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Medical Image Segmentation: A Brief Survey

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Book cover Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies

Abstract

Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical objects of interest for analysis is essential in almost any computer-aided diagnosis system or other medical imaging applications. Various aspects of segmentation features and algorithms have been extensively explored for many years in a host of publications. However, the problem remains challenging, with no general and unique solution, due to a large and constantly growing number of different objects of interest, large variations of their properties in images, different medical imaging modalities, and associated changes of signal homogeneity, variability, and noise for each object. This chapter overviews most popular medical image segmentation techniques and discusses their capabilities, and basic advantages and limitations. The state-of-the-art techniques of the last decade are also outlined.

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References

  1. Pham DL, Xu C, Prince JL (2000) A survey of current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–338

    PubMed  CAS  Google Scholar 

  2. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Pearson Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  3. Farag A, Ahmed MN, El-Baz A, Hassan H (2005) Advanced segmentation techniques. In: Wilson DL, Laxminarayan S, Suri JS (eds) Handbook of biomedical image analysis, volume I: segmentation models. Kluwer Academic/Plenum Publishers, New York, pp 479–534, Chapter 9

    Google Scholar 

  4. Prastawa M, Bullitt E, Gerig G (2009) Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med Image Anal 13(2):297–311

    PubMed  Google Scholar 

  5. El-Baz A, Farag A, Gimel’farb G, Falk R, El-Ghar M, Eldiasty T (2006) A framework for the automatic segmentation of lung nodules from low dose chest CT scans. In: Proceedings of the international conference on pattern recognition (ICPR’06), Hong Kong, 20–24 August 2006, pp 611–614

    Google Scholar 

  6. Grau V, Mewes AUJ, Alcaniz M, Kikinis R, Warfield SK (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23(4):447–458

    PubMed  CAS  Google Scholar 

  7. Greenspan H, Ruf A, Goldberger J (2006) Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans Med Imaging 25(9):1233–1245

    PubMed  Google Scholar 

  8. Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, Vembar M, Olszewski ME, Subramanyan K, Lavi G, Weese J (2008) Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging 27(9):1189–1201

    PubMed  Google Scholar 

  9. El-Baz A, Farag A, Yuksel S, Abou El-Ghar M, Eldiasty T, Ghoneim M (2007) Application of deformable models for the detection of acute renal rejection. In: Suri JS, Farag A (eds) Handbook of parametric and geometric deformable models: biomedical and clinical applications, II. Springer, New York, pp 293–333, Chapter 10

    Google Scholar 

  10. Laws KI (1980) Textured image segmentation. Ph.D. Thesis, University of Southern California, Los Angeles, CA

    Google Scholar 

  11. Malik J, Perona P (1990) Preattentive texture discrimination with early vision mechanisms. J Opt Soc Am 7(5):923–932

    CAS  Google Scholar 

  12. Unser M, Eden M (1990) Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering. IEEE Trans Syst Man Cybern B Cybern 20(4):804–815

    Google Scholar 

  13. Schowengerdt RA (2007) Remote sensing: models and methods for image processing, 3rd edn. Elsevier, Burlington, MA

    Google Scholar 

  14. Campbell FW, Robson J (1968) Application of Fourier analysis to the visibility of gratings. J Physiol 197(3):551–566

    PubMed  PubMed Central  CAS  Google Scholar 

  15. Coggins JM, Jain AK (1985) A spatial filtering approach to texture analysis. Pattern Recognit Lett 3(3):195–203

    Google Scholar 

  16. Smith GM (1998) Image texture analysis using zero crossing information. Ph.D. Thesis, University of Queensland, Australia

    Google Scholar 

  17. Choi H, Baraniuk RG (2001) Multiscale image segmentation using wavelet-domain hidden Markov models. IEEE Trans Image Process 10(9):1309–1321

    PubMed  CAS  Google Scholar 

  18. Jin Y, Angelini E, Laine A (2005) Wavelets in medical image processing: denoising, segmentation, and registration. In: Suri JS, Wilson DL, Laxminarayan S (eds) Handbook of biomedical image analysis, vol I. Kluwer Academic/Plenum Publishers, New York, pp 305–358, Segmentation Models, Chapter 6

    Google Scholar 

  19. Mandelbrot BB (1983) The fractal geometry of nature. W. H. Freeman and Company Limited, New York

    Google Scholar 

  20. Barnsley M (1993) Fractals everywhere. Academic, Boston, MA

    Google Scholar 

  21. Haindl M (1991) Texture synthesis. CWI Q 4:305–331

    Google Scholar 

  22. Li H, Liu KJR, Lo S-CB (1997) Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans Med Imaging 16(6):785–798

    PubMed  CAS  Google Scholar 

  23. Chaudhuri B, Sarker N, Kundu P (1993) Improved fractal geometry based texture segmentation technique. IEEE Proc Comput Digit Tech 140(5):223–241

    Google Scholar 

  24. Xia Y, Feng D, Zhao R (2006) Morphology-based multifractal estimation for texture segmentation. IEEE Trans Image Process 15(3):614–623

    PubMed  Google Scholar 

  25. Dubes RC, Jain AK (1989) Random field models in image analysis. J Appl Stat 16(2):131–164

    Google Scholar 

  26. Chellappa R (1989) Two dimensional discrete Gaussian Markov random field models for image processing. J Inst Electron Telecommun Eng 35(2):114–120

    Google Scholar 

  27. Kashyap R (1984) Characterization and estimation of two dimensional ARMA models. IEEE Trans Inform Theory 30(5):736–745

    Google Scholar 

  28. Besag J (1974) Spatial interaction and statistical analysis of lattice systems. J R Stat Soc Series B Methodol 36(2):192–236

