A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013

[1]  G. M.,et al.  Partial Differential Equations I , 2023, Applied Mathematical Sciences.

[2]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[4]  Wei Li,et al.  A Novel Level Set Based Shape Prior Method for Liver Segmentation from MRI Images , 2008, MIAR.

[5]  M. Giger,et al.  Automatic segmentation of liver structure in CT images. , 1993, Medical physics.

[6]  Martin von Siebenthal,et al.  Analysis and modelling of respiratory liver motion using 4DMRI , 2008 .

[7]  Chandrika Kamath,et al.  Investigation of implicit active contours for scientific image segmentation , 2004, IS&T/SPIE Electronic Imaging.

[8]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[9]  Karol Mikula,et al.  Solution of nonlinearly curvature driven evolution of plane curves , 1999 .

[10]  M. Grayson The heat equation shrinks embedded plane curves to round points , 1987 .

[11]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[12]  Xue-Cheng Tai,et al.  A Level Set Formulation of Geodesic Curvature Flow on Simplicial Surfaces , 2010, IEEE Transactions on Visualization and Computer Graphics.

[13]  James S. Duncan,et al.  Game-Theoretic Integration for Image Segmentation , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Carmen Ayuso,et al.  MRI angiography is superior to helical CT for detection of HCC prior to liver transplantation: An explant correlation , 2003, Hepatology.

[15]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[16]  Roman Goldenberg,et al.  Fast Geodesic Active Contours , 1999, Scale-Space.

[17]  Min-Seok Kim,et al.  Robust Text-Independent Speaker Identification Using Hybrid PCA&LDA , 2006, MICAI.

[18]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[19]  Jianwu Xu,et al.  Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. , 2010, Medical physics.

[20]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  John E Skandalakis,et al.  Hepatic surgical anatomy. , 2004, The Surgical clinics of North America.

[22]  D. Chopp Computing Minimal Surfaces via Level Set Curvature Flow , 1993 .

[23]  Janice Ward,et al.  Liver metastases in candidates for hepatic resection: comparison of helical CT and gadolinium- and SPIO-enhanced MR imaging. , 2005, Radiology.

[24]  Chenyang Xu,et al.  Gamma -Convergence Approximation to Piecewise Smooth Medical Image Segmentation , 2007, MICCAI.

[25]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[26]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[27]  Tomoaki Ichikawa,et al.  MRI in the Evaluation of Hepatocellular Nodules: Role of Pulse Sequences and Contrast Agents , 2004, Intervirology.

[28]  D. Larkman,et al.  Parallel magnetic resonance imaging , 2007, Physics in medicine and biology.

[29]  James G. Malcolm,et al.  Localized statistics for DW-MRI fiber bundle segmentation , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[30]  Johan Montagnat,et al.  Volumetric medical images segmentation using shape constrained deformable models , 1997, CVRMed.

[31]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[32]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[33]  Xun Wang,et al.  Deformable Contour Method: A Constrained Optimization Approach , 2004, International Journal of Computer Vision.

[34]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  P. McNicholas,et al.  Model‐based clustering of longitudinal data , 2010 .

[36]  P J Robinson,et al.  Combined use of MR contrast agents for evaluating liver disease. , 2001, Magnetic resonance imaging clinics of North America.

[37]  Klaus D. Tönnies,et al.  A New Approach for Model-Based Adaptive Region Growing in Medical Image Analysis , 2001, CAIP.

[38]  Wieslaw Lucjan Nowinski,et al.  Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique , 2012, International Journal of Computer Assisted Radiology and Surgery.

[39]  Xianghua Xie,et al.  Active Contouring Based on Gradient Vector Interaction and Constrained Level Set Diffusion , 2010, IEEE Transactions on Image Processing.

[40]  杨帆 A Shape-Optimized Framework for Kidney Segmentation in Ultrasound Images Using NLTV Denoising and DRLSE , 2012 .

[41]  M S Brown,et al.  Knowledge-Based Segmentation of Pediatric Kidneys in CT for Measurement of Parenchymal Volume , 2001, Journal of computer assisted tomography.

