CAnat: An algorithm for the automatic segmentation of anatomy of medical images
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.
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
Fusion of motion segmentation algorithms
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.
Automatic segmentation of intra-cochlear anatomy in post-implantation CT
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.
Enhanced Segment Compression Steganographic Algorithm
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.
The Watershed Algorithm for Image Segmentation
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.
Speech Segmentation Algorithm Based On Fuzzy Memberships
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...
An algorithm for segmenting range imagery
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.
Visual-hint Boundary to Segment Algorithm for Image Segmentation
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.
Interactive segmentation techniques algorithms and performance evaluation
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
Heart region segmentation from low-dose CT scans: an anatomy based approach
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.
Uniform wire segmentation algorithm of distributed interconnects
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.
Analysis of Image Segmentation Algorithms Using MATLAB
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.
Linac design algorithm with symmetric segments
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
Fusion of Image Segmentation Algorithms using Consensus Clustering
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...
A Review of Retinal Vessel Segmentation Techniques and Algorithms
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...
Segmentation precision of abdominal anatomy for MRI-based radiotherapy
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
Segmentation of Medical Image using Clustering and Watershed Algorithms
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...
A Framework for Evaluating Video Object Segmentation Algorithms
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...
A new algorithm of brain volume contours segmentation
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.
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%.
A New Image Segmentation Algorithm and It’s Application in lettuce object segmentation
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.
A Survey of Image Segmentation Algorithms Based On Fuzzy Clustering
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...
3D automatic anatomy segmentation based on iterative graph-cut-ASM
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
3D automatic anatomy segmentation based on iterative graph-cut-ASM
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
An optimization algorithm for volumetrically segmented aperture-based IMRT
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
Video segmentation using multiple features based on EM algorithm
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.
CT segmentation of dental shapes by anatomy-driven reformation imaging and B-spline modelling.
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
Illumination Compensation Algorithm for Unevenly Lighted Document Segmentation
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.
A Hierarchical Algorithm for Multiphase Texture Image Segmentation
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...
3D automatic anatomy segmentation based on iterative graph-cut-ASM
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...
Analysis of image thresholding segmentation algorithms based on swarm intelligence
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.
Gaussian Kernel Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
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.
IMPROVED RANDOMIZED ALGORITHM FOR THE EQUIVALENT 2-CATALOG SEGMENTATION PROBLEM
无
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.
TCP smart framing: a segmentation algorithm to reduce TCP latency
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 ...
Fuzzy Clustering Algorithms for Effective Medical Image Segmentation
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...
Improved document image segmentation algorithm using multiresolution morphology
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.
Research of the multimodal brain-tumor segmentation algorithm
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.
COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES
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.
Segmentation of kidney using C-V model and anatomy priors
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.
A Review of Retinal Vessel Segmentation Techniques and Algorithms
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.
An Improved FCM Medical Image Segmentation Algorithm Based on MMTD
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.
Comparison of fuzzy connectedness and graph cut segmentation algorithms
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.
Efficient Algorithms for Segmentation of Item-Set Time Series
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.
Automated lung segmentation algorithm for CAD system of thoracic CT
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.
An Improved Image Segmentation Based on Mean Shift Algorithm
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.
CT examination of segmental liver transplants from living donors. Anatomy and pathological findings
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.)
Yarn image segmentation using the region growing algorithm
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
Segmentation algorithm for non-stationary compound Poisson processes
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...
Joint graph cut and relative fuzzy connectedness image segmentation algorithm
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...
Color Image Segmentation via Improved K-Means Algorithm
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.
Segmentation algorithm for non-stationary compound Poisson processes
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.
A Multi-Stage Algorithm for Enhanced XRay Image Segmentation
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.
Multi-agent Remote Sensing Image Segmentation Algorithm
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
Autonomous Image Segmentation using Density-Adaptive Dendritic Cell Algorithm
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.
An enhanced fast scanning algorithm for image segmentation
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.
A statistical learning algorithm for word segmentation
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.
Modeling and segmentation of intra-cochlear anatomy in conventional CT
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.
An Efficient Character Segmentation Based on VNP Algorithm
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.
FCM Clustering Algorithms for Segmentation of Brain MR Images
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.
Towards an automatic coronary artery segmentation algorithm.
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
Mammographic images segmentation based on chaotic map clustering algorithm
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
A novel algorithm for segmentation of brain MR images
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)
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.
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.
Split Bregman's algorithm for three-dimensional mesh segmentation
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.
Object Recognition Algorithm Utilizing Graph Cuts Based Image Segmentation
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
A hybrid lung and vessel segmentation algorithm for computer aided detection of pulmonary embolism
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.
A comparative study of Image Region-Based Segmentation Algorithms
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.
