusting Algorithm Based on Segmentation%基于分段的线程池尺寸自适应调整算法

    Institute of Scientific and Technical Information of China (English)

    孙旭东; 韩江洪; 刘征宇; 解新胜

    2011-01-01

    提出一种基于分段的线程池尺寸自适应调整算法.该算法将用户请求量分为上升段、平衡段和下降段3段,根据当前用户请求数、线程数自适应调整线程池尺寸,从而满足用户需求.实验结果表明,相比基于平均数的调整算法,该算法能更好地处理并发的用户请求,响应时间更短.%This paper presents a thread pool size adaptive adjusting algorithm based on segmentation. User request quantity is divided into rising balancing and dropping section. This algorithm changes the size of the thread pool based on present request and thread in order to meet need of user requirement. Experimental results show that this algorithm can effectively process multiple concurrent requests, its response time is shorter compared with adjusting algorithm based average value.

  7. Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm

    OpenAIRE

    Lu, Chia-Feng; Guo, Wan-Yuo; Chang, Feng-Chi; Huang, Shang-Ran; Chou, Yen-Chun; Wu, Yu-Te

    2013-01-01

    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 th...

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

    OpenAIRE

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

    2012-01-01

    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...

  9. Segmentation of Color Images Based on Different Segmentation Techniques

    OpenAIRE

    Purnashti Bhosale; Aniket Gokhale

    2013-01-01

    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...

  10. 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%.

  11. A Segmentation Algorithm of OCT Image for Macula Edema%一种眼底黄斑水肿OCT图像分割方法

    Institute of Scientific and Technical Information of China (English)

    杨平; 彭清; 刘维平; 杨新

    2011-01-01

    According to the characteristics of OCT images for macula edema, we studied a method for segmentation of the macula edema. Based on the Chan-Vese model, we proposed an improved level-set algorithm. With defining the integer-valued signed function directly, the curve could evolute outward or inward by changing the inside neighboring rid points and outside neighboring grid points into each other. We realized image segmentation which is much faster than the method of Chan-Vese model and smoothness regularization. We segmented 45 images and extracted the macula edema of each image. After achieving good segmentation results, we estimated the volume of the macular edema. The method provides quantitative analytic tools for clinical diagnosis and therapy.%基于眼底黄斑部相干光断层扫描(OCT)图像提出了分割黄斑水肿的方法.根据Chan-Vese模型,采用了一种改进的水平集算法,直接定义整数值的符号函数,曲线的外扩和内缩.通过内外轮廓线上点的相互转化实现,实现了快速分割和曲线平滑.本文用该方法对眼底黄斑水肿45张断层图像进行了分割,提取了黄斑水肿区域轮廓,取得了良好的分割效果,并估算了眼底黄斑水肿的体积,为临床诊断和治疗提供了定量分析的工具.

  12. Adaptive algorithm for printed Uighur character segmentation%印刷体维吾尔文字符切分自适应算法

    Institute of Scientific and Technical Information of China (English)

    张振东; 哈力旦·阿布都热依木; 赵永霄

    2014-01-01

    To research and develop the webcam taking and translating Uighur word system and to solve the difficulties of Uighur word letter segmentation in the system ,a new algorithm based on the adaptive threshold was proposed .Aiming at the features of the Uighur word pictures taken by the webcam ,the word was exacted accurately first .Then the main part of the word was used to make the projection histogram of pixels ,the threshold of segmentation was extracted automatically .The threshold was used in segmentation at last .Experiments verify the segmentation accuracy rate of this method reaches over 96% .At the same time ,it has better adaptability for different images ,and it can promote the study of Uighur word camera translation system .%为研究开发维吾尔文摄像头取词翻译系统,解决其中维吾尔文字单词图像切分难题,提出一种印刷体维吾尔文字符自适应切分算法。针对摄像头取词图像特点,准确提取目标单词;利用维吾尔文单词基线以上的主体部分做像素积分投影,从投影结果中自动提取切分阈值;利用该阈值完成字符切分,达到自适应的效果。经过实验验证,该方法切分正确率达到了96%以上,针对不同图像具有较好的适应性,对维吾尔文摄像头取词翻译系统的研究具有促进作用。

  13. Application of Cuckoo Search Algorithm in Multi-threshold Image Segmentation%布谷鸟搜索算法在多阈值图像分割中的应用

    Institute of Scientific and Technical Information of China (English)

    柳新妮; 马苗

    2013-01-01

    穷举式搜索在寻找多个分割阈值时,计算较为复杂.为解决该问题,提出一种基于布谷鸟搜索算法的多阈值图像分割算法.以Otsu法设计适应度函数,利用布谷鸟搜索算法的并行寻优性能寻找待分割图像的最优阈值.实验结果表明,与细菌觅食算法和人工蜂群算法相比,该算法的寻优速度更快,找到的阈值质量更高.%Aiming at the problem of searching multiple thresholds by exhaustive search,a new image multi-threshold segmentation algorithm based on Cuckoo Search(CS) algorithm is proposed in this paper.This algorithm employs Otsu method as the fitness function,and uses the favorable parallel searching performance of CS algorithm to quickly and accurately find the optimal thresholds of the image to be segmented.Experimental results show that CS algorithm outforms Bacterial Foraging(BF) algorithm and Artificial Bee Colony(ABC)algorithm in terms of segmentation speed and segmentation thresholds.

  14. GPU-based relative fuzzy connectedness image segmentation

    International Nuclear Information System (INIS)

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

  15. Babel Tower, Paris and Brisbane: a tour around their influences on hepatic segmentation terminology.

    OpenAIRE

    Oscar Claudio Andriani

    2010-01-01

    Liver anatomy has always been the same. However, its interpretation has changed during time according to the development of imaging and surgery. Cantlie introduced a new concept on liver anatomy using the term ―lobe‖ in a different way. Segmental anatomy, introduced in the middle of the XXth Century has a cornerstone in Couinaud’s studies. The fact that anglosaxons followed Cantlie’s concept about right and left lobes even in segmental anatomy after the description by Goldsmith & Woodburne, w...

  16. 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...

  17. Dorsal fin anatomy (Cetacean dorsal fin Anatomy)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Cetacean dorsal fin Anatomy for ONR. Comparison within populations to ascertain phenotypic differences. Findings corroborate field observation. dorsal fin description

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

    Directory of Open Access Journals (Sweden)

    Jose Martin Carreira

    2013-04-01

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

  19. 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

    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.%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。

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

    International Nuclear Information System (INIS)

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

  1. 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....

  2. 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.

  3. 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.

  4. A Survey of Semantic Segmentation

    OpenAIRE

    Thoma, Martin

    2016-01-01

    This survey gives an overview over different techniques used for pixel-level semantic segmentation. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant papers are given. Recently published approaches with convolutional neural networks are mentioned and typical problematic situations for segmentation algorithms are examined. A taxonomy of...

  5. Improving the robustness of interventional 4D ultrasound segmentation through the use of personalized prior shape models

    Science.gov (United States)

    Barbosa, Daniel; Queirós, Sandro; Morais, Pedro; Baptista, Maria J.; Monaghan, Mark; Rodrigues, Nuno F.; D'hooge, Jan; Vilaça, João. L.

    2015-03-01

    While fluoroscopy is still the most widely used imaging modality to guide cardiac interventions, the fusion of pre-operative Magnetic Resonance Imaging (MRI) with real-time intra-operative ultrasound (US) is rapidly gaining clinical acceptance as a viable, radiation-free alternative. In order to improve the detection of the left ventricular (LV) surface in 4D ultrasound, we propose to take advantage of the pre-operative MRI scans to extract a realistic geometrical model representing the patients cardiac anatomy. This could serve as prior information in the interventional setting, allowing to increase the accuracy of the anatomy extraction step in US data. We have made use of a real-time 3D segmentation framework used in the recent past to solve the LV segmentation problem in MR and US data independently and we take advantage of this common link to introduce the prior information as a soft penalty term in the ultrasound segmentation algorithm. We tested the proposed algorithm in a clinical dataset of 38 patients undergoing both MR and US scans. The introduction of the personalized shape prior improves the accuracy and robustness of the LV segmentation, as supported by the error reduction when compared to core lab manual segmentation of the same US sequences.

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

    International Nuclear Information System (INIS)

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

  8. 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

  9. 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...

  10. Anatomy: Spotlight on Africa

    Science.gov (United States)

    Kramer, Beverley; Pather, Nalini; Ihunwo, Amadi O.

    2008-01-01

    Anatomy departments across Africa were surveyed regarding the type of curriculum and method of delivery of their medical courses. While the response rate was low, African anatomy departments appear to be in line with the rest of the world in that many have introduced problem based learning, have hours that are within the range of western medical…

  11. Robust optic nerve segmentation on clinically acquired CT

    Science.gov (United States)

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

    2014-03-01

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

  12. 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.

  13. 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 imagination. The comics were drawn on paper and then recreated with digital graphics software. More than 500 comic strips have been drawn and labeled in Korean language, and some of them have been translated into English. All comic strips can be viewed on the Department of Anatomy homepage at the Ajou University School of Medicine, Suwon, Republic of Korea. The comic strips were written and drawn by experienced anatomists, and responses from viewers have generally been favorable. These anatomy comic strips, designed to help students learn the complexities of anatomy in a straightforward and humorous way, are expected to be improved further by the authors and other interested anatomists. PMID:21634024

  14. Fast inter-mode decision algorithm for high-efficiency video coding based on similarity of coding unit segmentation and partition mode between two temporally adjacent frames

    Science.gov (United States)

    Zhong, Guo-Yun; He, Xiao-Hai; Qing, Lin-Bo; Li, Yuan

    2013-04-01

    High-efficiency video coding (HEVC) introduces a flexible hierarchy of three block structures: coding unit (CU), prediction unit (PU), and transform unit (TU), which have brought about higher coding efficiency than the current national video coding standard H.264/advanced video coding (AVC). HEVC, however, simultaneously requires higher computational complexity than H.264/AVC, although several fast inter-mode decisions were proposed in its development. To further reduce this complexity, a fast inter-mode decision algorithm is proposed based on temporal correlation. Because of the distinct difference of inter-prediction block between HEVC and H.264/AVC, in order to use the temporal correlation to speed up the inter prediction, the correlation of inter-prediction between two adjacent frames needs to be analyzed according to the structure of CU and PU in HEVC. The probabilities of all the partition modes in all sizes of CU and the similarity of CU segmentation and partition modes between two adjacent frames are tested. The correlation of partition modes between two CUs with different sizes in two adjacent frames is tested and analyzed. Based on the characteristics tested and analyzed, at most, two prior partition modes are evaluated for each level of CU, which reduces the number of rate distortion cost calculations. The simulation results show that the proposed algorithm further reduces coding time by 33.0% to 43.3%, with negligible loss in bitrate and peak signal-to-noise ratio, on the basis of the fast inter-mode decision algorithms in current HEVC reference software HM7.0.

  15. 基于混合单纯形算法的模糊均值图像分割%Fuzzy Means Image Segmentation Based on Hybridized Nelder-Mead Simplex Algorithm

    Institute of Scientific and Technical Information of China (English)

    李艳灵; 刘婷

    2012-01-01

    针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度.%Standard fuzzy C-means algorithm is sensitive to initial data, and gets in the local optimization easily. For this reason, fuzzy means image segmentation based on hybridized Nelder-Mead simplex algorithm is proposed in this paper. Nelder-Mead simplex algorithm has the virtues of rapid searching and less calculation. The virtues of particle swarm operation algorithm is strong adaptability and better global search capability. In the new algorithm, the virtues of Nelder-Mead simplex algorithm and particle swarm operation algorithm are used and the result of hybridized Nelder-Mead simplex algorithm is regarded as the input of fuzzy C-means algorithm for image segmentation. Experimental results show that new algorithm not only has higher convergence speed, but also can achieve more robust segmentation results.

  16. Image Information Mining Utilizing Hierarchical Segmentation

    Science.gov (United States)

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

    2002-01-01

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

  17. 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.