[42]  Chong-Soo Kim,et al.  Preoperative detection of hepatocellular carcinoma: comparison of combined contrast-enhanced MR imaging and combined CT during arterial portography and CT hepatic arteriography , 2004, European Radiology.

[43]  S. Afaq Husain,et al.  Use of neural networks for feature based recognition of liver region on CT images , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[44]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Krishna Juluru MRI of the liver: A practical guide , 2008 .

[46]  Hans-Peter Meinzer,et al.  Active Shape Models for a Fully Automated 3D Segmentation of the Liver - An Evaluation on Clinical Data , 2006, MICCAI.

[47]  Yu-Jin Zhang,et al.  Advances in image and video segmentation , 2006 .

[48]  Chengke Wu,et al.  A New Active Contour Model: Curvature Gradient Vector Flow , 2006, ACCV.

[49]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[50]  B. V. Van Beers,et al.  Detection of hepatic metastases: ferumoxides-enhanced MR imaging versus unenhanced MR imaging and CT during arterial portography. , 1996, Radiology.

[51]  Aly A. Farag,et al.  A kidney segmentation approach from DCE-MRI using level sets , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[52]  Jerry L. Prince,et al.  Medical image seg-mentation using deformable models , 2000 .

[53]  B. Marincek,et al.  Detection of liver metastases: comparison of superparamagnetic iron oxide-enhanced and unenhanced MR imaging at 1.5 T with dynamic CT, intraoperative US, and percutaneous US. , 1995, Radiology.

[54]  Richard M. Leahy,et al.  Geodesic curvature flow on surfaces for automatic sulcal delineation , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[55]  Yoshinobu Sato,et al.  Automated liver segmentation using multislice CT images , 2003, Systems and Computers in Japan.

[56]  Gang Chen,et al.  An Improved Level Set for Liver Segmentation and Perfusion Analysis in MRIs , 2009, IEEE Transactions on Information Technology in Biomedicine.

[57]  C. Couinaud,et al.  Liver Anatomy: Portal (and Suprahepatic) or Biliary Segmentation , 2000, Digestive Surgery.

[58]  Lixu Gu,et al.  A novel liver perfusion analysis method , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[59]  Hans-Peter Meinzer,et al.  A Statistical Deformable Model for the Segmentation of Liver CT Volumes , 2007 .

[60]  Alexei Sourin,et al.  Interactive surface-guided segmentation of brain MRI data , 2009, Comput. Biol. Medicine.

[61]  Chuanjiang He,et al.  Variational level set methods for image segmentation based on both L2 and Sobolev gradients , 2012 .

[62]  M.F. Santarelli,et al.  A Robust Method for Assessment of Iron Overload in Liver by Magnetic Resonance Imaging , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[63]  Zhengrong Liang,et al.  Haustral Fold Segmentation With Curvature-Guided Level Set Evolution , 2013, IEEE Transactions on Biomedical Engineering.

[64]  T. Murakami,et al.  Preoperative detection of malignant hepatic tumors: comparison of combined methods of MR imaging with combined methods of CT. , 2000, AJR. American journal of roentgenology.

[65]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[66]  Gongping Yang,et al.  -Means Based Fingerprint Segmentation with Sensor Interoperability , 2010, EURASIP J. Adv. Signal Process..

[67]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[68]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[69]  D. N. Geary Mixture Models: Inference and Applications to Clustering , 1989 .

[70]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[71]  Abhinav K Jha,et al.  A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images , 2010, 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI).

[72]  Paul D. McNicholas,et al.  Parsimonious Gaussian mixture models , 2008, Stat. Comput..

[73]  A. Yezzi,et al.  Metrics in the space of curves , 2004, math/0412454.

[74]  Jue Zhang,et al.  A Fast and Robust Approach to Liver Nodule Detection in MR Images , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

[75]  James A. Sethian,et al.  Image Processing: Flows under Min/Max Curvature and Mean Curvature , 1996, CVGIP Graph. Model. Image Process..

[76]  Karl Stierstorfer,et al.  Multi-detector row CT systems and image-reconstruction techniques. , 2005, Radiology.

[77]  P. Smereka,et al.  A Remark on Computing Distance Functions , 2000 .