    Google Scholar 

  29. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6(6):721–741

    PubMed  CAS  Google Scholar 

  30. Gimel’farb G (1996) Texture modeling by multiple pairwise pixel interactions. IEEE Trans Pattern Anal Mach Intell 18(11):1110–1114

    Google Scholar 

  31. Zhu SC, Wu Y, Mumford D (1998) Filters, random fields, and maximum entropy (FRAME): towards a unified theory for texture modeling. Int J Comput Vis 27(2):107–126

    Google Scholar 

  32. El-Baz A, Farag AA (2003) Image segmentation using GMRF models: parameters estimation and applications. In: Proceedings of IEEE international conference on image processing (ICIP’03), Barcelona, Spain, 14–18 September 2003, vol. II, pp 177–180

    Google Scholar 

  33. Roth S, Black MJ (2009) Fields of experts. Int J Comput Vis 82(2):205–229

    Google Scholar 

  34. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239

    Google Scholar 

  35. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

    PubMed  Google Scholar 

  36. Veklser O (2007) Graph cut based optimization for MRFs with truncated convex priors. In: IEEE conference on computer vision and pattern recognition (CVPR’07), Minneapolis, MN, 17–22 June 2007

    Google Scholar 

  37. Kohli P, Kumar MP, Torr PHS (2007) P3 & beyond: solving energies with higher order cliques. In: IEEE conference on computer vision and pattern recognition (CVPR’07), Minneapolis, MN, 17–22 June 2007

    Google Scholar 

  38. Kohli P, Ladicky L, Torr P (2008) Robust higher order potentials for enforcing label consistency. In: IEEE conference on computer vision and pattern recognition (CVPR’08), Anchorage, AK, 23–28 June 2008

    Google Scholar 

  39. Ramalingam S, Kohli P, Alahari K, Torr P (2008) Exact inference in multi-label CRFs with higher order cliques. In: IEEE conference on computer vision and pattern recognition (CVPR’08), Anchorage, AK, 23–28 June 2008

    Google Scholar 

  40. Ali AM, Farag AA, Gimel’farb GL (2008) Optimizing binary MRFs with higher order cliques. In: Proceedings of the 10th European conference on computer vision (ECCV’08), Marseille, France, 12–18 October 2008, part III, pp 98–111

    Google Scholar 

  41. Ishikawa H (2009) Higher-order clique reduction in binary graph cut. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR’09), Miami, FL, 20–25 June 2009, pp 2993–3000

    Google Scholar 

  42. Rother C, Kohli P, Feng W, Jia J (2009) Minimizing sparse higher order energy functions of discrete variables. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR’09), Miami, FL, 20–25 June 2009, pp 1382–1389

    Google Scholar 

  43. Rousson M, Paragios N, Shape priors for level set representations. In: Proceedings of the 7th European conference on computer vision (ECCV’02), Denmark, Copenhagen, 27 May–2 June 2002, part II, pp 78–92

    Google Scholar 

  44. Shen D, Zhan Y, Davatzikos C (2003) Segmentation of prostate boundaries from ultrasound images using statistical shape model. IEEE Trans Med Imaging 22(4):539–551

    PubMed  Google Scholar 

  45. Tsai A, Yezzi A Jr, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A (2003) A shape based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22(2):137–154

    PubMed  Google Scholar 

  46. Yang J, Staib LH, Duncan JS (2004) Neighbor-constrained segmentation with level set based 3-D deformable models. IEEE Trans Med Imaging 23(8):940–948

    PubMed  PubMed Central  Google Scholar 

  47. Tsai A, Wells W, Tempany C, Grimson E, Willsky A (2004) Mutual information in coupled multi-shape model for medical image segmentation. Med Image Anal 8(4):429–445

    PubMed  CAS  Google Scholar 

  48. Rohlfing T, Maurer CR Jr (2007) Shape-based averaging. IEEE Trans Image Process 16(1):153–161

    PubMed  Google Scholar 

  49. Duta N, Sonka M (1998) Segmentation and interpretation of MR brain images: an improved active shape model. IEEE Trans Med Imaging 17(6):1049–1062

    PubMed  CAS  Google Scholar 

  50. Shen D, Herskovits EH, Davatzikos C (2001) An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures. IEEE Trans Med Imaging 20(4):257–270

    PubMed  CAS  Google Scholar 

  51. Tao X, Prince JL, Davatzikos C (2002) Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain. IEEE Trans Med Imaging 21(5):513–524

    PubMed  Google Scholar 

  52. van Ginneken B, Frangi AF, Staal JJ, ter-Haar Romeny BM, Viergever MA (2002) Active shape model segmentation with optimal features. IEEE Trans Med Imaging 21(8):924–933

    PubMed  Google Scholar 

  53. Joshi S, Pizer S, Fletcher PT, Yushkevich P, Thall A, Marron JS (2002) Multiscale deformable model segmentation and statistical shape analysis using medial descriptions. IEEE Trans Med Imaging 21(5):538–550

    PubMed  Google Scholar 

  54. Pitiot A, Toga AW, Thompson PM (2002) Adaptive elastic segmentation of brain MRI via Shape-model-guided evolutionary programming. IEEE Trans Med Imaging 21(8):910–923

    PubMed  Google Scholar 

  55. Behiels G, Maes F, Vandermeulen D, Suetens P (2002) Deformable evaluation of image features and search strategies for segmentation of bone structures in radiographs using active shape models. Med Image Anal 6(1):47–62

    PubMed  Google Scholar 

  56. Davatzikos C, Tao X, Shen D (2003) Hierarchical active shape models, using the wavelet transform. IEEE Trans Med Imaging 22(3):414–423

    PubMed  Google Scholar 

  57. Gong L, Pathak SD, Haynor DR, Cho PS, Kim Y (2004) Parametric shape modeling using deformable super ellipses for prostate segmentation. IEEE Trans Med Imaging 23(3):340–349