  18. Side scan sonar imagery segmentation algorithm by BEMD-improved multilayer level set model%BEMD-分层水平集侧扫声纳图像快速分割算法

    Institute of Scientific and Technical Information of China (English)

    叶秀芬; 王雷; 王天

    2012-01-01

    针对侧扫声纳图像不同区域的像素分布特点,提出了一种改进的BEMD(二维经验模态分解)-分层水平集分割算法.介绍了CV(Chan和Vese)水平集模型和分层水平集模型,利用分层水平集模型进行三类分割.为了提高分割精度,利用BEMD重新描述模型的能量函数.通过BEMD的加权参数,在不影响分割精度的前提下提高模型的抗噪性能.分析了c-均值算法与水平集算法的联系,利用改进的c-均值算法初始化水平集演化曲线,以减少迭代次数.对水平集能量函数添加惩罚项,以提高水平集演化速度.利用改进的BEMD-分层水平集分割算法进行无监督的图像分割实验并与其他算法比较,验证了该算法的抗噪性、分割的准确性和快速性.%Characteristics of different regions in side scan sonar imagery are analyzed. A kind of improved BE-MD-multilayer level set segmentation algorithm is proposed to segment sonar imagery into three categories of region. In order to improve the accuracy of segmentation and the convergence rate of the level set model, BEMD (Two-dimensional Empirical Mode Decomposition) is used to describe the model's energy function. Through BEMD weighted parameters, the anti-noise performance of the model is improved without losing the accuracy of segmentation. After analysis of the contact of the c-means algorithm and the level set algorithm, an improved c-means algorithm is used to initialize the level set evolution curve. What's more, a penalty term in the energy of level set is added to improve the evolution speed. Unsupervised sonar image segmentation experiments are done by using improved BEMD-hierarchical level set segmentation algorithm. Compared with other algorithms, the noise immunity, the segmentation accuracy and rapidity of the proposed algorithm are validated.

  19. Applied peritoneal anatomy

    International Nuclear Information System (INIS)

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

  20. 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.

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

    International Nuclear Information System (INIS)

    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.)

  2. 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…

  3. 基于Gabor小波和SVM的人脸表情识别算法%Facial Expression Recognition Algorithm Based on Gabor Wavelet Automatic Segmentation and SVM

    Institute of Scientific and Technical Information of China (English)

    陈亚雄

    2011-01-01

    针对包含表情信息的静态图像,提出基于Gabor小波和SVM的人脸表情识别算法.根据先验知识,并使用形态学和积分投影相结合定位眉毛眼睛区域,采用模板内计算均值定位嘴巴区域,自动分割出表情子区域.对分割出的表情子区域进行Gabor小波特征提取,在利用Fisher线性判别对特征进行降维,去除冗余和相关.利用支持向量机对人脸表情进行分类.用该算法在日本表情数据库上进行测试,获得了较高的识别准确率.证明了该算法的有效性.%A facial recognition algorithm based on Gabor wavelet and SVM is proposed in allusion to static image containing expression Information. The mathematical morphology combined with projection is adopted to locate the brow and eye region < and the calculating mean value in template is employed to locate the mouth region, which can segment the expression sub-regions automatically. The features of the expression sub-regions are extracted by Gabor wavelet transformation and then effective Gabor expression features are selected by Fisher linear discriminate (FLD) to deduce the dimension and redundancy of the features. The features are sent to support vector machine (SVM) to classify the different expressions. The algorithm was tested in Japanese female expression database. It can get a high precision of recognition. The feasibility of this method was verified by experiments.

  4. Anatomy of the Eye

    Science.gov (United States)

    ... Errors Scientists in the Laboratory Visual Acuity Testing Anatomy of the Eye View complete NEI image albums ... the NEI Website Manager . Department of Health and Human Services | The National Institutes of Health | USA.gov ...

  5. K-means聚类算法在民航客户细分中的应用%Application of K-means algorithm to civil aviation customer segmentation

    Institute of Scientific and Technical Information of China (English)

    杨倩倩; 生佳根; 赵海田

    2015-01-01

    This paper aims at studying the problem of customer value in customer relationship management according to current airline data warehouse. The K-means clustering algorithm is used to build an aviation customer segmentation model which makes the customers classified into three types, and the relative marketing strategies were put forward accordingly. Experimental results show that the method can highlight the differences between samples, calculating the customer value more properly, and the customer value is improved by nearly 30%.%针对客户关系管理中客户价值这一问题,通过对航空公司现有数据仓库中客户信息的分析,采用数据挖掘技术中的K-means聚类算法建立民航客户细分模型,并通过实验将民航客户细分为3类,提出了对这3类航空客户的相关营销策略。实验结果表明该方法能突出客户之间的行为特征差异,更加准确地划分客户类型,进而使得客户价值约提高30%。

  6. Image segmentation and registration algorithm to collect thoracic skeleton semilandmarks for characterization of age and sex-based thoracic morphology variation.

    Science.gov (United States)

    Weaver, Ashley A; Nguyen, Callistus M; Schoell, Samantha L; Maldjian, Joseph A; Stitzel, Joel D

    2015-12-01

    Thoracic anthropometry variations with age and sex have been reported and likely relate to thoracic injury risk and outcome. The objective of this study was to collect a large volume of homologous semilandmark data from the thoracic skeleton for the purpose of quantifying thoracic morphology variations for males and females of ages 0-100 years. A semi-automated image segmentation and registration algorithm was applied to collect homologous thoracic skeleton semilandmarks from 343 normal computed tomography (CT) scans. Rigid, affine, and symmetric diffeomorphic transformations were used to register semilandmarks from an atlas to homologous locations in the subject-specific coordinate system. Homologous semilandmarks were successfully collected from 92% (7077) of the ribs and 100% (187) of the sternums included in the study. Between 2700 and 11,000 semilandmarks were collected from each rib and sternum and over 55 million total semilandmarks were collected from all subjects. The extensive landmark data collected more fully characterizes thoracic skeleton morphology across ages and sexes. Characterization of thoracic morphology with age and sex may help explain variations in thoracic injury risk and has important implications for vulnerable populations such as pediatrics and the elderly. PMID:26496701

  7. 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。

  8. Anatomy of The Anatomy of Racial Inequality

    OpenAIRE

    Steven Raphael

    2002-01-01

    In this review, I summarize and offer thoughts about two arguments key to Glenn Loury's analysis of the anatomy of racial inequality. The first concerns the idea that many negative stereotypes held about blacks in the United States are self-fulfilling, despite little evidence of inherent differences between the races in human potential. The second argument concerns the proposition that the racial stigmatization of blacks is deeply embedded in the public consciousness and that such stigma raci...

  9. Neuron anatomy structure reconstruction based on a sliding filter

    OpenAIRE

    Luo, Gongning; Sui, Dong; Wang, Kuanquan; Chae, Jinseok

    2015-01-01

    Background Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task. Methods This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filte...

  10. 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.

  11. Segmentation of Noisy Discrete Surfaces

    OpenAIRE

    Provot, Laurent; Debled-Rennesson, Isabelle

    2008-01-01

    International audience We propose in this paper a segmentation process that can deal with noisy discrete objects. A flexible approach considering arithmetic discrete planes with a variable width is used to avoid the over-segmentation that might happen when classical segmentation algorithms based on regular discrete planes are used to decompose the surface of the object. A method to choose a seed and different segmentation strategies according to the shape of the surface are also proposed.

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

    Institute of Scientific and Technical Information of China (English)

    徐黎明; 吕继东

    2015-01-01

    For harvesting robot, fruit identification 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

  13. LAPAROSCOPIC ANATOMY OF THE EXTRAHEPATIC BILIARY TRACT

    Directory of Open Access Journals (Sweden)

    E. Târcoveanu

    2005-01-01

    Full Text Available Development of mini-invasive surgery determinates a rapid improvement in laparoscopic regional anatomy. As laparoscopy is becoming common in most surgical departments, basic laparoscopic anatomy is mandatory for all residents in general surgery. Successful general surgery starts in the anatomy laboratory. Successfully minim invasive surgery starts in the operative theatre with laparoscopic exploration. The initial laparoscopic view of the right upper quadrant demonstrates primarily the subphrenic spaces, abdominal surface of the diaphragm and diaphragmatic surface of the liver. The falciform ligament is a prominent dividing point between the left subphrenic space and the right subphrenic space. The ligamentum teres hepatis is seen in the free edge of the falciform. Upward traction on the gallbladder exposes the structures of Calot’s triangle and the hepatoduodenal ligament. The liver is divided into anatomic segments based on internal anatomy that is invisible to the laparoscopist. Surface landmarks include the falciform ligament and the gallbladder fossa. The surgical procedures performed laparoscopically currently include liver biopsy, wedge resection, fenestration of hepatic cysts, laparoscopic approach of the hidatid hepatic cyst, and atypical hepatectomy. We present the laparoscopic anatomy of extrahepatic biliary tract. Once the gallbladder is elevated, inspection reveals Hartmann’s pouch and the cystic duct. The typical angular junction of the cystic duct on the common duct actually occurs in a minority of patients and the length and course of the cystic duct are highly variable. The boundaries of Calot’s triangle are often not well seen. The cystic artery is often visible under the peritoneum as it runs along the surface of the gallbladder. The variations of the structures of the hepatoduodenal ligament may occur to injuries during laparoscopic cholecystectomy. Cholangiography increases the safety of dissection of biliary tract by

  14. [Viennese school of anatomy].

    Science.gov (United States)

    Angetter, D C

    1999-10-01

    Anatomical science played a minor role in Vienna for centuries until Gerard van Swieten, in the 18th century, recognized the importance of anatomy for medical education. In the 19th century the anatomical school at the University of Vienna development to its height. A new building and a collection of preparations attracted a large number of students. Finally, a second department of anatomy was established. Political ideologies started to affect this institution in the beginning of the 20th century. Anti-Semitism emerged and caused uproars and fights among the students of the two departments. In 1938 both were united under Eduard Pernkopf, a dedicated Nazi and chairman of the department of anatomy, Decan of the medical faculty (1938-1943) and later on President of the University of Vienna (1943-1945). He was suspected of using cadavers of executed persons for the purpose of research and education. PMID:10546321

  15. 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.

  16. An interactive anatomy dissection DVD

    OpenAIRE

    Al-Sabah, Fadel YS

    2013-01-01

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

  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. MEDICAL IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    Madhavi Latha

    2010-07-01

    Full Text Available Image segmentation is an essential but critical component in low level vision image analysis, pattern recognition, and in robotic systems. It is one of the most difficult and challenging tasks in image processing which determines the quality of the final result of the image analysis. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. Various image segmentation algorithms are discussed. Some examples in different image formats are presented and overall results discussed and compared considering different parameters.

  19. 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....

  20. Anatomy of the Brain

    Science.gov (United States)

    ... Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain Tumor Symptoms Diagnosis Types of Tumors Tumor Grade Risk Factors Brain Tumor Statistics ABTA Publications Brain Tumor ...

  1. The Anatomy Puzzle Book.

    Science.gov (United States)

    Jacob, Willis H.; Carter, Robert, III

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

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

    Directory of Open Access Journals (Sweden)

    A A Eicher

    2012-09-01

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

  3. 基于模糊ABC算法的空间域SAR图像阈值分割%SAR image threshold segmentation in spatial domain based on fuzzy artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    柳新妮; 马苗

    2012-01-01

    In order to increase the speed of SAR image segmentation based on fuzzy Artificial Bee Colony algorithm, a method of SAR image in spatial threshold segmentation is proposed. In this method, gray morphology operations are employed to reduce the inherent image noise, and the searching range is reduced on the basis of the histogram feature of the denoised image. Simultaneously, a fuzzy function is introduced in order to refine the motion of bees, and fast search the optimal threshold. Experimental results show that the proposed method is not only robust to the speckle noise in SAR images, but also superior to the segmentation methods based on Genetic algorithm or Artificial Fish Swarm algorithm in terms of segmenting speed and quality.%为提高SAR(合成孔径雷达)图像分割速度,提出一种基于模糊ABC(人工蜂群)算法的空间域SAR图像闽值分割方法.该方法利用灰度形态学算子抑制图像噪声,根据抑噪图像的直方图特征缩小阈值范围,同时引入模糊隶属度函数优化蜂的运动轨迹,快速搜索最优分割阂值.实验结果显示,该方法不仅能有效抑制可见光图像和真实SAR图像中的斑点噪声,而且分割速度与分割质量明显优于基于遗传算法和人工鱼群算法的分割方法.

  4. 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.