[78]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[79]  S. Osher,et al.  Regular Article: A PDE-Based Fast Local Level Set Method , 1999 .

[80]  Xianglong Tang,et al.  Probability density difference-based active contour for ultrasound image segmentation , 2010, Pattern Recognit..

[81]  Vinayadatt V Kohir,et al.  Level set issues for efficient image segmentation , 2011 .

[82]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[83]  Armando Barreto,et al.  A 3-D Liver Segmentation Method with Parallel Computing for Selective Internal Radiation Therapy , 2012, IEEE Transactions on Information Technology in Biomedicine.

[84]  Abdul Rahman Ramli,et al.  Survey on liver CT image segmentation methods , 2011, Artificial Intelligence Review.

[85]  Alejandro F. Frangi,et al.  Three-dimensional modeling for functional analysis of cardiac images, a review , 2001, IEEE Transactions on Medical Imaging.

[86]  Mark Sussman,et al.  An Efficient, Interface-Preserving Level Set Redistancing Algorithm and Its Application to Interfacial Incompressible Fluid Flow , 1999, SIAM J. Sci. Comput..

[87]  J. Barkhausen,et al.  Spio-MR Imaging versus Double-Phase Spiral CT in Detecting Malignant Lesions of the Liver , 1999, Acta radiologica.

[88]  S. Casciaro,et al.  Fully Automatic Liver Segmentation through Graph-Cut Technique , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[89]  Klaus D. Tönnies,et al.  Segmentation of medical images using adaptive region growing , 2001, SPIE Medical Imaging.

[90]  Lawrence O. Hall,et al.  Fast Accurate Fuzzy Clustering through Data Reduction , 2003 .

[91]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[92]  Li-Hong Juang,et al.  MRI brain lesion image detection based on color-converted K-means clustering segmentation , 2010 .

[93]  Thomas Lange,et al.  Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model , 2007 .

[94]  N. Ayache,et al.  Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery , 2001 .

[95]  Yongtian Wang,et al.  MR-guided liver cancer surgery by nonrigid registration , 2010, 2010 International Conference of Medical Image Analysis and Clinical Application.

[96]  L. Bidaut Data and image processing for abdominal imaging , 2000 .

[97]  Sanyou Zeng,et al.  Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation , 2011 .

[98]  Kramer Dm Basic principles of magnetic resonance imaging. , 1984 .

[99]  R. Pohle,et al.  Self-learning model-based segmentation of medical images , 2001 .

[100]  Richard M. Slone,et al.  Body CT : a practical approach , 2000 .

[101]  Ke Chen,et al.  A 3D multi-grid algorithm for the Chan–Vese model of variational image segmentation , 2012, Int. J. Comput. Math..

[102]  Michael Unser,et al.  Variational B-Spline Level-Set: A Linear Filtering Approach for Fast Deformable Model Evolution , 2009, IEEE Transactions on Image Processing.

[103]  Young Dae Kim,et al.  COMPARISON OF CLINICAL UTILITY BETWEEN NEW AND OLD BLEEDING CRITERIA : A PROSPECTIVE STUDY OF EVALUATION FOR THE BLEEDING ACADEMIC RESEARCH CONSORTIUM DEFINITION OF BLEEDING IN PATIENTS WITH UNDERGOING PERCUTANEOUS INTERVENTION , 2013 .

[104]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[105]  Yang Tang,et al.  Shape-aided kidney extraction in MR urography , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[106]  Cüneyt Güzelis,et al.  Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation , 2008, Comput. Biol. Medicine.

[107]  Jean Gao,et al.  A deformable model for automatic CT liver extraction. , 2005, Academic radiology.

[108]  Carlos Platero Dueñas,et al.  Automatic Method to Segment the Liver on Multi-Phase MRI , 2008 .

[109]  A. Mojsilovic,et al.  Wavelet image extension for analysis and classification of infarcted myocardial tissue , 1997, IEEE Transactions on Biomedical Engineering.

[110]  Matthias Fenchel,et al.  Reconstructing liver shape and position from MR image slices using an active shape model , 2008, SPIE Medical Imaging.

[111]  Lavdie Rada,et al.  A New Variational Model with Dual Level Set Functions for Selective Segmentation , 2012 .