    PubMed  Google Scholar 

  58. Tutar IB, Pathak SD, Gong L, Cho PS, Wallner K, Kim Y (2006) Semiautomatic 3-D prostate segmentation from TRUS images using spherical harmonics. IEEE Trans Med Imaging 25(12):1645–1654

    PubMed  Google Scholar 

  59. El-Baz A, Gimel’farb G (2008) Image segmentation with a parametric deformable model using shape and appearance priors. In: IEEE conference on computer vision and pattern recognition (CVPR’08), Anchorage, AK, 23–28 June 2008

    Google Scholar 

  60. Shi Y, Qi F, Xue Z, Chen L, Ito K, Matsuo H, Shen D (2008) Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Trans Med Imaging 27(4):481–494

    PubMed  CAS  Google Scholar 

  61. Nguyen H, Ji Q (2008) Shape-driven three-dimensional watersnake segmentation of biological membranes in electron tomography. IEEE Trans Med Imaging 27(5):616–628

    PubMed  CAS  Google Scholar 

  62. Liu J, Udupa JK (2009) Oriented active shape models. IEEE Trans Med Imaging 28(4):571–584

    PubMed  CAS  Google Scholar 

  63. Yan P, Xu S, Turkbey B, Kruecker J (2010) Discrete deformable model guided by partial active shape model for TRUS image segmentation. IEEE Trans Biomed Eng 57(5):1158–1166

    PubMed  Google Scholar 

  64. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Understanding (GVIU) 61(1):38–59

    Google Scholar 

  65. Leventon ME, Grimson WEL, Faugeras O (2000) Statistical shape influence in geodesic active contours. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR’2000), Hilton Head Island, SC, 13–15 June 2000, pp 316–323

    Google Scholar 

  66. Chen Y, Thiruvenkadam S, Tagare HD, Huang F, Wilson D, Geiser EA (2001) On the incorporation of shape priors into geometric active contours. In: Proceedings of IEEE workshop on variational and level set methods, Vancouver, BC, Canada, 2001, pp 145–152

    Google Scholar 

  67. Litvin A, Karl WC (2003) Levelset based segmentation using data driven shape prior on feature histograms. In: IEEE workshop on statistical signal processing. pp 166–169

    Google Scholar 

  68. Tsai A, Wells W, Warfield SK, Willsky A (2004) Level set methods in an EM framework for shape classification and estimation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’04), Saint-Malo, France, 26–30 September 2004, pp 1–9

    Google Scholar 

  69. Yang J, Duncan JS (2004) 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Med Image Anal 8(3):285–294

    PubMed  PubMed Central  Google Scholar 

  70. Krim H, Yezzi A Jr (2006) Statistics and analysis of shapes (modeling and simulation in science engineering and technology). Springer Science and Business Media LLC, Boston

    Google Scholar 

  71. Davies R, Twining C, Taylor C (2008) Statistical models of shape optimisation and evaluation. Springer-Verlag London Limited, London, UK

    Google Scholar 

  72. Rogowska J (2000) Overview and fundamentals of medical image segmentation. In: Bankman I (ed) Handbook of medical image processing and analysis. Elsevier, Amsterdam, The Netherlands, pp 69–85

    Google Scholar 

  73. Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc London Series B Biol Sci 207(1167):187–217

    CAS  Google Scholar 

  74. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    PubMed  CAS  Google Scholar 

  75. Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features based on Gabor filters. IEEE Trans Image Process 11(10):1160–1167

    PubMed  Google Scholar 

  76. Duda RO, Hart BE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15

    Google Scholar 

  77. Lester JM, Williams HA, Weintraub BA, Brenner JF (1978) Two graph searching techniques for boundary finding in white blood cell images. Comput Biol Med 8(4):293–308

    PubMed  CAS  Google Scholar 

  78. Thedens DR, Skorton DJ, Feagle SR (1995) Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging. IEEE Trans Med Imaging 14(1):42–55

    PubMed  CAS  Google Scholar 

  79. Martelli A (1972) Edge detection using heuristic search methods. Comput Graph Image Process 1(2):169–182

    Google Scholar 

  80. Digabel H, Lantuejoul C (1978) Iterative algorithms. In: Chermant J-L (ed) 2nd European symposium on quantitative analysis of microstructures in material science, biology and medicine, Riederer Verlag, Stuttgart, Germany, pp 85–99

    Google Scholar 

  81. Krivanek A, Sonka M (1998) Ovarian ultrasound image analysis: follicle segmentation. IEEE Trans Med Imaging 17(6):935–944

    PubMed  CAS  Google Scholar 

  82. Karvelis PS, Tzallas AT, Fotiadis DI, Georgiou I (2008) A multichannel watershed-based segmentation method for multispectral chromosome classification. IEEE Trans Med Imaging 27(5):697–708

    PubMed  CAS  Google Scholar 

  83. Ukil S, Reinhardt JM (2009) Anatomy-guided Lung lobe segmentation in X-Ray CT images. IEEE Trans Med Imaging 28(2):202–214

    PubMed  PubMed Central  Google Scholar 

  84. Alush A, Greenspan H, Goldberger J (2010) Automated and interactive lesion detection and segmentation in uterine cervix images. IEEE Trans Med Imaging 29(2):488–501

    PubMed  Google Scholar 

  85. Vincken KL, Koster ASE, Viergever MA (1997) Probabilistic multiscale image segmentation. IEEE Trans Pattern Anal Mach Intell 19(2):109–120

    Google Scholar 

  86. Snel JG, Venema HW, Grimbergen CA (1998) Detection of the carpal bone contours from 3-D MR images of the wrist using a planar radial scale-space snake. IEEE Trans Med Imaging 17(6):1063–1072