  5. Quantitative normal thoracic anatomy at CT.

    Science.gov (United States)

    Matsumoto, Monica M S; Udupa, Jayaram K; Tong, Yubing; Saboury, Babak; Torigian, Drew A

    2016-07-01

    Automatic anatomy recognition (AAR) methodologies for a body region require detailed understanding of the morphology, architecture, and geographical layout of the organs within the body region. The aim of this paper was to quantitatively characterize the normal anatomy of the thoracic region for AAR. Contrast-enhanced chest CT images from 41 normal male subjects, each with 11 segmented objects, were considered in this study. The individual objects were quantitatively characterized in terms of their linear size, surface area, volume, shape, CT attenuation properties, inter-object distances, size and shape correlations, size-to-distance correlations, and distance-to-distance correlations. A heat map visualization approach was used for intuitively portraying the associations between parameters. Numerous new observations about object geography and relationships were made. Some objects, such as the pericardial region, vary far less than others in size across subjects. Distance relationships are more consistent when involving an object such as trachea and bronchi than other objects. Considering the inter-object distance, some objects have a more prominent correlation, such as trachea and bronchi, right and left lungs, arterial system, and esophagus. The proposed method provides new, objective, and usable knowledge about anatomy whose utility in building body-wide models toward AAR has been demonstrated in other studies. PMID:27065241

  6. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

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

  7. EVENT SEGMENTATION

    OpenAIRE

    Zacks, Jeffrey M.; Swallow, Khena M.

    2007-01-01

    One way to understand something is to break it up into parts. New research indicates that segmenting ongoing activity into meaningful events is a core component of ongoing perception, with consequences for memory and learning. Behavioral and neuroimaging data suggest that event segmentation is automatic and that people spontaneously segment activity into hierarchically organized parts and sub-parts. This segmentation depends on the bottom-up processing of sensory features such as movement, an...

  8. The anatomy workbook

    International Nuclear Information System (INIS)

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

  9. 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.

  10. Penile Embryology and Anatomy

    OpenAIRE

    Yiee, Jenny H.; Baskin, Laurence S

    2010-01-01

    Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7thand 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile sk...

  11. Plugin procedure in segmentation and application to hyperspectral image segmentation

    OpenAIRE

    Girard, R

    2010-01-01

    In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segmentation. Hyperspectral images are images that have both spatial and spectral coherence with thousands of spectral bands on each pixel. In the proposed procedure we combine a reduction di...

  12. 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.

  13. Executions and scientific anatomy.

    Science.gov (United States)

    Dolezal, Antonín; Jelen, Karel; Stajnrtova, Olga

    2015-12-01

    The very word "anatomy" tells us about this branch's connection with dissection. Studies of anatomy have taken place for approximately 2.300 years already. Anatomy's birthplace lies in Greece and Egypt. Knowledge in this specific field of science was necessary during surgical procedures in ophthalmology and obstetrics. Embalming took place without public disapproval just like autopsies and manipulation with relics. Thus, anatomical dissection became part of later forensic sciences. Anatomical studies on humans themselves, which needed to be compared with the knowledge gained through studying procedures performed on animals, elicited public disapprobation and prohibition. When faced with a shortage of cadavers, anatomists resorted to obtaining bodies of the executed and suicide victims - since torture, public display of the mutilated body, (including anatomical autopsy), were perceived as an intensification of the death penalty. Decapitation and hanging were the main execution methods meted out for death sentences. Anatomists preferred intact bodies for dissection; hence, convicts could thus avoid torture. This paper lists examples of how this process was resolved. It concerns the manners of killing, vivisection on people in the antiquity and middle-ages, experiments before the execution and after, vivifying from seeming death, experiments with galvanizing electricity on fresh cadavers, evaluating of sensibility after guillotine execution, and making perfect anatomical preparations and publications during Nazism from fresh bodies of the executed. PMID:26859596

  14. Segmentation: Identification of consumer segments

    DEFF Research Database (Denmark)

    Høg, Esben

    2005-01-01

    It is very common to categorise people, especially in the advertising business. Also traditional marketing theory has taken in consumer segments as a favorite topic. Segmentation is closely related to the broader concept of classification. From a historical point of view, classification has its...... 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. The...... 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...

  15. 股骨近端相关影像解剖学测量研究%The research of relevant image anatomy measurements of the proximal segment of femur

    Institute of Scientific and Technical Information of China (English)

    吴炳华; 尹生江; 戴闽; 徐聪

    2015-01-01

    Objective To obtain the morphological parameters of the proximal segment of the femur in Chinese healthy senior population through three-dimensional reconstruction of CT images of the femur in order to provide the anatomical basis for the design of the proximal femoral internal fixation system. Method The CT scan data of the proximal segment of the femur of 60 Chinese healthy senior personswas obtained and the three-dimensional reconstruction of the femur was performed using the system’s default software. The bony landmarks were determined on the three-dimensional images of the normal femur. Measurement included the anatomic indexes of the lesser trochanter, neck shaft, angle, femoral offset and femoral canal, etc. Results There was no statistically significant difference of all measurement parameters between the left side and the right side (P>0.05), and length of the vertical axis of the lesser trochanter was (26.80 ± 2.53) mm and its the transverse diameter was(16.09±1.72) mm;The height and the volume of the lesser trochanter was (8.41± 1.50) mm and (727.15 ± 165.18) mm3, respectively. The minimum length of the screw used to fix the lesser trochanter of the femur was (46.77 ± 2.70) mm, and the up-dip angle was (16.83 ± 2.06)° , the neck-shaft angle was(131.42 ± 8.03)° , the range of femoral offset was(37.85 ± 7.02)mm. Conclusion It is feasible to accurately identify the anatomical landmarks on the three-dimensional reconstruction of the femur. The measurement data was beneficial to treatment of the elderly patients with intertrochanteric fractures and design of the proximal femoral internal fixation system for fixation of the lesser trochanter.%目的:目的通过CT三维重建测量正常国人老年股骨近端的相关形态参数,为设计股骨近端内固定系统提供解剖学依据。方法对60位老年国人的股骨近端进行CT扫描,利用自带的医学图像处理软件进行三维重建,标定相关解剖标志,测量指

  16. Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors.

    Directory of Open Access Journals (Sweden)

    Marija Marčan

    Full Text Available Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm and medium-sized (30 mm tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.

  17. 一种关于眼底渗血区检测的双标记双分割算法%A Testing Double-marked Double Segmentation Algorithm for Fundus Oozing Area

    Institute of Scientific and Technical Information of China (English)

    何金娇; 李巧倩; 赖永森; 詹宇艺; 徐文华

    2014-01-01

    Subhyaloid hemorrhage detection in the diagnosis of diseases is important,fundus image processing can reduce the computer doctor of duplication. But due to retinal image quality,detection algorithms,as well as the diversity and complexity of the bleed area and other factors,the current detection methods have kinds of rough and low rate of detection. It presents a new segmentation of fundus oculi lesion detection algorithm,including two segmentation and two marked extraction. Division for the first time is the simple segmentation of maximum cluster variance for image,mainly removing most of the background,extracting image segmentation tags. The second division, mainly to connect tag,the processed image is morphology for segmenting type,in order to obtain more detailed bleeding lesion area. Ex-perimental results show the effectiveness of the algorithm.%眼底出血区检测在疾病诊断中具有重要意义,计算机处理眼底图像可以减少医生的重复劳动。但由于受到眼底图像质量、检测算法,以及出血区的多样性和复杂性等因素的影响,目前的检测方法存在检测种类粗糙和检出率低的问题。文中提出一种新的眼底病灶检测分割算法,算法包括两次分割和两次标记提取。第一次分割是对图像进行简单的最大类间方差分割,主要是去除大部分的背景,提取分割得到的图像粗标记;第二次分割,主要是对形态学处理后的图像进行连通标记,进行类聚分割,以获得更细致的病变渗血区域。实验结果表明该算法是有效的。

  18. PET/CT Interpretation: Abdominal Anatomy

    International Nuclear Information System (INIS)

    Detail knowledge of abdominal anatomy is essential for accurate interpretation of oncologic PET/CT. The objective of this lecture is to provide the core knowledge and guidance about, peritoneal cavity, vessels, nodal, internal organ, especially liver segmental anatomy, and retroperitoneal spaces to nuclear medicine physicians in their interpretation of oncologic PET/CT. Peritoneal Cavity: The peritoneal spaces are easiest to recognize when there is ascites. The right subphrenic space communicates with anterior and posterior subhepatic (Morrison's) space. The left subphrenic space freely communicate with the left subhepatic space. The right and left subphrenic spaces are separated by falciform ligament and do not communicate directly. The lesser sac is the isolated peritoneal compartment between the stomach and pancreas. It communicates with the rest of the peritoneal cavity (greater sac) through the Foramen of Winslow. The right subphrenic and subhepatic spaces communicate freely with the pelvic peritoneal cavity thru the right paracolic gutter. The phrenicocolic ligament prevents free communication between the left subphrenic / subhepatic space and left paracolic gutter. Free fluid and peritoneal metastases commonly settles in pelvis as the most dependent portion of the peritoneal cavity. The small mesentery suspends the jejunum and ileum and extends obliquely from the left upper quadrant to right lower quadrant. Disease originating above the ligament is directed towards the right lower quadrant and below the ligament can spread to pelvis. The greater omentum hangs from the greater curvature of the stomach and descends in front of the abdominal viscera and serves as a fertile ground for peritoneal metastases. Vessels: The abdominal aorta descends anterior to the left side of the spine to its bifurcation at the level of the iliac crest. The normal aorta does not exceed 3 cm diameter and tapers progressively as it descends distally. The common iliac arteries

  19. Algorithmes pour la segmentation et l'amélioration de la qualité des images et des vidéos

    OpenAIRE

    Bertolino, Pascal

    2012-01-01

    Travaux sur la segmentation des images et des vidéos en vue de leur codage, indexation et interprétation ainsi que sur l'amélioration de la qualité de la restitution de ces images sur les écrans plats.

  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. Who is repeating anatomy? Trends in an undergraduate anatomy course.

    Science.gov (United States)

    Schutte, Audra F

    2016-03-01

    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, remediation rates and trends in an undergraduate anatomy course with over 400 students enrolled each semester at a large Midwestern university were identified. Demographic data was collected from spring 2004 to spring 2010, including students' age, ethnicity, major of study, class standing, college admission tests (ACT and SAT®) scores, anatomy laboratory and lecture examination scores, and final anatomy grades for each semester. Eleven percent of the students repeated the course at least once. Gender, ethnicity, major of study and SAT scores were all shown to be associated with whether or not a student would need to repeat the course. On average, students who repeated anatomy demonstrated significant improvements in lecture and laboratory scores when comparing first and second enrollments in anatomy, and therefore also saw improved final course grades in their second enrollment. These findings will aid future instructors to identify and assist at-risk students to succeed in anatomy. Instructors from other institutions may also find the results to be useful for identifying students at risk for struggling. Anat Sci Educ 9: 171-178. © 2015 American Association of Anatomists. PMID:26179910

  2. [Pandora's box of anatomy].

    Science.gov (United States)

    Weinberg, Uri; Reis, Shmuel

    2008-05-01

    Physicians in Nazi Germany were among the first to join the Nazi party and the SS, and were considered passionate and active supporters of the regime. Their actions included development and implementation of the racial theory thus legitimizing the development of the Nazi genocide plan, leadership and execution of the sterilization and euthanasia programs as well as atrocious human experimentation. Nazi law allowed the use of humans and their remains in research institutions. One of the physicians whose involvement in the Nazi regime was particularly significant was Eduard Pernkopf. He was the head of the Anatomy Institute at the University of Vienna, and later became the president of the university. Pernkopf was a member of the Nazi party, promoted the idea of "racial hygiene", and in 1938, "purified" the university from all Jews. In Pernkopfs atlas of anatomy, the illustrators expressed their sympathy to Nazism by adding Nazi symbols to their illustrations. In light of the demand stated by the "Yad Vashem" Institute, the sources of the atlas were investigated. The report, which was published in 1998, determined that Pernkopfs Anatomy Institute received almost 1400 corpses from the Gestapo's execution chambers. Copies of Pernkopfs atlas, accidentally exposed at the Rappaport School of Medicine in the Technion, led to dilemmas concerning similar works with a common background. The books initiated a wide debate in Israel and abroad, regarding ethical aspects of using information originated in Nazi crimes. Moreover, these findings are evidence of the evil to which science and medicine can give rise, when they are captured as an unshakable authority. PMID:18770971

  3. 用于中文分词的组合型歧义消解算法%COMBINATORIAL WORD SENSES DISAMBIGUATION ALGORITHM FOR CHINESE WORD SEGMENTATION

    Institute of Scientific and Technical Information of China (English)

    袁鼎荣; 李新友; 邵延振

    2011-01-01

    自动分词技术的瓶颈是切分歧义,切分歧义可分为交集型切分歧义和组合型切分歧义.以组合型歧义字段所在句子为研究对象,考察歧义字段不同切分方式所得结果与其前后搭配所得词在全文中的支持度,构造从合或从分切分支持度度量因子,依据该因子消除组合型歧义.通过样例说明和实验验证该方法可行并优于现有技术.%The bottleneck of automatic word segmentation is to segment the ambiguity of word senses, which can be divided into crossing ambiguity and combinational ambiguity of the word senses. In this paper, we took the sentence including word section with combinational ambiguity as our research object, examined the support degree of the words composed of the segmented results of ambiguous word section derived from different segmentation methods and their co-occurrence words in the text, constructed the metric factor of support degree of segmentations either in compliance to composition or to separation, the combinational ambiguity of word senses is cleared up according to the factor. The feasibility of the method and its predominance over present techniques have been illustrated by the exemplar and attested by the experiment.