FCM Algorithm for Medical Image Segmentation Using HMRF.
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...
Joint graph cut and relative fuzzy connectedness image segmentation algorithm.
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
WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION
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.
An Improved Image Segmentation Algorithm Based on MET Method
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.
Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm
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
Simulated annealing spectral clustering algorithm for image segmentation
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.
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
An Improved Convexity Based Segmentation Algorithm for Heavily Camouflaged Images
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.
Crowdsourcing the creation of image segmentation algorithms for connectomics
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.
A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm
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) ...
Sampling protein conformations using segment libraries and a genetic algorithm
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
Self-adaptive algorithm for segmenting skin regions
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.
Bladder segmentation in MR images with watershed segmentation and graph cut algorithm
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.
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
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
Page Segmentation using XY Cut Algorithm in OCR Systems - A Review
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.
CSHURI - Modified HURI algorithm for Customer Segmentation and Transaction Profitability
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 ...
Efficient Active Contour and K-Means Algorithms in Image Segmentation
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, ...
A modified region growing algorithm for multi-colored image object segmentation
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.
New two-dimensional fuzzy C-means clustering algorithm for image segmentation
无
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.
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.
Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness
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.
Categorizing segmentation quality using a quantitative quality assurance algorithm
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.
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...
Anatomy of the ostia venae hepaticae and the retrohepatic segment of the inferior vena cava.
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.
Anatomy of the ostia venae hepaticae and the retrohepatic segment of the inferior vena cava.
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
Using Quadtree Algorithm for Improving Fuzzy C-means Method in Image Segmentation
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
Wound size measurement of lower extremity ulcers using segmentation algorithms
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.
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...
Integrating Real-Time Analysis With The Dendritic Cell Algorithm Through Segmentation
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...
Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term
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...
Segmentation of Handwritten Chinese Character Strings Based on improved Algorithm Liu
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.
An Efficient Character Segmentation Based on VNP Algorithm
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...
Objective Performance Evaluation of Video Segmentation Algorithms with Ground-Truth
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.
An efficient Video Segmentation Algorithm with Real time Adaptive Threshold Technique
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.
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)
AN INTELLIGENT SEGMENTATION ALGORITHM FOR MICROARRAY IMAGE PROCESSING
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.
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)
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.
The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce
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.
Flame Image Segmentation Based on the Bee Colony Algorithm with Characteristics of Levy Flights
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.
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.
Segmentation of Mushroom and Cap width Measurement using Modified K-Means Clustering Algorithm
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.
A Class Of Iterative Thresholding Algorithms For Real-Time Image Segmentation
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.
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...
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: \
Automatic tuning of MST segmentation of mammograms for registration and mass detection algorithms
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.
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.
PRESEE: an MDL/MML algorithm to time-series stream segmenting.
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
Is STAPLE algorithm confident to assess segmentation methods in PET imaging?
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.
Is STAPLE algorithm confident to assess segmentation methods in PET imaging?
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
PREPAID TELECOM CUSTOMERS SEGMENTATION USING THE K-MEAN ALGORITHM
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 companiesâ€™ products and services are developed and sold considering customersâ€™ demands. Fulfilling customersâ€™ demands, taken as the starting point of relationship marketing, can be obtained by acknowledging that the customersâ€™ needs and wishes are heterogeneous. The segmentation of the customersâ€™ 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 customersâ€™ 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
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.
Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm
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.
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
Performance evaluation of image segmentation algorithms on microscopic image data
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
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
DDoS Defense Algorithm Based on Multi-Segment Timeout Technology
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.
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.
Optree: a learning-based adaptive watershed algorithm for neuron segmentation.
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
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.
New CSC Segment Builder Algorithm with MC TeV Muons in CMS Experiment
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.
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
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
Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation
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.
A MULTILEVEL AUTOMATIC THRESHOLDING FOR IMAGE SEGMENTATION USING GENETIC ALGORITHM AND DWT
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.
Color tongue image segmentation using fuzzy Kohonen networks and genetic algorithm
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.
IMPROVED ALGORITHM FOR ROAD REGION SEGMENTATION BASED ON SEQUENTIAL MONTE-CARLO ESTIMATION
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.
Algorithms for automatic segmentation of bovine embryos produced in vitro
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%
SEGMENTATION ALGORITHM BASED ON EDGE-SEARCHING FOR MUlTI-LINEAR STRUCTURED LIGHT IMAGES
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.
Region-Based Segmentation: Fuzzy Connectedness, Graph Cut and Related Algorithms
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.
Performance characterization of clustering algorithms for colour image segmentation
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...
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.
A Study Of Image Segmentation Algorithms For Different Types Of Images
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.