    PubMed  CAS  Google Scholar 

  87. Gauch JM (1999) Image segmentation and analysis via multiscale gradient watershed hierarchies. IEEE Trans Image Process 8(1):69–79

    PubMed  CAS  Google Scholar 

  88. Petrovic A, Escoda OD, Vandergheynst P (2004) Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations. IEEE Trans Image Process 13(8):1104–1114

    PubMed  Google Scholar 

  89. Li CH, Tam PKS (1998) An iterative algorithm for minimum cross-entropy thresholding. Pattern Recognit Lett 19(8):771–776

    Google Scholar 

  90. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–168

    Google Scholar 

  91. Wirjad O (2007) Survey of 3D image segmentation methods. Technical report, Fraunhofer ITWM, no. 123

    Google Scholar 

  92. Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vis Graph Image Process 29(1):100–132

    Google Scholar 

  93. Hojjatoleslami SA, Kruggel F (2001) Segmentation of large brain lesions. IEEE Trans Med Imaging 20:666–669

    PubMed  CAS  Google Scholar 

  94. Wan S-Y, Higgins WE (2003) Symmetric region growing. IEEE Trans Image Process 12(9):1007–1015

    PubMed  Google Scholar 

  95. Mendonca AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25:1200–1213

    PubMed  Google Scholar 

  96. Dehmeshki J, Member HA, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans Image Process 27(4):467–480

    CAS  Google Scholar 

  97. Zucker SW (1976) Region growing childhood and adolescence. Comput Graph Image Process 5(3):382–399

    Google Scholar 

  98. Bezdek JC, Hall LO, Clarke LP (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20(4):1033–1048

    PubMed  CAS  Google Scholar 

  99. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    Google Scholar 

  100. Bishop CM (2006) Pattern recognition and machine learning. Springer Science and Business Media LLC, Singapore

    Google Scholar 

  101. Scott DW (1992) Multivariate density estimation: theory, practice, and visualization. Wiley, New York

    Google Scholar 

  102. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, Canada

    Google Scholar 

  103. McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York

    Google Scholar 

  104. Frey BJ, Jojic N (2005) A comparison of algorithms for inference and learning in probabilistic graphical models. IEEE Trans Pattern Anal Mach Intell 27(9):1392–1416

    PubMed  Google Scholar 

  105. Farag AA, El-Baz AS, Gimel’farb G (2006) Precise segmentation of multimodal images. IEEE Trans Image Process 15(4):952–968

    PubMed  Google Scholar 

  106. El-Baz A, Gimel’farb G (2009) Robust medical images segmentation using learned shape and appearance models. In: Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’09), London, UK, 20–24 September 2009, pp 281–288

    Google Scholar 

  107. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th international conference on machine learning (ICML01), Williamstown, MA, 28 June–1 July 2001, pp 282–289

    Google Scholar 

  108. Kim J, Fisher JW, Tsai A, Wible C, Willsky A, Wells WM (2000) Incorporating spatial priors into an information theoretic approach for fMRI data analysis. In: Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’2000), Pennsylvania, 11–14 October 2000, pp 62–71.

    Google Scholar 

  109. Boykov Y, Jolly M-P (2000) Interactive organ segmentation using graph cuts. In: Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’2000), Pennsylvania, 11–14 October 2000, pp 276–286

    Google Scholar 

  110. Kim J, Zabih R (2003) Automatic segmentation of contrast-enhanced image sequences. In: Proceedings of the international conference on computer vision (ICCV’03), Nice, France, 13–16 October 2003, pp 502–509

    Google Scholar 

  111. Boykov YY, Jolly M-P (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proceedings of the 8th IEEE international conference on computer vision (ICCV’01), Vancouver, BC, Canada, 7–14 July 2001, pp 105–112

    Google Scholar 

  112. Freedman D, Drineas P (2005) Energy minimization via graph cuts: settling what is possible. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR’05), San Diego, CA, 20–26 June 2005, vol II, pp 939–946

    Google Scholar 

  113. Kolmogorov V, Zabih R (2002) Multicamera scene reconstruction via graph cuts. In: Proceedings of the 7th European conference on computer vision (ECCV’02), Denmark, Copenhagen, 27 May–2 June 2002, part III, pp 82–96

    Google Scholar 

  114. Kolmogorov V, Zabih R (2004) What energy functions can be minimized via graph cuts? IEEE Trans Pattern Anal Mach Intell 26(2):147–159

    PubMed  Google Scholar 

  115. Greig DM, Porteous BT, Seheult AH (1989) Exact maximum a posteriori estimation for binary images. J R Stat Soc Series B Methodol 51(2):271–279

    Google Scholar 

  116. Roy S, Cox IJ (1998) A maximum-flow formulation of the n-camera stereo correspondence problem. In: Proceedings of the 6th international conference on computer vision (ICCV’98), Bombay, India, 4–7 January 1998, pp 492–499

    Google Scholar 

  117. Hartmann SL, Parks MH, Martin PR, Dawant BM (1999) Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and freeform transformations: part II, validation on severely atrophied brains. IEEE Trans Med Imaging 18(10):917–926

    PubMed  CAS  Google Scholar 

  118. Marroquin JL, Vemuri BC, Botello S, Calderon F, Fernandez-Bouzas A (2002) An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans Med Imaging 21(8):934–945

    PubMed  CAS  Google Scholar 

  119. Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM (2003) Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. Acad Radiol 10(3):255–265

    PubMed  Google Scholar 

  120. Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492

    PubMed  Google Scholar 

  121. Rohlfing T, Russakoff DB, Maurer CR (2004) Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans Med Imaging 23(8):983–994

    PubMed  Google Scholar 

  122. Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure J-G, Thiran J-P (2004) Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imaging 23(10):1301–1314

    PubMed  Google Scholar 

  123. Sluimer I, Prokop M, van Ginneken B (2005) Toward automated segmentation of the pathological lung in CT. IEEE Trans Med Imaging 24(8):1025–1038