  4. 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.

  5. 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.

  6. Segmental neurofibromatosis

    OpenAIRE

    Galhotra, Virat; Sheikh, Soheyl; Jindal, Sanjeev; Singla, Anshu

    2014-01-01

    Segmental neurofibromatosis is a rare disorder, characterized by neurofibromas or cafι-au-lait macules limited to one region of the body. Its occurrence on the face is extremely rare and only few cases of segmental neurofibromatosis over the face have been described so far. We present a case of segmental neurofibromatosis involving the buccal mucosa, tongue, cheek, ear, and neck on the right side of the face.

  7. 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

    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。%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.

  8. Normal cranial CT anatomy

    International Nuclear Information System (INIS)

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

  9. TEACHING ANATOMY TO UNDERGRADUATE STUDENTS

    Directory of Open Access Journals (Sweden)

    Sharadkumar Pralhad Sawant,

    2015-09-01

    Full Text Available Introduction: Anatomy is the base of medical science in India and is taught practically to all disciplines of undergraduate health sciences in the first year. It is an acknowledged fact that a basic knowledge of Anatomy is a prerequisite to learn any other branch of medicine. All medical professionals must have a basic knowledge of Anatomy so as to ensure safe medical practice. Traditionally Anatomy teaching consists of didactic lectures as well as dissections or prosections as per the requirement of the course. Lecture is defined as an oral discourse on a given subject before an audience for purpose of instruction and leaning. In the traditional method lectures were taken via chalk & board, but nowadays power point presentations are increasingly being used. To make Anatomy learning both pleasant and motivating, new methods of teaching gross anatomy are being assessed as medical colleges endeavour to find time in their curricula for new content without fore-going fundamental anatomical knowledge. This paper examines the other teaching methodologies for teaching gross anatomy. Conclusion: Proper utilization of newer technologies along with the traditional teaching methods will certainly lead to enhanced understanding of gross anatomy and will ultimately improve students’ performance.

  10. Unsupervised Image Segmentation Contest

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Mikeš, Stanislav

    Stockholm: IEEE Computer Society, 2014, s. 1484-1489. ISBN 978-1-4799-5208-3. ISSN 1051-4651. [ICPR 2014 - The 22nd International Conference on Pattern Recognition. Stockholm (SE), 24.08.2014-28.08.2014] R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : unsupervised image segmentation * color image segmentation algorithms * content-based image retrieval Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2014/RO/haindl-0431256.pdf

  11. Research on Chinese Word Segmentation Based on Bi- Direction Marching Method and Feature Selection Algorithm%基于双向匹配法和特征选择算法的中文分词技术研究

    Institute of Scientific and Technical Information of China (English)

    麦范金; 李东普; 岳晓光

    2011-01-01

    Bi-direction marching method is a traditional algorithm, which can find ambiguity but can not solve the ambiguity problem. In order to find a better solution, this paper proposes a combination method based on bidirection marching method and feature selection algorithm. Through the accumulation of corpus, a Chinese word segmentation system is designed. Experimental results show that the new Chinese word segmentation method is better than traditional methods.%传统的双向匹配算法虽然能够发现歧义现象,但是却不能解决歧义问题.为了更好地进行歧义消解,提出了一种基于双向匹配法和特征选择算法的中文分词技术,通过积累的语料库,设计并实现了一个基于两种方法的分词系统.该系统的实验结果表明,基于双向匹配法和特征选择算法的中文分词技术比传统方法的效果要好.

  12. 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.

  13. Segmental Neurofibromatosis

    Directory of Open Access Journals (Sweden)

    Yesudian Devakar

    1997-01-01

    Full Text Available Segmental neurofibromatosis is a rare variant of neurofibromatosis in which the lesions are confined to one segment or dermatome of the body. They resemble classical neurofibromas in their morphology, histopathology and electron microscopy. However, systemic associations are usually absent. We report one such case with these classical features.

  14. 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。

  15. Segmentation of Handwritten Text in Gurmukhi Script

    Directory of Open Access Journals (Sweden)

    Rajiv K. Sharma

    2008-09-01

    Full Text Available Character segmentation is an important preprocessing step for text recognition.The size and shape of characters generally play an important role in the processof segmentation. But for any optical character recognition (OCR system, thepresence of touching characters in textual as well handwritten documents furtherdecreases correct segmentation as well as recognition rate drastically. Becauseone can not control the size and shape of characters in handwritten documentsso the segmentation process for the handwritten document is too difficult. Wetried to segment handwritten text by proposing some algorithms, which wereimplemented and have shown encouraging results. Algorithms have beenproposed to segment the touching characters. These algorithms have shown areasonable improvement in segmenting the touching handwritten characters inGurmukhi script.

  16. Segmentation of complex document

    Directory of Open Access Journals (Sweden)

    Souad Oudjemia

    2014-06-01

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

  17. Image Segmentation Using Pairwise Correlation Clustering

    OpenAIRE

    H. Umamaheswari,; S. Saraswathi

    2015-01-01

    A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a p...

  18. Segmentation of phase contrast magnetic resonance imaging to study the dynamic of perimedullary cerebrospinal fluid

    International Nuclear Information System (INIS)

    Phase contrast magnetic resonance imaging allows studying quantitatively the perimedullary cerebrospinal fluid (CSF) dynamics. However, the anatomy of the subarachnoid space difficult the segmentation of CSF due to the presence of vascular structures and spinal nerves. The aim of this paper is to describe a semiautomatic segmentation method for the study of the perimedullary CSF dynamics. The process is started with a seed point within the region to analyze. The algorithm creates a correlation map, calculates a threshold value and classifies pixels of CSF combining different temporal characteristics of flow behavior as input attributes to a k-means algorithm. One observer carried out ten times the segmentation of the cervical images in 5 healthy subjects; stroke volume and area were calculated. The variability of the obtained measurements was evaluated as an estimation of the reproducibility of the method. For this the coefficient of variation was calculated. The variability of the measurements was less than 5%. The method facilitates the quantification of perimedullary CSF. Stroke volume and the area at C2C3 space and prepontine cistern were measured in 16 healthy subjects.

  19. A Segmentation Algorithm Combined with Non-local Information and Graph Cut%结合非局部信息与图割的图像分割算法

    Institute of Scientific and Technical Information of China (English)

    王涛; 纪则轩; 孙权森

    2015-01-01

    The interactive image segmentation algorithm based on graph cut segments a foreground object from its background, which has drawn great interest in image processing and computer vision. In order to further improve the accuracy of the segmentation, this paper presents an interactive segmentation algo-rithm by introducing the non-local information into graph cut framework. To model the non-local informa-tion of the image, we set a fixed-size search window for each pixel, and hence each pixel only needs to con-sider the relationships with the pixels in the search window. Instead of using intensity of each pixel, the im-age patches are utilized to compute the similarity between pixels when considering the similarities between non-local pixels. By introducing the non-local information into the graph cut framework, a new energy term combining the local and non-local information is constructed by merging the local and non-local information in the boundary term of the conventional energy function. A new set of non-local edges is added in the graph to represent the non-local information of the image. The segmentation results can be obtained by solving the mincut/maxflow algorithm. Finally, the experimental results demonstrate the effectiveness and feasibility of the proposed algorithm.%基于图割的交互式图像分割方法从图像背景中分离出前景目标,在图像处理和计算机视觉领域引起了广泛的关注。为了进一步提高分割精度,提出一种结合图像非局部信息和图割的交互式图像分割算法。在建模图像非局部信息时为每个像素点设置一个固定大小的搜索窗口,每个像素点只需考虑与搜索窗口内像素之间的关系;计算非局部像素对之间相似性时采用图像片替代像素,通过图像片之间的相似性替代像素之间的相似性,以表征图像的非局部信息;将图像非局部信息引入到图割框架中,在传统能量函数的边界项将图像的

  20. MARKET SEGMENTATION

    Directory of Open Access Journals (Sweden)

    Munaga Ramakrishna Mohan Rao

    2015-01-01

    Full Text Available Market segmentation is a marketing strategy that involves dividing a broad target market into subsets of consumers, businesses, or countries who have common needs and priorities, and then designing and implementing strategies to target them. Market segmentation strategies may be used to identify the target customers, and provide supporting data for positioning to achieve a marketing plan objective. Businesses may develop product differentiation strategies, or an undifferentiated approach, involving specific products or product lines depending on the specific demand and attributes of the target segment.

  1. [Surgery without anatomy?].

    Science.gov (United States)

    Stelzner, F

    2016-08-01

    Anatomy is the basis of all operative medicine. While this branch of scientific medicine is frequently not explicitly mentioned in surgical publications, it is nonetheless quintessential to medical education. In the era of video sequences and digitized images, surgical methods are frequently communicated in the form of cinematic documentation of surgical procedures; however, this occurs without the help of explanatory drawings or subtexts that would illustrate the underlying anatomical nomenclature, comment on fine functionally important details or even without making any mention of the surgeon. In scientific manuscripts color illustrations frequently appear in such overwhelming quantities that they resemble long arrays of trophies but fail to give detailed explanations that would aid the therapeutic translation of the novel datasets. In a similar fashion, many anatomy textbooks prefer to place emphasis on illustrations and photographs while supplying only a paucity of explanations that would foster the understanding of functional contexts and thus confuse students and practitioners alike. There is great temptation to repeat existing data and facts over and over again, while it is proportionally rare to make reference to truly original scientific discoveries. A number of examples are given in this article to illustrate how discoveries that were made even a long time ago can still contribute to scientific progress in current times. This includes the NO signaling molecules, which were first described in 1775 but were only discovered to have a pivotal role as neurotransmitters in the function of human paradoxical sphincter muscles in 2012 and 2015. Readers of scientific manuscripts often long for explanations by the numerous silent coauthors of a publication who could contribute to the main topic by adding in-depth illustrations (e. g. malignograms, evolution and involution of lymph node structures). PMID:27251482

  2. Unsupervised Texture Segmentation

    OpenAIRE

    Haindl, Michal; Mikes, Stanislav

    2008-01-01

    We discussed three efficient and robust methods for unsupervised texture segmentation with unknown number of classes based on the underlying Markovian and GM texture models and their modifications for medical mammographics and remote sensing applications, respectively. Although these algorithm use the random field type models they are fast because they use efficient recursive or pseudo-likelihood parameter estimation of the underlying texture models and therefore they are much faster than the...

  3. 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...

  4. Carpal Ligament Anatomy and Biomechanics.

    Science.gov (United States)

    Pulos, Nicholas; Bozentka, David J

    2015-08-01

    A fundamental understanding of the ligamentous anatomy of the wrist is critical for any physician attempting to treat carpal instability. The anatomy of the wrist is complex, not only because of the number of named structures and their geometry but also because of the inconsistencies in describing these ligaments. The complex anatomy of the wrist is described through a review of the carpal ligaments and their effect on normal carpal motion. Mastery of this topic facilitates the physician's understanding of the patterns of instability that are seen clinically. PMID:26205699

  5. Interacting with image hierarchies for fast and accurate object segmentation

    Science.gov (United States)

    Beard, David V.; Eberly, David H.; Hemminger, Bradley M.; Pizer, Stephen M.; Faith, R. E.; Kurak, Charles; Livingston, Mark

    1994-05-01

    Object definition is an increasingly important area of medical image research. Accurate and fairly rapid object definition is essential for measuring the size and, perhaps more importantly, the change in size of anatomical objects such as kidneys and tumors. Rapid and fairly accurate object definition is essential for 3D real-time visualization including both surgery planning and Radiation oncology treatment planning. One approach to object definition involves the use of 3D image hierarchies, such as Eberly's Ridge Flow. However, the image hierarchy segmentation approach requires user interaction in selecting regions and subtrees. Further, visualizing and comprehending the anatomy and the selected portions of the hierarchy can be problematic. In this paper we will describe the Magic Crayon tool which allows a user to define rapidly and accurately various anatomical objects by interacting with image hierarchies such as those generated with Eberly's Ridge Flow algorithm as well as other 3D image hierarchies. Preliminary results suggest that fairly complex anatomical objects can be segmented in under a minute with sufficient accuracy for 3D surgery planning, 3D radiation oncology treatment planning, and similar applications. Potential modifications to the approach for improved accuracy are summarized.