[112]  De-Xing Kong,et al.  HYPERBOLIC MEAN CURVATURE FLOW: EVOLUTION OF PLANE CURVES , 2008, 0803.0408.

[113]  M V Knopp,et al.  Hepatic lesions: morphologic and functional characterization with multiphase breath-hold 3D gadolinium-enhanced MR angiography--initial results. , 1999, Radiology.

[114]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[115]  Jakob Wasza,et al.  Article in Press G Model Computerized Medical Imaging and Graphics Segmentation of Kidneys Using a New Active Shape Model Generation Technique Based on Non-rigid Image Registration , 2022 .

[116]  J. Sethian Evolution, implementation, and application of level set and fast marching methods for advancing fronts , 2001 .

[117]  Chi Dongxiang,et al.  Iterative Quadtree Decomposition Segmentation of Liver MR Image , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[118]  C. Price,et al.  Controlled Multicenter Evaluation of a Bacteriophage-Based Method for Rapid Detection of Staphylococcus aureus in Positive Blood Cultures , 2013, Journal of Clinical Microbiology.

[119]  Yunmei Chen,et al.  On the incorporation of shape priors into geometric active contours , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[120]  Pierre Kornprobst,et al.  Mathematical problems in image processing - partial differential equations and the calculus of variations , 2010, Applied mathematical sciences.

[121]  James A McCoy,et al.  a new class of fully nonlinear curvature flows , 2009 .

[122]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[123]  Xun Wang,et al.  A comparative study of deformable contour methods on medical image segmentation , 2008, Image Vis. Comput..

[124]  J. Sandberg,et al.  Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. , 2010, Medical physics.

[125]  Yen-Wei Chen,et al.  Liver Segmentation from Low Contrast Open MR Scans Using K-Means Clustering and Graph-Cuts , 2010, ISNN.

[126]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[127]  Masaharu Kobashi,et al.  Knowledge-based organ identification from CT images , 1992, Medical Imaging.

[128]  E. Fishman,et al.  Automatic liver segmentation technique for three-dimensional visualization of CT data. , 1996, Radiology.

[129]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[130]  A. Yezzi,et al.  On the relationship between parametric and geometric active contours , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

[131]  G. Barles,et al.  Front propagation and phase field theory , 1993 .

[132]  Ali Rafiee,et al.  Using neural network for liver detection in abdominal MRI images , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[133]  O. Faugeras,et al.  Computer Vision — ECCV 90 , 1990, Lecture Notes in Computer Science.

[134]  Cüneyt Güzelis,et al.  An automatic level set based liver segmentation from MRI data sets , 2012, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA).

[135]  S. Zucker,et al.  Toward a computational theory of shape: an overview , 1990, eccv 1990.

[136]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[137]  Henry Völzke,et al.  A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images. , 2010, Magnetic resonance imaging.

[138]  Gao Yan,et al.  An automatic kidney segmentation from abdominal CT images , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[139]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[140]  B. Condon,et al.  Image non-uniformity in magnetic resonance imaging: its magnitude and methods for its correction. , 1987, The British journal of radiology.

[141]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[142]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[143]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[144]  Yo-Sung Ho,et al.  Automatic liver segmentation for volume measurement in CT Images , 2006, J. Vis. Commun. Image Represent..

[145]  Gang Cheng,et al.  Comparison of FDG-PET, MRI and CT for post radiofrequency ablation evaluation of hepatic tumors , 2012, Annals of Nuclear Medicine.

[146]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[147]  T. Chan,et al.  A Variational Level Set Approach to Multiphase Motion , 1996 .

[148]  Ranjan Maitra Initializing Partition-Optimization Algorithms , 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[149]  Rémi Ronfard,et al.  Region-based strategies for active contour models , 1994, International Journal of Computer Vision.

[150]  Suhong Ko,et al.  Segmentation of 3D MR Liver Images Using Synchronised Oscillators Network , 2007, 2007 International Symposium on Information Technology Convergence (ISITC 2007).

[151]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[152]  Yong Yin,et al.  Automated CT liver segmentation using improved Chan-Vese model with global shape constrained energy , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[153]  M. Meng,et al.  Image segmentation by improved level set evolution algorithm , 2012, 2012 IEEE International Conference on Information and Automation.