    PubMed  Google Scholar 

  124. Stancanello J, Romanelli P, Modugno N, Cerveri P, Ferrigno G, Uggeri F, Cantore G (2006)Atlas-based identification of targets for functional radiosurgery. Med Phys 33(6):1603–1611

    PubMed  Google Scholar 

  125. Zhang L, Hoffman EA, Reinhardt JM (2006) Atlas-driven lung lobe segmentation in volumetric X-Ray CT images. IEEE Trans Med Imaging 25(1):1–16

    PubMed  Google Scholar 

  126. O’Donnell LJ, Westin C-F (2007) Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans Med Imaging 26(11):1562–1575

    PubMed  Google Scholar 

  127. Han X, Fischl B (2007) Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans Med Imaging 26(4):479–486

    PubMed  Google Scholar 

  128. Bello M, Ju T, Carson J, Warren J, Chiu W, Kakadiaris IA (2007) Learning-based segmentation framework for tissue images containing gene expression data. IEEE Trans Med Imaging 26(5):728–744

    PubMed  Google Scholar 

  129. Leemput KV (2009) Encoding probabilistic brain atlases using Bayesian inference. IEEE Trans Med Imaging 28(6):822–837

    PubMed  Google Scholar 

  130. Sabuncu MR, Balci SK, Shenton ME, Golland P (2009) Image-driven population analysis through mixture modeling. IEEE Trans Med Imaging 28(9):1473–1487

    PubMed  PubMed Central  Google Scholar 

  131. Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, van Ginneken B (2009) Multiatlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging 28(7):1000–1010

    PubMed  Google Scholar 

  132. Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solorzano C (2009) Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging 28(8):1266–1277

    PubMed  Google Scholar 

  133. van Rikxoort EM, Prokop M, de Hoop B, Viergever MA, Pluim J, van Ginneken B (2010) Automatic segmentation of pulmonary lobes robust against incomplete fissures. IEEE Trans Med Imaging 29(6):1286–1296

    PubMed  Google Scholar 

  134. Rohlfing T, Brandt R, Menzel R, Russakoff DB, Maurer CR Jr (2005) Quo vadis, atlas-based segmentation? In: Suri JS, Wilson DL, Laxminarayan S (eds) Handbook of Biomedical Image Analysis, vol III: Registration Models. Kluwer Academic/Plenum Publishers, New York, pp 435–486, chapter 11

    Google Scholar 

  135. Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226–239

    Google Scholar 

  136. Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Google Scholar 

  137. Terzopoulos D (1987) On matching deformable models to images. Technical report no. 60, Schlumberger Palo Alto research, Palo Alto, CA, 1986. Reprinted in Topical meeting on machine vision, technical digest series, vol 12. pp 160–163

    Google Scholar 

  138. Terzopoulos D, Fleischer K (1988) Deformable models. Vis Comput 4:306–331

    Google Scholar 

  139. Terzopoulos D, Witkin A, Kass M (1988) Constraints on deformable models: recovering 3D shape and nonrigid motion. Artif Intell 36:91–123

    Google Scholar 

  140. Amini AA, Weymouth TE, Jain RC (1990) Using dynamic programming for solving variational problems in vision. IEEE Trans Pattern Anal Mach Intell 12(9):855–867

    Google Scholar 

  141. Terzopoulos D, Metaxas D (1991) Dynamic 3-D models with local and global deformations: deformable superquadrics. IEEE Trans Pattern Anal Mach Intell 13(7):703–714

    Google Scholar 

  142. Staib LH, Duncan JS (1992) Boundary finding with parametrically deformable models. IEEE Trans Pattern Anal Mach Intell 14(11):1061–1075

    Google Scholar 

  143. Staib LH, Duncan JS (1992) Deformable Fourier models for surface finding in 3-D images. Vis Biomed Comput 14:90–104

    Google Scholar 

  144. Mortensen E, Morse B, Barrett W, Udupa J (1992) Adaptive boundary detection using livewire two dimensional dynamic programming. In: Proceedings of computers in cardiology, Durham, NC, 11–14 October 1992, pp 635–638

    Google Scholar 

  145. Williams DJ, Shah M (1992) A fast algorithm for active contour and curvature estimation. Comput Vis Image Process Understand 55(1):14–26

    Google Scholar 

  146. McInerney T, Terzopoulos D (1995) Topologically adaptable snakes. In: Proceedings of 5th international conference on computer vision (ICCV’95), Cambridge, MA, 20–23 June 1995, pp 840–845

    Google Scholar 

  147. McInerney T, Terzopoulos D (1995) A dynamic finite surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. Comput Med Imaging Graph 19(1):69–83

    PubMed  CAS  Google Scholar 

  148. Davatzikos CA, Prince JL (1995) An active contour model for mapping the cortex. IEEE Trans Med Imaging 14(1):65–80

    PubMed  CAS  Google Scholar 

  149. Lobregt S, Viergever MA (1995) A discrete dynamic contour model. IEEE Trans Med Imaging 14(1):12–24

    PubMed  CAS  Google Scholar 

  150. Staib LH, Duncan JS (1996) Model-based deformable surface finding for medical images. IEEE Trans Med Imaging 15(5):720–731

    PubMed  CAS  Google Scholar 

  151. McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108

    PubMed  CAS  Google Scholar 

  152. Szekely G, Kelemen A, Brechbuhler C, Gerig G (1996) Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models. Med Image Anal 1(1):19–34

    PubMed  CAS  Google Scholar 

  153. Yezzi A Jr, Kichenassamy S, Kumar A, Olver P, Tennenbaum A (1997) A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imaging 16(2):199–209