  6. Mammographic Segmentation Using WaveCluster

    OpenAIRE

    Michael Barnathan

    2012-01-01

    Segmentation of clinically relevant regions from potentially noisy images represents a significant challenge in the field of mammography. We propose novel approaches based on the WaveCluster clustering algorithm for segmenting both the breast profile in the presence of significant acquisition noise and segmenting regions of interest (ROIs) within the breast. Using prior manual segmentations performed by domain experts as ground truth data, we apply our method to 150 film mammograms with signi...

  7. Towards Perceptually Driven Segmentation Evaluation Metrics

    OpenAIRE

    Drelie Gelasca, E.; Ebrahimi, T.; Farias, M; Carli, M; Mitra, S.

    2004-01-01

    To be reliable, an automatic segmentation evaluation metric has to be validated by subjective tests. In this paper, a formal protocol for subjective tests for segmentation quality assessment is presented. The most common artifacts produced by segmentation algorithms are identified and an extensive analysis of their effects on the perceived quality is performed. A psychophysical experiment was performed to assess the quality of video with segmentation errors. The results show how an objective ...

  8. MARKET SEGMENTATION

    OpenAIRE

    Munaga Ramakrishna Mohan Rao

    2015-01-01

    Market segmentation is a marketing strategy that involves dividing a broad target market into subsets of consumers, businesses, or countries who have common needs and priorities, and then designing and implementing strategies to target them. Market segmentation strategies may be used to identify the target customers, and provide supporting data for positioning to achieve a marketing plan objective. Businesses may develop product differentiation strategies, or an undifferentiated approach, inv...

  9. Olfaction: anatomy, physiology and behavior

    OpenAIRE

    Benignus, Vernon A.; Prah, James D.

    1982-01-01

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

  10. OLFACTION: ANATOMY, PHYSIOLOGY AND BEHAVIOR

    Science.gov (United States)

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

  11. Microsurgical anatomy of the posterior circulation

    Directory of Open Access Journals (Sweden)

    Pai Balaji

    2007-01-01

    Full Text Available Context: The microsurgical anatomy of the posterior circulation is very complex and variable. Surgical approaches to this area are considered risky due to the presence of the various important blood vessels and neural structures. Aims: To document the microsurgical anatomy of the posterior circulation along with variations in the Indian population. Materials and Methods: The authors studied 25 cadaveric brain specimens. Microsurgical dissection was carried out from the vertebral arteries to the basilar artery and its branches, the basilar artery bifurcation, posterior cerebral artery and its various branches. Measurements of the outer diameters of the vertebral artery, basilar artery and posterior cerebral artery and their lengths were taken. Results: The mean diameter of the vertebral artery was 3.4 mm on the left and 2.9 mm on the right. The diameter of the basilar artery varied from 3-7 mm (mean of 4.3 mm. The length varied from 24-35 mm (mean of 24.9 mm. The basilar artery gave off paramedian and circumferential perforating arteries. The origin of the anterior inferior cerebellar artery (AICA varied from 0-21 mm (mean 10.0 mm from the vertebrobasilar junction. The diameter of the AICA varied from being hypoplastic i.e., < 0.5 mm to 2 mm (mean 1.0 mm. The superior cerebellar artery (SCA arises very close to the basilar bifurcation, in our series (1-3 mm from the basilar artery bifurcation. The diameter of the SCA varied from 0.5-2.5 mm on both sides. The posterior cerebral artery (PCA is divided into four segments. The PCA gave rise to perforators (thalamoperforators, thalamogeniculate arteries, circumflex arteries and peduncular arteries, medial posterior choroidal artery, lateral posterior choroidal artery and cortical branches. In 39 specimens the P1 segment was found to be larger than the posterior communicating artery, in six specimens it was found to be equal to the diameter of the posterior communicating artery and in five specimens it

  12. Surgical Anatomy of the Eyelids.

    Science.gov (United States)

    Sand, Jordan P; Zhu, Bovey Z; Desai, Shaun C

    2016-05-01

    Slight alterations in the intricate anatomy of the upper and lower eyelid or their underlying structures can have pronounced consequences for ocular esthetics and function. The understanding of periorbital structures and their interrelationships continues to evolve and requires consideration when performing complex eyelid interventions. Maintaining a detailed appreciation of this region is critical to successful cosmetic or reconstructive surgery. This article presents a current review of the anatomy of the upper and lower eyelid with a focus on surgical implications. PMID:27105794

  13. Anatomie et identification des bois

    OpenAIRE

    Jourez, Benoît

    2010-01-01

    Anatomie des bois Structure anatomique des résineux et des feuillus Structure de la membrane cellulaire structure submicroscopique Anatomie du bois des essences feuillues tropicales Caractères anatomiques servant à l'identification des essences Reconnaissance microscopique du bois des essences résineuses et feuillues Duramen et duraminisation Formations anormales ( bois de compression et bois de tension) Chimie du bois Composition générale Cellulose, hé...

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

    Directory of Open Access Journals (Sweden)

    Sjögren Jane

    2012-01-01

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

  15. Image segmentation approach based on cuckoo search algorithm and 2-D Fisher criterion%基于杜鹃搜索和二维Fisher准则的图像分割方法

    Institute of Scientific and Technical Information of China (English)

    叶志伟; 王明威; 刘伟; 尹宇洁

    2016-01-01

    Thresholding method is one of the most common methods for image segmentation, however thresholding methods based on 1 -D histogram are easily ruined by the noise. Thresholding based on 2 -D histogram and Fisher criterion function can overcome the shortcoming of 1-D threshold method, which has the better segmentation performance. But due to huge computation is required for 2-D Fisher criterion function its speed is very slow. Commonly used optimization methods to speed up thresholding based on 2-D Fisher criterion function like particle swarm optimization and genetic algorithm are easily to fall into the local optimum. Cuckoo search is a newly proposed meta-heuristic optimization algorithm; testing results on some benchmarks indicate that cuckoo search has better global convergence ability than particle swarm optimization and genetic algorithm. In this paper, by employing cuckoo search algorithm, a segmentation approach was proposed based on 2 -D Fisher criterion function. The experimental results show that the proposed method decreases the seeking time of optimal threshold with the basic 2 -D Fisher criterion based thresholding method, which is a well performing method and is more suitable for real-time image segmentation.%阈值法是图像分割最为常用的方法之一,然而基于一维直方图的阈值方法分割结果容易受噪声的影响.基于二维直方图的二维Fisher准则能够克服一维阈值法缺陷,具有较好的分割性能.但是二维Fisher准则阈值法在求取最优阈值时需要大量的计算,运算速度非常慢.常用的二维Fisher准则阈值优化计算方法如粒子群算法和遗传算法容易陷入局部最优.杜鹃搜索算法是新近提出的一种元启发优化算法,一些经典的函数优化问题测试结果表明杜鹃搜索算法全局寻优能力优于粒子群算法和遗传算法.在介绍杜鹃搜索算法的基础上,提出一种基于杜鹃搜索算法改进的二维Fisher准则阈值分割方法.

  16. Research of Parallel Algorithm for Image Segmentation Under Multi-core Environment%多核环境下的图像分割并行算法研究

    Institute of Scientific and Technical Information of China (English)

    刘张桥; 王成良; 焦晓军

    2011-01-01

    对多核环境下的图像分割并行算法进行研究,在基于正交小波分解的多分辨率图像锥中引入模糊C-均值(FCM)算法,采用OpenMP语言设计P-FCM多核并行模型,并给出该模型的算法实现步骤.在对初始图像数据预处理时,采用矩形块数据分割法进行图像分块,将分块后的子图像数据作为并行运算时的输入数据由主线程分给不同的处理器.实验结果表明,在处理较大图像时,该算法效率较高.%This paper studies the parallel algorithm for image segmentation under multi-core environment. It combines the multiresolution image pyramid based on the orthogonal wavelet decomposition with the Fuzzy C-means(FCM) clustering algorithm, and achieves the multi-processor and multi-threaded parallel model of P-FCM with the parallel language of OpenMP. It gives the algorithm implementation of the model. For the original image data preprocessing, the parallel method of rectangular block is used to divide the original image data into sub-rectangular data blocks, which will be used as the input data and assigned to different processor by the mater thread for the parallel computing. Experimental results show that, in dealing with the large size images, this algorithm has high efficiency.

  17. Segmentation of Indus Texts: A Dynamic Programming Approach.

    Science.gov (United States)

    Siromoney, Gift; Huq, Abdul

    1988-01-01

    Demonstrates how a dynamic programing algorithm can be developed to segment unusually long written inscriptions from the Indus Valley Civilization. Explains the problem of segmentation, discusses the dynamic programing algorithm used, and includes tables which illustrate the segmentation of the inscriptions. (GEA)

  18. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-11-10

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

  19. Bayesian segmentation of hyperspectral images

    CERN Document Server

    Mohammadpour, Adel; Mohammad-Djafari, Ali

    2007-01-01

    In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.

  20. Bayesian segmentation of hyperspectral images

    Science.gov (United States)

    Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali

    2004-11-01

    In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.

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

    International Nuclear Information System (INIS)

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

  2. Penile Embryology and Anatomy

    Directory of Open Access Journals (Sweden)

    Jenny H. Yiee

    2010-01-01

    Full Text Available Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7thand 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile skin sensation and lie within Buck's fascia. These nerves are notably absent at the 12 o'clock position. Perineal nerves supply skin sensation to the ventral shaft skin and frenulum. Cavernosal nerves lie within the corpora cavernosa and are responsible for sexual function. Paired cavernosal, dorsal, and bulbourethral arteries have extensive anastomotic connections. During erection, the cavernosal artery causes engorgement of the cavernosa, while the deep dorsal artery leads to glans enlargement. The majority of venous drainage occurs through a single, deep dorsal vein into which multiple emissary veins from the corpora and circumflex veins from the spongiosum drain. The corpora cavernosa and spongiosum are all made of spongy erectile tissue. Buck's fascia circumferentially envelops all three structures, splitting into two leaves ventrally at the spongiosum. The male urethra is composed of six parts: bladder neck, prostatic, membranous, bulbous, penile, and fossa navicularis. The urethra receives its blood supply from both proximal and distal directions.

  3. Penile embryology and anatomy.

    Science.gov (United States)

    Yiee, Jenny H; Baskin, Laurence S

    2010-01-01

    Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7th and 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile skin sensation and lie within Buck's fascia. These nerves are notably absent at the 12 o'clock position. Perineal nerves supply skin sensation to the ventral shaft skin and frenulum. Cavernosal nerves lie within the corpora cavernosa and are responsible for sexual function. Paired cavernosal, dorsal, and bulbourethral arteries have extensive anastomotic connections. During erection, the cavernosal artery causes engorgement of the cavernosa, while the deep dorsal artery leads to glans enlargement. The majority of venous drainage occurs through a single, deep dorsal vein into which multiple emissary veins from the corpora and circumflex veins from the spongiosum drain. The corpora cavernosa and spongiosum are all made of spongy erectile tissue. Buck's fascia circumferentially envelops all three structures, splitting into two leaves ventrally at the spongiosum. The male urethra is composed of six parts: bladder neck, prostatic, membranous, bulbous, penile, and fossa navicularis. The urethra receives its blood supply from both proximal and distal directions. PMID:20602076

  4. 递推人工蜂群的模糊划分熵多阈值分割算法%A Fuzzy Partition Entropy Approach for Multi-Thresholding Segmentation Based on the Recursive Artificial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    尹诗白; 赵祥模; 王卫星; 王一斌