Knowledge Automatic Indexing Based on Concept Lexicon and Segmentation Algorithm
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.
A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.
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
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.
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.
Automated Algorithm for Carotid Lumen Segmentation and 3D Reconstruction in B-mode images
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.
Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images
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...
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 ...
Nonlinear physical segmentation algorithm for determining the layer boundary from lidar signal.
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
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)
Urinary stone size estimation: a new segmentation algorithm-based CT method
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.)
Performance evaluation of a contextual news story segmentation algorithm
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.
Control algorithm for the petal-shape segmented-mirror telescope with 18 mirrors
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.
An audio-based sports video segmentation and event detection algorithm
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....
Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images
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
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
An evolutionary algorithm for the segmentation of muscles and bones of the lower limb.
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%) .
A Decision-Tree-Based Algorithm for Speech/Music Classification and Segmentation
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.
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.
An Adaptive Frame Skipping and VOP Interpolation Algorithm for Video Object Segmentation
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.
Segmentation of Touching Hand written Telugu Characters by using Drop Fall Algorithm
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.
On the importance of FIB-SEM specific segmentation algorithms for porous media
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
High-speed MRF-based segmentation algorithm using pixonal images
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...
Hopfield-K-Means clustering algorithm: A proposal for the segmentation of electricity customers
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)
3D segmentation of medical images using a fast multistage hybrid algorithm
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.)
Hopfield-K-Means clustering algorithm: A proposal for the segmentation of electricity customers
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)
The Research of ECG Signal Automatic Segmentation Algorithm Based on Fractal Dimension Trajectory
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.
Analyzing the medical image by using clustering algorithms through segmentation process
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.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
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...
Brain tumor segmentation in MR slices using improved GrowCut algorithm
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.
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...
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.
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.
An effective method for segmentation of MR brain images using the ant colony optimization algorithm.
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
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.
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
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.
Application of Micro-segmentation Algorithms to the Healthcare Market:A Case Study
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.
Efficient algorithms for analyzing segmental duplications with deletions and inversions in genomes
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.
Multispectral image segmentation using parallel mean shift algorithm and CUDA technology
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.
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.
Image Segmentation Algorithm Based on Spectral Clustering Algorithm%谱聚类图像分割算法研究
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.
AUTOMATIC SEGMENTATION ALGORITHM FOR THE LUMEN OF THE CAROTID ARTERY IN ULTRASOUND B-MODE IMAGES
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...
INFARCT DETECTION IN BRAIN MRI USING IMPROVED SEGMENTATION ALGORITHM AND VOLUME VISUALIZATION
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 ...
3-D segmentation algorithm of small lung nodules in spiral CT images
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 ...
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.
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.
Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.
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
Image Segmentation and Maturity Recognition Algorithm based on Color Features of Lingwu Long Jujube
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.
Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation
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.
SEGMENTATION OF CT SCAN LUMBAR SPINE IMAGE USING MEDIAN FILTER AND CANNY EDGE DETECTION ALGORITHM
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.
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.
A variable fluence step clustering and segmentation algorithm for step and shoot IMRT
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)
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
CT liver volumetry using geodesic active contour segmentation with a level-set algorithm
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.
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
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.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
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...
Anatomy-Based Algorithms for Detecting Oral Cancer Using Reflectance and Fluorescence Spectroscopy
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...
Digital Terrain from a Two-Step Segmentation and Outlier-Based Algorithm
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.
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.
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
浅谈中文切词算法%A Tentative Study on Chinese Segmentation Algorithm
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.
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.
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.
DICOM Image Retrieval Using Novel Geometric Moments and Image Segmentation Algorithm
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.
A quantum mechanics-based algorithm for vessel segmentation in retinal images
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.
A quantum mechanics-based algorithm for vessel segmentation in retinal images
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.
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...
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.
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
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.
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.
Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape
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...
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.
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... Surveillance Modules » Anatomy & Physiology Cancer Registration & Surveillance Modules Anatomy & Physiology Intro to the Human Body Body Functions & Life Process Anatomical Terminology Review ...
Practical Constraint K-Segment Principal Curve Algorithms for Generating Railway GPS Digital Map
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.
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
An efficient algorithm for multiphase image segmentation with intensity bias correction.
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
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....
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...
KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on K-Means Clustering and Graph Cut
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.
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.
A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.
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
An iris segmentation algorithm based on edge orientation for off-angle iris recognition
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.
An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition
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.
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.
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.)
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
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
KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on K-Means Clustering and Graph Cut
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...
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
Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk
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
DIALIGN-T: An improved algorithm for segment-based multiple sequence alignment
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.
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
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... 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 ...
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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....
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An algorithm for target airplane segmentation & extraction%一种目标飞机分割提取方法
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。
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.
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.
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