    PubMed  Google Scholar 

  154. Klein A, Lee F, Amini AA (1997) Quantitative coronary angiography with deformable spline models. IEEE Trans Med Imaging 16(5):468–482

    PubMed  CAS  Google Scholar 

  155. Grzeszczuk RP, Levin DN (1997) Brownian strings: segmenting images with stochastically deformable contours. IEEE Trans Pattern Anal Mach Intell 19(10):1100–1114

    Google Scholar 

  156. Cootes TF, Edwards GJ, Taylor CJ (1998) Active appearance models. In: Proceedings of the 5th European conference on computer vision (ECCV’98), Freiburg, Germany, 2–6 June 1998, vol 2, pp 484–498

    Google Scholar 

  157. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    PubMed  CAS  Google Scholar 

  158. Mikic I, Krucinski S, Thomas JD (1998) Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates. IEEE Trans Med Imaging 17(2):274–284

    PubMed  CAS  Google Scholar 

  159. Atkins MS, Mackiewich BT (1998) Fully automatic segmentation of the brain in MRI. IEEE Trans Med Imaging 17(1):98–107

    PubMed  CAS  Google Scholar 

  160. Lachaud J-O, Montanvert A (1999) Deformable meshes with automated topology changes for coarse-to-fine three-dimensional surface extraction. Med Image Anal 3(2):187–207

    PubMed  CAS  Google Scholar 

  161. McInerney T, Terzopoulos D (1999) Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Trans Med Imaging 18(10):840–850

    PubMed  CAS  Google Scholar 

  162. McInerney T, Terzopoulos D (2000) T-snakes: topology adaptive snakes. Med Image Anal 4(2):73–91

    PubMed  CAS  Google Scholar 

  163. Falcao AX, Udupa JK (2000) A 3D generalization of user-steered live-wire segmentation. Med Image Anal 4(4):389–402

    PubMed  CAS  Google Scholar 

  164. Falcao AX, Udupa JK, Miyazawa FK (2000) An ultra-fast user-steered image segmentation paradigm: live wire on the fly. IEEE Trans Med Imaging 19(1):55–62

    PubMed  CAS  Google Scholar 

  165. Mokhtarian F, Mohanna F (2001) Fast active contour convergence through curvature scale space filtering. In: Proceedings of image and vision computing New Zealand, Dunedin, New Zealand, 2001, pp 157–162

    Google Scholar 

  166. Fenster SD, Kender JR (2001) Sectored snakes: evaluating learned-energy segmentations. IEEE Trans Pattern Recognit Mach Intell 23(9):1028–1034

    Google Scholar 

  167. Ghanei A, Soltanian-Zadeh H (2002) A discrete curvature-based deformable surface model with application to segmentation of volumetric images. IEEE Trans Inf Technol Biomed 9(4):285–295

    Google Scholar 

  168. Snel JG, Venema HW, Grimbergen CA (2002) Deformable triangular surfaces using fast 1-D radial Lagrangian dynamics-segmentation of 3-D MR and CT images of the wrist. IEEE Trans Med Imaging 21(8):888–903

    PubMed  Google Scholar 

  169. Goldenberg R, Kimmel R, Rivlin E, Rudzsky M (2002) Cortex segmentation: a fast variational geometric approach. IEEE Trans Med Imaging 21(12):1544–1551

    PubMed  Google Scholar 

  170. McInerney T, Hamarneh G, Shenton M, Terzopoulos D (2002) Deformable organisms for automatic medical image analysis. Med Image Anal 6(3):251–266

    PubMed  PubMed Central  Google Scholar 

  171. Sermesant M, Forest C, Pennec X, Delingette H, Ayache N (2003) Deformable biomechanical models: application to 4D cardiac image analysis. Med Image Anal 7(4):457–488

    Google Scholar 

  172. Madabhushi A, Metaxas DN (2003) Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imaging 22(2):155–169

    PubMed  Google Scholar 

  173. Brusseau E, de Korte CL, Mastik F, Schaar J, van der Steen AFW (2004) Fully automatic luminal contour segmentation in intracoronary ultrasound imaging-A statistical approach. IEEE Trans Med Imaging 23(5):554–566

    PubMed  Google Scholar 

  174. Yao J, Miller M, Franaszek M, Summers RM (2004) Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models. IEEE Trans Med Imaging 23(11):1344–1352

    PubMed  Google Scholar 

  175. Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, Kennedy L (2004) Optic nerve head segmentation. IEEE Trans Med Imaging 23(2):256–264

    PubMed  Google Scholar 

  176. Ghebreab S, Smeulders AWM (2004) Combining strings and necklaces for interactive three-dimensional segmentation of spinal images using an integral deformable spine model. IEEE Trans Med Imaging 51(10):1821–1829

    Google Scholar 

  177. Pang B, Zhang D, Wang K (2005) The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine. IEEE Trans Med Imaging 24(8):946–956

    PubMed  Google Scholar 

  178. Kaus MR, von Berg J, Weese J, Niessen W, Pekar V (2004) Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 8(3):245–254

    PubMed  Google Scholar 

  179. de Bruijne M, van Ginneken B, Viergever MA, Niessen WJ (2004) Interactive segmentation of abdominal aortic aneurysms in CTA images. Med Image Anal 8:127–138

    PubMed  Google Scholar 

  180. Montagnat J, Delingette H (2005) 4D deformable models with temporal constraints: application to 4D cardiac image segmentation. Med Image Anal 9:87–100

    PubMed  Google Scholar 

  181. Chen T, Metaxas D (2005) A hybrid framework for 3D medical image segmentation. Med Image Anal 9(6):547–565

    PubMed  Google Scholar 

  182. Chrastek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, Lausen B, Lammer R, Mardin CY, Michelson G (2005) Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Image Anal 9(4):297–314