    2012-01-01

    针对图像分割中模糊划分熵算法在多阈值选取时存在的效率低、计算重复的问题,提出了一种递推人工蜂群的模糊划分熵多阈值分割算法(RAFPEA).首先选择附加边界条件及灰度权重的隶属函数来构建图像的模糊熵模型,并将该模型中不同变量的组合计算转化为递推过程,进而保存此过程中不重复的瞬间递推值,然后引入人工蜂群算法,利用预存的递推结果来计算蜂群寻优时的个体适应度值,从而减少重复计算,达到快速寻优的目的.实验结果表明:RAFPEA的均一度与精确的穷举模糊划分熵法相同,但运行时间仅为穷举、遗传的模糊划分熵算法的5%;随着阈值数量的增加,运行时间稳定不变,在确保精度的前提下,可高效地对图像进行多阈值分割.%A new recursive artificial bee colony fuzzy partition entropy algorithm (RAFPEA) for multi-thresholding image segmentation is proposed to solve the inefficiency and repeated computation in fuzzy partition entropy approach for selecting the thresholds in the process of image segmentation. The membership functions with attached boundary conditions and gray weights are selected to build the image fuzzy entropy model. The combined computation of different variables in this model is converted to the recursive process and the no-repetitive results of the processing moments are stored. Then the artificial bee colony algorithm (ABCA) uses the stored results to calculate the fitness value of individual species in the ABCA so that the repeated calculations can be reduced and the optimal thresholds can be searched effectively. Experimental results and comparisons with common algorithms indicate that the run time accounts for 5% of ones of the fuzzy partition entropy approaches based on exhaustive algorithm and genetic algorithm. And the uniformity obtained by the proposed scheme is equivalent to the one via exhaustive search. Moreover, as the number of

  5. SKIN SEGMENTATION AND SKULL SEGMENTATION FOR MEDICAL IMAGING

    Directory of Open Access Journals (Sweden)

    Eric Yogi Tjandra

    2014-01-01

    Full Text Available In this paper aims we present tools for medical imaging applications to do skin and skull segmentation in a short time. The desired output for skin segmentation is a 3D visualization of the facial skin without any cavities or holes inside the head, while skull segmentation aims to create a 3D visualization of the skull bones. The algorithm used for skin segmentation is thresholding the image, extracting the largest connected component, and holefilling to fill the unnecessary holes. As for the skull segmentation, the process is done by removing the spines which is connected to the skull, and then extracting the largest connected component. Afterwards, mesh generation is done to produce the 3D objects from the processed images. This mesh generation process is done using the marching cubes algorithm. The testing results show that the skin and skull segmentation process will work well when there are no other objects that are connected to the skin or the skull. Skin segmentation process takes a significant amount of time, primarily caused by the holefilling process.

  6. Maximin affinity learning of image segmentation

    CERN Document Server

    Turaga, Srinivas C; Helmstaedter, Moritz; Denk, Winfried; Seung, H Sebastian

    2009-01-01

    Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are ...

  7. Multiatlas segmentation as nonparametric regression.

    Science.gov (United States)

    Awate, Suyash P; Whitaker, Ross T

    2014-09-01

    This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems. PMID:24802528

  8. Spatial positioning fuzzy C- means algorithm in segmentation of range-gated image%距离选通激光成像空间定位模糊C均值聚类分割法

    Institute of Scientific and Technical Information of China (English)

    曹忆南; 王新伟; 周燕

    2013-01-01

    针对距离选通激光成像对比度低、照度不均、图像模糊的特点,提出了一种基于空间定位的模糊C均值聚类方法( SPFCM )对目标进行分割。传统的模糊C均值聚类法存在以下缺点:一是需要预先获得目标分类数量,自适应性较差;二是对空间信息不敏感,导致目标轮廓不完整以及错误分类。针对上述缺陷,文中对传统算法进行了改进,引入了初定位的概念,首先利用最大类间方差法(Otsu法)和数学形态学工具对子目标进行初步定位,再将其形心方位信息和灰度信息融合到聚类过程中,以较短的迭代过程实现不同目标的归类。实验结果证明基于空间定位的模糊C均值聚类法可以完整、有效地对距离选通激光图像进行提取分割,处理时间优于传统FCM。%A fuzzy C- means algorithm based on spatia l positioning was proposed to do the segmentation for range-gated image, which had the feature of low contrast, uneven illumination, and blurring. Object extraction is essential in image processing, providing the basic and necessary information for other methods. Traditional FCM algorithm needs the number of classes to cluster the data, which limits its adaptability. It also lacks in sensitivity of spatial information, resulting in misclassification as well as incomplete extraction of objects. For the above defects, the traditional algorithm was improved by pre-positioning. Firstly, median filter, Otsu method, and mathematical morphology method were applied to do the initial segmentation, obtaining the centroid and grayscale information of all targets, which took very short time. Then both of the centroid and grayscale information were used in clustering process, accomplishing the classification with fewer iterations and less time consuming than traditional FCM. Experiments indicate that the the Spatial Positioning FCM (SPFCM) is effective in segmentation of range-gated image, the

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

    Science.gov (United States)

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

    2016-07-01

    Veterinary anatomy is often a source of trepidation for many students. Currently professional veterinary programs, similar to medical curricula, within the United States have no admission requirements for anatomy as a prerequisite course. The purpose of the current study was to evaluate the impact of a week-long precourse in veterinary anatomy on both objective student performance and subjective student perceptions of the precourse educational methods. Incoming first year veterinary students in the Louisiana State University School of Veterinary Medicine professional curriculum were asked to participate in a free precourse before the start of the semester, covering the musculoskeletal structures of the canine thoracic limb. Students learned the material either via dissection only, instructor-led demonstrations only, or a combination of both techniques. Outcome measures included student performance on examinations throughout the first anatomy course of the professional curriculum as compared with those who did not participate in the precourse. This study found that those who participated in the precourse did significantly better on examinations within the professional anatomy course compared with those who did not participate. Notably, this significant improvement was also identified on the examination where both groups were exposed to the material for the first time together, indicating that exposure to a small portion of veterinary anatomy can impact learning of anatomical structures beyond the immediate scope of the material previously learned. Subjective data evaluation indicated that the precourse was well received and students preferred guided learning via demonstrations in addition to dissection as opposed to either method alone. Anat Sci Educ 9: 344-356. © 2015 American Association of Anatomists. PMID:26669269

  10. Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data.

    Science.gov (United States)

    Cadotte, Adam; Cadotte, David W; Livne, Micha; Cohen-Adad, Julien; Fleet, David; Mikulis, David; Fehlings, Michael G

    2015-01-01

    Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects. PMID:26445367

  11. ANATOMY ON PATTERN RECOGNITION

    OpenAIRE

    MAYANK PARASHER; SHRUTI SHARMA; A .K. SHARMA,; J.P.Gupta

    2011-01-01

    Pattern Recognition is the science of recognizing patterns by machines. This is very wide research area as of today, because every newresearch tries to make machine as intelligent as human for recognizing patterns. Pattern recognition is an active research and an importanttrait of ‘artificial intelligence’. This review paper introduces pattern recognition, its fundamental definitions, and provides understanding of related research work. This paper presents different types of algorithms, their...

  12. FISICO: Fast Image SegmentatIon COrrection

    Science.gov (United States)

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

    2016-01-01

    Background and Purpose In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis. Methods We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images. Results Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively. PMID:27224061

  13. FISICO: Fast Image SegmentatIon COrrection.

    Directory of Open Access Journals (Sweden)

    Waldo Valenzuela

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

  14. Leveraging Colour Segmentation for Upper-Body Detection

    OpenAIRE

    Duffner, Stefan; Odobez, Jean-Marc

    2014-01-01

    This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segmented using a multi-class probabilistic colour segmentation algorithm that leverages spatial as well as colour prior distributions for different semantic object regions (skin, hair, clothing, background). These multi-class soft segmentation ...

  15. Learning Taxonomy for Text Segmentation by Formal Concept Analysis

    CERN Document Server

    Lupea, Mihaiela; Marian, Zsuzsana

    2010-01-01

    In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driven clustering of sentences. The Concept-oriented Clustering Segmentation algorithm (COCS) is based on k-means linear clustering of the sentences. Experimental results obtained using COCS algorithm are presented.

  16. Segmental neurofibromatosis

    OpenAIRE

    Yesudian Devakar; Krishnan S. G. S; Jayaraman M; Janaki V R; Yesudian Patrick

    2014-01-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region...

  17. Language Segmentation

    OpenAIRE

    Alfter, David

    2015-01-01

    Language segmentation consists in finding the boundaries where one language ends and another language begins in a text written in more than one language. This is important for all natural language processing tasks. The problem can be solved by training language models on language data. However, in the case of low- or no-resource languages, this is problematic. I therefore investigate whether unsupervised methods perform better than supervised methods when it is difficult or impossible to trai...

  18. The Anatomy of Anatomy: A Review for Its Modernization

    Science.gov (United States)

    Sugand, Kapil; Abrahams, Peter; Khurana, Ashish

    2010-01-01

    Anatomy has historically been a cornerstone in medical education regardless of nation or specialty. Until recently, dissection and didactic lectures were its sole pedagogy. Teaching methodology has been revolutionized with more reliance on models, imaging, simulation, and the Internet to further consolidate and enhance the learning experience.…

  19. Anatomy Adventure: A Board Game for Enhancing Understanding of Anatomy

    Science.gov (United States)

    Anyanwu, Emeka G.

    2014-01-01

    Certain negative factors such as fear, loss of concentration and interest in the course, lack of confidence, and undue stress have been associated with the study of anatomy. These are factors most often provoked by the unusually large curriculum, nature of the course, and the psychosocial impact of dissection. As a palliative measure, Anatomy…

  20. Anatomy adventure: a board game for enhancing understanding of anatomy.

    Science.gov (United States)

    Anyanwu, Emeka G

    2014-01-01

    Certain negative factors such as fear, loss of concentration and interest in the course, lack of confidence, and undue stress have been associated with the study of anatomy. These are factors most often provoked by the unusually large curriculum, nature of the course, and the psychosocial impact of dissection. As a palliative measure, Anatomy Adventure, a board game on anatomy was designed to reduce some of these pressures, emphasize student centered and collaborative learning styles, and add fun to the process of learning while promoting understanding and retention of the subject. To assess these objectives, 95 out of over 150 medical and dental students who expressed willingness to be part of the study were recruited and divided into a Game group and a Non-game group. A pretest written examination was given to both groups, participants in the Game group were allowed to play the game for ten days, after which a post-test examination was also given. A 20-item questionnaire rated on a three-point scale to access student's perception of the game was given to the game group. The post-test scores of the game group were significantly higher (P game counterparts. Also the post-test score of the game based group was significantly better (P game was interesting, highly informative, encouraged team work, improved their attitude, and perception to gross anatomy. PMID:23878076

  1. Entangled decision forests and their application for semantic segmentation of CT images.

    Science.gov (United States)

    Montillo, Albert; Shotton, Jamie; Winn, John; Iglesias, Juan Eugenio; Metaxas, Dimitri; Criminisi, Antonio

    2011-01-01

    This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (nonuniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency. PMID:21761656

  2. Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways.

    Science.gov (United States)

    Panser, Karin; Tirian, Laszlo; Schulze, Florian; Villalba, Santiago; Jefferis, Gregory S X E; Bühler, Katja; Straw, Andrew D

    2016-08-01

    Identifying distinct anatomical structures within the brain and developing genetic tools to target them are fundamental steps for understanding brain function. We hypothesize that enhancer expression patterns can be used to automatically identify functional units such as neuropils and fiber tracts. We used two recent, genome-scale Drosophila GAL4 libraries and associated confocal image datasets to segment large brain regions into smaller subvolumes. Our results (available at https://strawlab.org/braincode) support this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. The basis for the structural assignment is clustering of voxels based on patterns of enhancer expression. These initial clusters are agglomerated to make hierarchical predictions of structure. We applied the algorithm to central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 11 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates. PMID:27426516

  3. Segmentation of Unstructured Datasets

    Science.gov (United States)

    Bhat, Smitha

    1996-01-01

    Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis.

  4. 3D virtual table in anatomy education

    DEFF Research Database (Denmark)

    Dahl, Mads Ronald; Simonsen, Eivind Ortind

    The ‘Anatomage’ is a 3D virtual human anatomy table, with touchscreen functionality, where it is possible to upload CT-scans and digital. Learning the human anatomy terminology requires time, a very good memory, anatomy atlas, books and lectures. Learning the 3 dimensional structure, connections...