[154]  Joon Beom Seo,et al.  Ecient Liver Segmentation exploiting Level-Set Speed Images with 2.5D Shape Propagation , 2007 .

[155]  M. Gage,et al.  The Curve Shortening Flow , 1987 .

[156]  Janice Ward,et al.  Colorectal hepatic metastases: detection with SPIO-enhanced breath-hold MR imaging--comparison of optimized sequences. , 2003, Radiology.

[157]  Volodymyr Melnykov,et al.  Finite mixture models and model-based clustering , 2010 .

[158]  K. L. Moore,et al.  Clinically Oriented Anatomy , 1985 .

[159]  Hu Yijun,et al.  Segmentation of cDNA Microarray Spots Using K-means Clustering Algorithm and Mathematical Morphology , 2009, 2009 WASE International Conference on Information Engineering.

[160]  Dat Tran,et al.  Novel Convex Active Contour Model Using Local and Global Information , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[161]  HighWire Press,et al.  Journal of clinical microbiology : JCM. , 1975 .

[162]  Adam W. M. Mitchell,et al.  Gray's Anatomy for Students , 2004 .

[163]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[164]  Chin-Tu Chen,et al.  Image fusion for visualization of hepatic vasculature and tumors , 1995, Medical Imaging.

[165]  Pedro J. Moreno,et al.  A Study of Musical Instrument Classification Using Gaussian Mixture Models and Support Vector Machines , 1999 .

[166]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[167]  Abdel-Badeeh M. Salem,et al.  An efficient enhanced k-means clustering algorithm , 2006 .

[168]  László Ruskó,et al.  Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model , 2010, International Journal of Computer Assisted Radiology and Surgery.

[169]  G. Peeters,et al.  GMM SUPERVECTOR FOR CONTENT BASED MUSIC SIMILARITY , 2011 .

[170]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[171]  M. Rousson,et al.  Γ-Convergence Approximation to Piecewise Smooth Medical Image Segmentation , 2007 .

[172]  S. Osher,et al.  A PDE-Based Fast Local Level Set Method 1 , 1998 .

[173]  Baigalmaa Tsagaan,et al.  An Automated Segmentation Method of Kidney Using Statistical Information , 2002, MICCAI.

[174]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[175]  Johan Montagnat,et al.  Shape and Topology Constraints on Parametric Active Contours , 2001, Comput. Vis. Image Underst..

[176]  G.B. Coleman,et al.  Image segmentation by clustering , 1979, Proceedings of the IEEE.

[177]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[178]  Baba C. Vemuri,et al.  Robust Point Set Registration Using Gaussian Mixture Models , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[179]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[180]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[181]  M. P. Sebastian,et al.  Improving the Accuracy and Efficiency of the k-means Clustering Algorithm , 2009 .

[182]  Du-Yih Tsai Automatic Segmentation of Liver Structure in CT Images Using a Neural Network (Special Section of Letters Selected from the 1994 IEICE Spring Conference) , 1994 .

[183]  Olivier Faugeras,et al.  Reconciling Distance Functions and Level Sets , 2000, J. Vis. Commun. Image Represent..

[184]  Li Wang,et al.  Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy , 2010, Journal of Neuroscience Methods.

[185]  Xavier Bresson,et al.  Fast Global Minimization of the Active Contour/Snake Model , 2007, Journal of Mathematical Imaging and Vision.

[186]  R. Semelka,et al.  Liver metastases: comparison of current MR techniques and spiral CT during arterial portography for detection in 20 surgically staged cases. , 1999, Radiology.

[187]  L. Boni,et al.  Evaluation of the cardiovascular health study (CHS) instrument and the Vulnerable Elders Survey-13 (VES-13) in elderly cancer patients. Are we still missing the right screening tool? , 2013, Annals of oncology : official journal of the European Society for Medical Oncology.

[188]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[189]  B. Dawant,et al.  Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods. , 2005, Radiology.

[190]  Bjørn Olstad,et al.  Encoding of a priori Information in Active Contour Models , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[191]  Hyunjin Park,et al.  Construction of an abdominal probabilistic atlas and its application in segmentation , 2003, IEEE Transactions on Medical Imaging.