    PubMed  CAS  Google Scholar 

  183. Olabarriaga SD, Rouet J-M, Fradkin M, Breeuwer M, Niessen WJ (2005) Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling. IEEE Trans Med Imaging 24(4):477–485

    PubMed  Google Scholar 

  184. Zagrodsky V, Walimbe V, Castro-Pareja CR, Qin JX, Song J-M, Shekhar R (2005) Registration-assisted segmentation of real-time 3-D echocardiographic data using deformable models. IEEE Trans Med Imaging 24(9):1089–1099

    PubMed  Google Scholar 

  185. Freedman D, Radke RJ, Zhang T, Jeong Y, Lovelock DM, Chen GTY (2005) Model-based segmentation of medical imagery by matching distributions. IEEE Trans Med Imaging 24(3):281–292

    PubMed  Google Scholar 

  186. Dufour A, Shinin V, Tajbakhsh S, Olivo-Marin J-C, Guillen-Aghion N, Zimmer C (2005) Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans Med Imaging 14(9):1396–1410

    Google Scholar 

  187. Zhan Y, Shen D (2006) Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. IEEE Trans Med Imaging 25(3):256–272

    PubMed  Google Scholar 

  188. Xu M, Thompson PM, Toga AW (2006) Adaptive reproducing kernel particle method for extraction of the cortical surface. IEEE Trans Med Imaging 25(6):755–767

    PubMed  CAS  Google Scholar 

  189. van Assen HC, Danilouchkine MG, Frangi AF, Ordas S, Westenberg JJM, Reiber JHC, Lelieveldt BPF (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10:286–303

    PubMed  Google Scholar 

  190. Li B, Acton ST (2007) Active contour external force using vector field convolution for image segmentation. IEEE Trans Image Process 16(8):2096–2106

    PubMed  Google Scholar 

  191. Huang X, Metaxas DN (2008) Metamorphs: deformable shape and appearance models. IEEE Trans Pattern Anal Mach Intell 30(8):1444–1459

    PubMed  Google Scholar 

  192. Bernard O, Friboulet D, Thevenaz P, Unser M (2009) Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. IEEE Trans Image Process 18(6):1179–1191

    PubMed  Google Scholar 

  193. Fripp J, Crozier S, Warfield SK, Ourselin S (2010) Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans Med Imaging 29(1):55–64

    PubMed  Google Scholar 

  194. Manzke R, Meyer C, Ecabert O, Peters J, Noordhoek NJ, Thiagalingam A, Reddy VY, Chan RC, Weese J (2010) Automatic segmentation of rotational X-Ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures. IEEE Trans Med Imaging 29(2):260–272

    PubMed  Google Scholar 

  195. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulation. J Comput Phys 79:12–49

    Google Scholar 

  196. N Paragios, R Deriche (1999) Unifying boundary and region-based information for geodesic active tracking. In: Proceedings of the IEEE international conference in computer vision and pattern recognition (CVPR’99), Collins, CO, 23–25 June 1999, vol 2, pp 300–305

    Google Scholar 

  197. Ma W-Y, Manjunath BS (2000) Edge flow: a technique for boundary detection and image segmentation. IEEE Trans Image Process 9(8):1375–1388

    PubMed  CAS  Google Scholar 

  198. Shen D, Davatzikos C (2000) An adaptive-focus deformable model using statistical and geometric information. IEEE Trans Pattern Anal Mach Intell 22(8):906–913

    Google Scholar 

  199. Tsai A, Yezzi A Jr, Willsky A (2001) Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans Image Process 10(8):1169–1186

    PubMed  CAS  Google Scholar 

  200. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    PubMed  CAS  Google Scholar 

  201. Baillard C, Hellier P, Barillot C (2001) Segmentation of brain 3D MR images using level sets and dense registration. Med Image Anal 5(3):185–194

    PubMed  CAS  Google Scholar 

  202. Lorigo LM, Faugeras OD, Grimson WEL, Keriven R, Kikinis R, Nabavi A, Westin C-F (2001) CURVES: curve evolution for vessel segmentation. Med Image Anal 5:195–206

    PubMed  CAS  Google Scholar 

  203. Carson C, Belongie S, Greenspan H, Malik J (2002) Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans Pattern Anal Mach Intell 24(8):1026–1038

    Google Scholar 

  204. Chen Y, Tagare HD, Thiruvenkadam S, Huang F, Wilson D, Gopinath KS, Briggs RW, Geiser EA (2002) Using prior shapes in geometric active contours in a variational framework. Int J Comput Vis 50(3):315–328

    Google Scholar 

  205. Sebastian TB, Tek H, Crisco JJ, Kimia BB (2003) Segmentation of carpal bones from CT images using skeletally coupled deformable models. Med Image Anal 7(1):21–45

    PubMed  Google Scholar 

  206. Vemuri BC, Ye J, Chen Y, Leonard CM (2003) Image registration via level-set motion: applications to atlas-based segmentation. Med Image Anal 7(1):1–20

    PubMed  CAS  Google Scholar 

  207. Yezzi A, Zollei L, Kapur T (2003) A variational framework for integrating segmentation and registration through active contours. Med Image Anal 7(2):171–185

    PubMed  CAS  Google Scholar 

  208. Cates JE, Lefohn AE, Whitaker RT (2004) GIST: an interactive, GPU-based level set segmentation tool for 3D medical images. Med Image Anal 8(3):217–231

    PubMed  Google Scholar 

  209. Kim J, Fisher JW III, Yezzi A, Cetin M, Willsky AS (2005) A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans Med Imaging 14(10):1486–1502

    Google Scholar 

  210. Tsai A, Wells WM, Warfield SK, Willsky AS (2005) An EM algorithm for shape classification based on level sets. Med Image Anal 9(5):491–502