  5. Efficient threshold for volumetric segmentation

    Science.gov (United States)

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

    2015-07-01

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

  6. Facial expression recognition algorithm based on local Gabor wavelet automatic segmentation%基于自动分割的局部Gabor小波人脸表情识别算法

    Institute of Scientific and Technical Information of China (English)

    刘姗姗; 王玲

    2009-01-01

    针对包含表情信息的静态灰度图像,提出基于自动分割的局部Gabor小波人脸表情识别算法.首先使用数学形态学与积分投影相结合定位眉毛眼睛区域,采用模板内计算均值定位嘴巴区域,自动分割出表情子区域.接着,对分割出的表情子区域进行Gabor小波变换提取表情特征,再利用Fisher线性判别分析进行选择,有效地去除了表情特征的冗余性和相关性.最后利用支持向量机实现对人脸表情的分类.用该算法在日本女性表情数据库上进行测试,实现了自动化且易于实现,结果证明了该方法的有效性.%A local Gabor wavelet facial expression recognition algorithm based on automatic segmentation to the still image containing facial expression information was introduced. Firstly, mathematical morphology combined with projection was used to locate the brow and eye region, and the mouth region was located by calculating template average, which can segment the expression sub-regions automatically. Secondly, features of the expression sub-regions were extracted by Gabor wavelet transformation and then effective Gabor expression features were selected by Fisher Linear Discriminant ( FLD) analysis, removing the redundancy and relevance of expression features. Finally the features were sent to Support Vector Machine (SVM) to classify different expressions. The algorithm was tested on Japanese female facial expression database. It is easy to realize automation. The feasibility of this method has been verified by experiments.

  7. Segmentation of sows in farrowing pens

    DEFF Research Database (Denmark)

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

    2014-01-01

    and illumination changes as well as motionless foreground objects. About 97% of the segmented binary images in the validation data sets can be used to track sow behaviours, such as position, orientation and movement. The experimental results demonstrate that the proposed algorithm is able to provide a......The correct segmentation of a foreground object in video recordings is an important task for many surveillance systems. The development of an effective and practical algorithm to segment sows in grayscale video recordings captured under commercial production conditions is described. The...... segmentation algorithm combines a modified adaptive Gaussian mixture model for background subtraction with the boundaries of foreground objects, which is obtained by using dyadic wavelet transform. This algorithm can accurately extract the shapes of a sow under complex environments, such as dynamic background...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-06-15

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

  9. Unsupervised Multiresolution Image Segmentation Integrating Color and Texture

    Institute of Scientific and Technical Information of China (English)

    XINGQiang; YUANBaozong; TANGXiaofang

    2004-01-01

    Unsupervised segmentation of images is highly useful in various applications including contentbased image retrieval. A novel multiresolution image segmentation algorithm, designed to separate a focused object of interest from background automatically, is described in this paper. According to the principle of human vision system, our algorithm first searches the salient block representing object in global image domain. Then all image blocks are clustered using the feature of color moments and texture in salient block. At last the algorithm classifies the image blocks belonging to object class in high resolution. Experiment shows that our algorithm achieves better segmentation results at higher speed compared with the traditional image segmentation approach using global optimization.

  10. DAGAL: Detailed Anatomy of Galaxies

    CERN Document Server

    Knapen, Johan H

    2016-01-01

    The current IAU Symposium is closely connected to the EU-funded network DAGAL (Detailed Anatomy of Galaxies), with the final annual network meeting of DAGAL being at the core of this international symposium. In this short paper, we give an overview of DAGAL, its training activities, and some of the scientific advances that have been made under its umbrella.

  11. Soul Anatomy: A virtual cadaver

    Directory of Open Access Journals (Sweden)

    Moaz Bambi

    2014-01-01

    Full Text Available In the traditional science of medicine and medical education, teaching human anatomy in the class has always been done using human cadavers. Not only does this violate human sanctity, but according to our research, it is not adequate to provide students with the alleged educational value that it is supposed to deliver. It is very cumbersome to organise all the aspects of cadaver care. Cadavers are also very limited when it comes to controlling their structures and any benefit is almost completely altered the first time the cadaver is used (dissected, and ironically, it is very weak at delivering actual real-life scenarios of a human body to students. Virtual anatomy has been a promising solution that many are counting on. But even today, we have not found a complete solution that combines all the benefits of using human cadavers and those introduced by its technical counterparts. "Soul Anatomy" aims to do just that. It brings the best of all worlds, from a natural intuitive control system, life-like feel of organs, precise accuracy in moving and controlling bodily structures, to the smallest details of being able to show medical information overlays from various medical databases connected to the internet; thus making use of technology in teaching human anatomy by providing a modern learning experience.

  12. Market Segmentation Using Bayesian Model Based Clustering

    OpenAIRE

    Van Hattum, P.

    2009-01-01

    This dissertation deals with two basic problems in marketing, that are market segmentation, which is the grouping of persons who share common aspects, and market targeting, which is focusing your marketing efforts on one or more attractive market segments. For the grouping of persons who share common aspects a Bayesian model based clustering approach is proposed such that it can be applied to data sets that are specifically used for market segmentation. The cluster algorithm can handle very l...

  13. Generative models for image segmentation and representation

    OpenAIRE

    González Díaz, Iván

    2011-01-01

    This PhD. Thesis consists of two well differentiated parts, each of them focusing on one particular field of Computer Vision. The first part of the document considers the problem of automatically generating image segmentations in video sequences in the absence of any kind of semantic knowledge or labeled data. To that end, a blind spatio-temporal segmentation algorithm is proposed that fuses motion, color and spatial information to produce robust segmentations. The approach follows an iterati...

  14. Consistent Segmentation using a Rician Classifier

    OpenAIRE

    Roy, Snehashis; Carass, Aaron; Bazin, Pierre-Louis; Resnick, Susan; Prince, Jerry L.

    2011-01-01

    Several popular classification algorithms used to segment magnetic resonance brain images assume that the image intensities, or log-transformed intensities, satisfy a finite Gaussian mixture model. In these methods, the parameters of the mixture model are estimated and the posterior probabilities for each tissue class are used directly as soft segmentations or combined to form a hard segmentation. It is suggested and shown in this paper that a Rician mixture model fits the observed data bette...

  15. K-means聚类算法在网游客户价值分类中的应用%The Application of K-means Clustering Algorithm Based on Network Game Customer Value Segmentation

    Institute of Scientific and Technical Information of China (English)

    张予垚; 黄霞; 史书畅; 陈学东

    2014-01-01

    随着网络游戏行业个性化需求增强,细分网游客户分析客户价值成为游戏运营盈利的关键。本文基于传统K-means聚类算法,结合网游用户消费特征,从初始聚类中心选择、聚类准则和聚类中心最优化方法三方面对该算法进行优化,建立更为完整、系统、准确的客户价值分类数学模型。并开发了客户相似度计算程序,对网游数据进行仿真实验,根据聚类结果对网游客户进行分类。%With the demand for personalized network game is enhanced, the segmentation of the network game customer and the analysis of the game customer value has become a key with which the game operation can be profitable. This paper is based on the traditional K-means clustering algorithm and combined with the consumption characteristics of the game customer to optimize the algorithm by the selection of the initial clustering center, the clustering criteria and the optimum of the clustering center. And then make a set of more complete,systematic and accurate mathematical model of customer value classification. And develop a program on the calculation of customer similarity to do the simulation experiment on some network game data and make a classification of the network game customer according to the experimental results .

  16. A contrario line segment detection

    CERN Document Server

    von Gioi, Rafael Grompone

    2014-01-01

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

  17. Comparison Study of Segmentation Techniques for Brain Tumour Detection

    OpenAIRE

    D. Manju; Dr.M.SEETHA; Dr. K. Venugopala Rao

    2013-01-01

    Image segmentation plays an important role in diagnosis and treatment of diseases. Imagesegmentation locates objects and boundaries with in images and the segmentation process is stopped whenregion of interest is separated from the input image. Based on the application, region of interest may differand hence none of the segmentation algorithm satisfies the global applications. Thus segmentation stillremains a challenging area for researchers. This paper emphasis on comparison study of segment...

  18. Is thinking worthwhile? A comparison of corporate segment choice strategies.

    OpenAIRE

    Buchta, Christian; Dolnicar, Sara; Freitag, Roman; Leisch, Friedrich; Meyer, David; Mild, Andreas; Ossinger, Martina

    2003-01-01

    The field of strategic marketing has long been identified as fruitful ground for gaining competitive advantage. Ever since the market segmentation concept was introduced in the late sixties, research interest steadily increased, covering issues as e.g. which fundamental segmentation strategy is most appropriate, in which ways can segments be identified or constructed, which algorithm provides optimal data-driven segmentation solutions, which number of segments should be constructed etc.. Inte...

  19. An Evaluation of Two Mammography Segmentation Techniques

    Directory of Open Access Journals (Sweden)

    Sujata, R. B. Dubey, R. Dhiman, T. J. Singh Chugh

    2012-12-01

    Full Text Available Mammographic mass detection is an important task for early detection of breast cancer diagnosis and treatment. This is however still remains a challenging task. In this paper, we have proposed a multilevel thresholding algorithm for segmenting the tumor. This paper compares two most popular method, namely between class variance (Otsu and entropy criterion (Kapur’s methods for segmenting the tumor. Our algorithms are tested on 20 mammograms and showing promising results.

  20. Segmental neurofibromatosis.

    Science.gov (United States)

    Sobjanek, Michał; Dobosz-Kawałko, Magdalena; Michajłowski, Igor; Pęksa, Rafał; Nowicki, Roman

    2014-12-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region. A histopathologic evaluation of three excised tumors revealed neurofibromas. No neurological and ophthalmologic symptoms of neurofibromatosis were diagnosed. PMID:25610358

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

    International Nuclear Information System (INIS)

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

  2. Brookhaven segment interconnect

    International Nuclear Information System (INIS)

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

  3. 基于中文分词算法的英语学习资源查询系统研究%A Study on Information Resources of English Learning Inquires System Based on Chinese Word Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    卢保娟

    2014-01-01

    Aiming at the usage of Artificial intelligence in the field of information search,this paper having introduced the information resources of English learning inquires system based on Chinese word segmentation algorithm.The system having achieved intelligent search of the English learning which through a kind of Chinese word segmentation’s search strategy and case- based reasoning technology. The results show that this system can get the users to gain satisfying results.%针对人工智能在信息搜索领域的实际应用,本文介绍了一种基于中文分词算法的英语学习资源查询系统。该系统通过一种基于中文分词算法的搜索策略,结合事例推理技术实现对英语学习资源的智能搜索。系统测试结果表明,用户可以通过该系统搜索到所提问问题的类似事例以及解决该问题的相关知识条款。

  4. Virtual Temporal Bone Anatomy

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Background The Visible Human Project(VHP) initiated by the U.S. National Library of Medicine has drawn much attention and interests from around the world. The Visible Chinese Human (VCH) project has started in China. The current study aims at acquiring a feasible virtual methodology for reconstructing the temporal bone of the Chinese population, which may provide an accurate 3-D model of important temporal bone structures that can be used in teaching and patient care for medical scientists and clinicians. Methods A series of sectional images of the temporal bone were generated from section slices of a female cadaver head. On each sectional image, SOIs (structures of interest) were segmented by carefully defining their contours and filling their areas with certain gray scale values. The processed volume data were then inducted into the 3D Slicer software(developed by the Surgical Planning Lab at Brigham and Women's Hospital and the MIT AI Lab) for resegmentation and generation of a set of tagged images of the SOIs. 3D surface models of SOIs were then reconstructed from these images. Results The temporal bone and structures in the temporal bone, including the tympanic cavity, mastoid cells, sigmoid sinus and internal carotid artery, were successfully reconstructed. The orientation of and spatial relationship among these structures were easily visualized in the reconstructed surface models. Conclusion The 3D Slicer software can be used for 3-dimensional visualization of anatomic structures in the temporal bone, which will greatly facilitate the advance of knowledge and techniques critical for studying and treating disorders involving the temporal bone.