[192]  Yunmei Chen,et al.  Using Prior Shapes in Geometric Active Contours in a Variational Framework , 2002, International Journal of Computer Vision.

[193]  Mila Nikolova,et al.  Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models , 2006, SIAM J. Appl. Math..

[194]  K. L. Moore,et al.  Clinically Oriented Anatomy, 4th Edition , 1999 .

[195]  Maximilian F Reiser,et al.  Enhancement of focal liver lesions at gadoxetic acid-enhanced MR imaging: correlation with histopathologic findings and spiral CT--initial observations. , 2005, Radiology.

[196]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[197]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[198]  Nizar Bouguila,et al.  Spatial color image segmentation based on finite non-Gaussian mixture models , 2012, Expert Syst. Appl..

[199]  Chunming Li,et al.  Implicit Active Contours Driven by Local Binary Fitting Energy , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[200]  Mubarak Shah,et al.  A Fast algorithm for active contours and curvature estimation , 1992, CVGIP Image Underst..

[201]  W. Clem Karl,et al.  A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution , 2008, IEEE Transactions on Image Processing.

[202]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[203]  L. Ruskó,et al.  Fully automatic liver segmentation for contrast-enhanced CT images , 2007 .

[204]  Gilson A. Giraldi,et al.  A boundary extraction method based on Dual-T-Snakes and dynamic programming , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[205]  Sylvia Frühwirth-Schnatter,et al.  Finite Mixture and Markov Switching Models , 2006 .

[206]  Victor M. Brea,et al.  Discrete-time CNN for image segmentation by active contours , 1998, Pattern Recognit. Lett..

[207]  Théodore Papadopoulo,et al.  Efficient Segmentation of Piecewise Smooth Images , 2007, SSVM.

[208]  John R. Kender,et al.  Sectored Snakes: Evaluating Learned-Energy Segmentations , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[209]  Ron Kimmel,et al.  Numerical geometry of images - theory, algorithms, and applications , 2003 .

[210]  Pau-Choo Chung,et al.  Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules , 2003, IEEE Transactions on Information Technology in Biomedicine.

[211]  David A. Rottenberg,et al.  Quantitative comparison of four brain extraction algorithms , 2004, NeuroImage.

[212]  W. Gibby Basic principles of magnetic resonance imaging. , 2005, Neurosurgery clinics of North America.

[213]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

[214]  S. Arridge,et al.  Sources of intensity nonuniformity in spin echo images at 1.5 T , 1994, Magnetic resonance in medicine.

[215]  M. Lafortune,et al.  Segmental anatomy of the liver: a sonographic approach to the Couinaud nomenclature. , 1991, Radiology.

[216]  M Georgi,et al.  MRI with superparamagnetic iron oxide: efficacy in the detection and characterization of focal hepatic lesions. , 1999, Magnetic resonance imaging.

[217]  Baigalmaa Tsagaan,et al.  Segmentation of kidney by using a deformable model , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[218]  P. Nacher,et al.  Magnetic Resonance Imaging: From Spin Physics to Medical Diagnosis , 2009 .

[219]  Carlo Bartolozzi,et al.  Clinical Management of Hepatic Malignancies: Ferucarbotran-Enhanced Magnetic Resonance Imaging Versus Contrast-Enhanced Spiral Computed Tomography , 2005, Digestive Diseases and Sciences.

[220]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[221]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[222]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[223]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[224]  Shitong Wang,et al.  Advanced fuzzy cellular neural network: Application to CT liver images , 2007, Artif. Intell. Medicine.

[225]  Stephen J. Wright,et al.  Springer Series in Operations Research , 1999 .

[226]  R. Osserman The isoperimetric inequality , 1978 .

[227]  C. Bandle Isoperimetric inequalities and applications , 1980 .

[228]  Ross T. Whitaker,et al.  A Level-Set Approach to 3D Reconstruction from Range Data , 1998, International Journal of Computer Vision.

[229]  Pheng-Ann Heng,et al.  Adaptive Liver Segmentation from Multi-slice CT Scans , 2008 .