    PubMed  Google Scholar 

  211. Yan P, Kassim AA (2006) Segmentation of volumetric MRA images by using capillary active contour. Med Image Anal 10:317–329

    PubMed  Google Scholar 

  212. Manniesing R, Velthuis BK, van Leeuwen MS, van der Schaaf IC, van Laar PJ, Niessen WJ (2006) Level set based cerebral vasculature segmentation and diameter quantification in CT angiography. Med Image Anal 10(2):200–214

    PubMed  CAS  Google Scholar 

  213. Holtzman-Gazit M, Kimmel R, Peled N, Goldsher D (2006) Segmentation of thin structures in volumetric medical images. IEEE Trans Image Process 15(2):354–363

    PubMed  Google Scholar 

  214. Osher S, Fedkiw R (2006) Level set methods and dynamic implicit surfaces, applied mathematical sciences, vol 153. Springer, New York

    Google Scholar 

  215. Chang H-H, Valentino DJ, Duckwiler GR, Toga AW (2007) Segmentation of brain MR images using a charged fluid model. IEEE Trans Biomed Eng 54(10):1798–1813

    PubMed  PubMed Central  Google Scholar 

  216. Abd El Munim HE, Farag AA (2007) Curve/surface representation and evolution using vector level sets with application to the shape-based segmentation problem. IEEE Trans Pattern Anal Mach Intell 29(6):945–958

    PubMed  Google Scholar 

  217. Gelas A, Bernard O, Friboulet D, Prost R (2007) Compactly supported radial basis functions based collocation method for level-set evolution in image segmentation. IEEE Trans Image Process 16(7):1873–1887

    PubMed  Google Scholar 

  218. Sun W, Cetin M, Chan R, Willsky AS (2008) Learning the dynamics and time-recursive boundary detection of deformable objects. IEEE Trans Image Process 17(11):2186–2200

    PubMed  Google Scholar 

  219. Law YN, Lee HK, Yip AM (2008) A multiresolution stochastic level set method for Mumford-Shah image segmentation. IEEE Trans Image Process 17(12):2289–2300

    PubMed  Google Scholar 

  220. Cheng K, Gu L, Wu J, Li W, Xu J (2008) A novel level set based shape prior method for liver segmentation from MRI images. In: Proceedings of the 4th international workshop on medical imaging and augmented reality, Tokyo, Japan, 2008, pp 150–159

    Google Scholar 

  221. Gooya A, Liao H, Matsumiya K, Masamune K, Masutani Y, Dohi T (2008) A variational method for geometric regularization of vascular segmentation in medical images. IEEE Trans Image Process 17(8):1295–1312

    PubMed  Google Scholar 

  222. Krinidis S, Chatzis V (2009) Fuzzy energy-based active contours. IEEE Trans Image Process 18(12):2747–2755

    PubMed  Google Scholar 

  223. Huang A, Abugharbieh R, Tam R (2009) A hybrid geometric-statistical deformable model for automated 3-D segmentation in brain MRI. IEEE Trans Biomed Eng 56(7):1838–1848

    PubMed  PubMed Central  Google Scholar 

  224. Wu X, Luboz V, Krissian K, Cotin S, Dawson S (2011) Segmentation and reconstruction of vascular structures for 3D real time simulation. Med Image Anal 15(1):22–34

    PubMed  Google Scholar 

  225. Vukadinovic D, van Walsum T, Manniesing R, Rozie S, Hameeteman R, de Weert TT, van der Lugt A, Niessen WJ (2010) Segmentation of the outer vessel wall of the common carotid artery in CTA. IEEE Trans Med Imaging 29(1):65–76

    PubMed  Google Scholar 

  226. Duan C, Liang Z, Bao S, Zhu H, Wang S, Zhang G, Chen JJ, Lu H (2010) A coupled level set framework for bladder wall segmentation with application to MR cystography. IEEE Trans Med Imaging 29(3):903–915

    PubMed  PubMed Central  Google Scholar 

  227. Khalifa F, El-Baz A, Gimelfarb G, Ousephand R, Abu El-Ghar M (2010) Shape-appearance guided level-set deformable model for image segmentation. In: IEEE international conference on pattern recognition (ICPR’2010), Istanbul, Turkey, August 2010 [in print]

    Google Scholar 

  228. Xu C, Pham DL, Prince JL (2000) Image segmentation using deformable models. In: Fitzpatrick JM, Sonka M (eds) Handbook on medical imaging. SPIE Press, Bellingham, WA, pp 129–174, Chapter 3

    Google Scholar 

  229. Jaklic A, Leonardis A, Solina F (2000) Segmentation and recovery of superquadrics. Kluwer Academic Publisher, Dordrecht, Netherland

    Google Scholar 

  230. Persoon E, Fu KS (1977) Shape discrimination using Fourier descriptors. IEEE Trans Syst Man Cybern 7(3):170–179

    Google Scholar 

  231. Zienkiewicz OC, Taylor RL, Zhu JZ (2005) The finite element method: its basis and fundamentals, 6th edn. Elsevier, Oxford, UK

    Google Scholar 

  232. Boykov Y, Kolmogorov V (2003) Computing geodesics and minimal surfaces via graph cuts. In: Proceedings of 9th IEEE international conference on computer vision (ICCV’03), Nice, France, 13–16 October 2003, vol 1, p 2633

    Google Scholar 

  233. Suri JS, Liu K, Singh S, Laxinaryan SN, Zeng X, Reden L (2002) Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed 6(1):8–28

    PubMed  Google Scholar 

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    Ayman El-Baz

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    Ayman S. El-Baz

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    Jasjit S. Suri

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Elnakib, A., Gimel’farb, G., Suri, J.S., El-Baz, A. (2011). Medical Image Segmentation: A Brief Survey. In: El-Baz, A., Acharya U, R., Laine, A., Suri, J. (eds) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8204-9_1

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