  5. Anatomy of a Bird

    Science.gov (United States)

    2007-12-01

    Using ESO's Very Large Telescope, an international team of astronomers [1] has discovered a stunning rare case of a triple merger of galaxies. This system, which astronomers have dubbed 'The Bird' - albeit it also bears resemblance with a cosmic Tinker Bell - is composed of two massive spiral galaxies and a third irregular galaxy. ESO PR Photo 55a/07 ESO PR Photo 55a/07 The Tinker Bell Triplet The galaxy ESO 593-IG 008, or IRAS 19115-2124, was previously merely known as an interacting pair of galaxies at a distance of 650 million light-years. But surprises were revealed by observations made with the NACO instrument attached to ESO's VLT, which peered through the all-pervasive dust clouds, using adaptive optics to resolve the finest details [2]. Underneath the chaotic appearance of the optical Hubble images - retrieved from the Hubble Space Telescope archive - the NACO images show two unmistakable galaxies, one a barred spiral while the other is more irregular. The surprise lay in the clear identification of a third, clearly separate component, an irregular, yet fairly massive galaxy that seems to be forming stars at a frantic rate. "Examples of mergers of three galaxies of roughly similar sizes are rare," says Petri Väisänen, lead author of the paper reporting the results. "Only the near-infrared VLT observations made it possible to identify the triple merger nature of the system in this case." Because of the resemblance of the system to a bird, the object was dubbed as such, with the 'head' being the third component, and the 'heart' and 'body' making the two major galaxy nuclei in-between of tidal tails, the 'wings'. The latter extend more than 100,000 light-years, or the size of our own Milky Way. ESO PR Photo 55b/07 ESO PR Photo 55b/07 Anatomy of a Bird Subsequent optical spectroscopy with the new Southern African Large Telescope, and archive mid-infrared data from the NASA Spitzer space observatory, confirmed the separate nature of the 'head', but also added

  6. Monte Carlo simulated coronary angiograms of realistic anatomy and pathology models

    Science.gov (United States)

    Kyprianou, Iacovos S.; Badal, Andreu; Badano, Aldo; Banh, Diemphuc; Freed, Melanie; Myers, Kyle J.; Thompson, Laura

    2007-03-01

    We have constructed a fourth generation anthropomorphic phantom which, in addition to the realistic description of the human anatomy, includes a coronary artery disease model. A watertight version of the NURBS-based Cardiac-Torso (NCAT) phantom was generated by converting the individual NURBS surfaces of each organ into closed, manifold and non-self-intersecting tessellated surfaces. The resulting 330 surfaces of the phantom organs and tissues are now comprised of ~5×10 6 triangles whose size depends on the individual organ surface normals. A database of the elemental composition of each organ was generated, and material properties such as density and scattering cross-sections were defined using PENELOPE. A 300 μm resolution model of a heart with 55 coronary vessel segments was constructed by fitting smooth triangular meshes to a high resolution cardiac CT scan we have segmented, and was consequently registered inside the torso model. A coronary artery disease model that uses hemodynamic properties such as blood viscosity and resistivity was used to randomly place plaque within the artery tree. To generate x-ray images of the aforementioned phantom, our group has developed an efficient Monte Carlo radiation transport code based on the subroutine package PENELOPE, which employs an octree spatial data-structure that stores and traverses the phantom triangles. X-ray angiography images were generated under realistic imaging conditions (90 kVp, 10° Wanode spectra with 3 mm Al filtration, ~5×10 11 x-ray source photons, and 10% per volume iodine contrast in the coronaries). The images will be used in an optimization algorithm to select the optimal technique parameters for a variety of imaging tasks.

  7. AUTOMATIC SEGMENTATION OF PELVIS FOR BRACHYTHERAPY OF PROSTATE.

    Science.gov (United States)

    Kardell, M; Magnusson, M; Sandborg, M; Alm Carlsson, G; Jeuthe, J; Malusek, A

    2016-06-01

    Advanced model-based iterative reconstruction algorithms in quantitative computed tomography (CT) perform automatic segmentation of tissues to estimate material properties of the imaged object. Compared with conventional methods, these algorithms may improve quality of reconstructed images and accuracy of radiation treatment planning. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis. The newly developed algorithm (MK2014) combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images. The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate. PMID:26567322

  8. Unsupervised Color-texture Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    YU Sheng-yang; ZHANG Fan; WANG Yong-gang; YANG Jie

    2008-01-01

    The measure J in J value segmentation (JSEG) falls to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.

  9. Unsupervised Segmentation Methods of TV Contents

    Directory of Open Access Journals (Sweden)

    Elie El-Khoury

    2010-01-01

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

  10. Soul Anatomy: A virtual cadaver

    OpenAIRE

    Moaz Bambi

    2014-01-01

    In the traditional science of medicine and medical education, teaching human anatomy in the class has always been done using human cadavers. Not only does this violate human sanctity, but according to our research, it is not adequate to provide students with the alleged educational value that it is supposed to deliver. It is very cumbersome to organise all the aspects of cadaver care. Cadavers are also very limited when it comes to controlling their structures and any benefit is almost comple...

  11. Ecological anatomy of ferns fronds

    Directory of Open Access Journals (Sweden)

    Nina M. Derzhavina

    2014-04-01

    Full Text Available Structural types of frond anatomy are distinguished on the basis of investigation of 30 species of homosporous ferns and with regard for literature: hydromorphic, hygromorphic, mesomorphic, subxeromorphic, and subsucculent (cryptic succulent. Following frond traits are of highest adaptive value: their area and thickness, type of mesophyll, dry weight of an area unit – specific superficial density, cellular volume, and number of cells per unit of frond area.

  12. Medical discourse in pathological anatomy.

    Science.gov (United States)

    Moskalenko, R; Tatsenko, N; Romanyuk, A; Perelomova, O; Moskalenko, Yu

    2012-05-01

    The paper is devoted to the peculiarities of medical discourse in pathological anatomy as coherent speech and as a linguistic correlate of medical practice taking into account the analysis of its strategies and tactics. The purpose of the paper is to provide a multifaceted analysis of the speech strategies and tactics of pathological anatomy discourse and ways of their implementation. The main strategies of medical discourse in pathological anatomy are an anticipating strategy, a diagnosing strategy and an explaining one. The supporting strategies are pragmatic, conversational and a rhetorical one. The pragmatic strategy is implemented through contact establishing tactics, the conversational one - with the help of control tactics, the rhetorical one - with the help of attention correction tactics. The above mentioned tactics and strategies are used in the distinguishing of major, closely interrelated strategies: "the contact strategy" (to establish contact with a patient's relatives - phatic replicas of greeting and addressing) and "the strategy of explanation" (used in the practice of a pathologist for a detailed explanation of the reasons of a patient's death). The ethic aspect of speech conduct of a doctor-pathologist is analyzed. PMID:22870841

  13. Anatomy of the infant head

    International Nuclear Information System (INIS)

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

  14. VIDEO SEGMENTATION USING A NOVEL LBP DESCRIPTOR

    Directory of Open Access Journals (Sweden)

    Zhongkun He

    2014-08-01

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

  15. Evaluation for Uncertain Image Classification and Segmentation

    CERN Document Server

    Martin, Arnaud; Arnold-Bos, Andreas

    2008-01-01

    Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

  16. Segmenting images analytically in shape space

    Science.gov (United States)

    Rathi, Yogesh; Dambreville, Samuel; Niethammer, Marc; Malcolm, James; Levitt, James; Shenton, Martha E.; Tannenbaum, Allen

    2008-03-01

    This paper presents a novel analytic technique to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert a given test volume into a binary map representation, and a novel energy functional is proposed whose minimum can be analytically computed to obtain the desired segmentation in the shape space. We compare the proposed method with the log-likelihood based energy to elucidate some key differences. Our algorithm is applied to the segmentation of brain caudate nucleus and hippocampus from MRI data, which is of interest in the study of schizophrenia and Alzheimer's disease. Our validation (we compute the Hausdorff distance and the DICE coefficient between the automatic segmentation and ground-truth) shows that the proposed algorithm is very fast, requires no initialization and outperforms the log-likelihood based energy.

  17. Natural Language Processing: Word Recognition without Segmentation.

    Science.gov (United States)

    Saeed, Khalid; Dardzinska, Agnieszka

    2001-01-01

    Discussion of automatic recognition of hand and machine-written cursive text using the Arabic alphabet focuses on an algorithm for word recognition. Describes results of testing words for recognition without segmentation and considers the algorithms' use for words of different fonts and for processing whole sentences. (Author/LRW)

  18. Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve.

    Science.gov (United States)

    Smistad, Erik; Lindseth, Frank

    2016-03-01

    The goal is to create an assistant for ultrasound- guided femoral nerve block. By segmenting and visualizing the important structures such as the femoral artery, we hope to improve the success of these procedures. This article is the first step towards this goal and presents novel real-time methods for identifying and reconstructing the femoral artery, and registering a model of the surrounding anatomy to the ultrasound images. The femoral artery is modelled as an ellipse. The artery is first detected by a novel algorithm which initializes the artery tracking. This algorithm is completely automatic and requires no user interaction. Artery tracking is achieved with a Kalman filter. The 3D artery is reconstructed in real-time with a novel algorithm and a tracked ultrasound probe. A mesh model of the surrounding anatomy was created from a CT dataset. Registration of this model is achieved by landmark registration using the centerpoints from the artery tracking and the femoral artery centerline of the model. The artery detection method was able to automatically detect the femoral artery and initialize the tracking in all 48 ultrasound sequences. The tracking algorithm achieved an average dice similarity coefficient of 0.91, absolute distance of 0.33 mm, and Hausdorff distance 1.05 mm. The mean registration error was 2.7 mm, while the average maximum error was 12.4 mm. The average runtime was measured to be 38, 8, 46 and 0.2 milliseconds for the artery detection, tracking, reconstruction and registration methods respectively. PMID:26513782

  19. Image Fusion Algorithm Based on Region Segmentation and Lifting Wavelet Transform%基于区域分割与提升小波变换的图像融合算法

    Institute of Scientific and Technical Information of China (English)

    高颖; 王阿敏; 支朋飞; 葛飞

    2014-01-01

    针对精确制导武器系统中,利用传统方法获取的融合图像使得红外目标模糊、识别率低、定位性差及不能继承可见光图像色彩特性而出现光谱扭曲与失真的现象,提出了一种基于区域分割和提升小波变换的红外与可见光图像融合方法。首先结合区域生长与边缘提取图像分割法,将红外图像背景区域与目标区域分开;其次采用像素邻域能量取大法,将红外目标区域映射到可见光背景中;最后将上步得到的融合图像与原图像进行低频加权,高频平均梯度的提升小波融合变换,防止因图像分割所形成的拼接错误而导致重要信息丢失现象。实验结果表明:融合后的图像,目标凸显,背景自然,能够达到准确定位与快速识别的目的,并对隐藏目标的检测有着重要的指导意义。%As regards precision-guided weapons systems, the fused images obtained by traditional methods give fuzzy detection , low recognition rate and poor positioning for infrared target;meanwhile they are unable to highlight the visible color characteristics;thus spectral distortion results. We present a fusion method of region-based segmen-tation and lifting wavelet transform for infrared and visible image. We do three things: (1) making the infrared background and destination areas separate with image segmentation methods of regional growth combined with edge detection;(2) using the maximum energy around pixel neighborhood to make infrared target mapped to the visible background;(3) for the fused image acquired by the step above-mentioned steps and the original images, utilizing the lifting wavelet transform about the weighted algorithm for low frequency and the average gradient for high fre-quency, thus avoiding important information being missed because of segmentation error. The experimental results and their analysis show preliminarily that:the fused image can highlight target, make background

  20. Farmland Cropping System Identification in China Based on a Sliding Segmentation Algorithm%基于滑动分割算法的我国耕地熟制识别研究

    Institute of Scientific and Technical Information of China (English)

    刘爽; 马欣; 李玉娥; 张平究

    2014-01-01

    熟制时空格局的正确识别对评估粮食产量的变化及其原因和农业发展的科学决策都有非常重要的意义,卫星遥感监测是获取区域和全国尺度熟制格局的有效手段.本文在对启发式分割算法改进的基础上行成了基于作物生长周期的滑动分割算法,并首次运用到耕地熟制的识别提取.针对NDVI时间序列曲线特征,在不引入熟制分区和物候等信息,仅以土地利用为辅助数据的前提下识别了1982-2006年我国耕地熟制格局.结果表明,本方法监测结果与统计数据和前人监测结果均呈显著相关性,为正相关关系,相关系数分别为0.77和0.93,均通过0.001水平的显著性检验;熟制的分布为区域性和复杂性并存,其空间分布规律性显著但熟制区内复杂性较明显;一熟区总体变动不明显但区域范围在逐步缩小,两熟区整体向北、向西扩展,向北扩展趋势明显,三熟区缓慢扩大并出现零星区域的北移、西移.%Multiple cropping is an important feature of China' s farmland cropping system and an important way to improve food production.Correct recognition of cropping spatio-temporal patterns is important to assess changes,understand grain yield,and the development of agriculture scientific decision-making.Here we present an improved heuristic segmentation algorithm and sliding segmentation algorithm based on crop growth cycles,and apply this to the identification of farmland cropping system extraction.NDVI time series curve characteristics analysis for the 1982-2006 cropping pattern of cultivated land in China was used.We found that the statistical check between monitoring results,statistical data and previous monitoring were positively correlated; the correlation coefficients were 0.77 and 0.93.The distribution of cropping systems is regional and complex,the space distribution of the main cropping system is regular,but the cropping system shows complexity within the cropping area