Computer vision and pattern recognition techniques for 2-D and 3-D MR cerebral cortical segmentation (Part I): A state-of-the-art review

Pattern Analysis and Applications

Volumn 5, Issue 1, 2002, Pages 46-76

  Suri, Jasjit S ^a   Singh, Sameer ^b   Reden, Laura ^a  

^a Marconi Medical Systems   (United States)

^b UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

2 D; 3 D; Boundary Surface based; Cortex; Cortical thickness; Deformable models; Fusion; Grey matter; Level sets; MRI; Partial Differential Equations (PDEs); Segmentation; Taxonomy; White matter

Indexed keywords

EID: 0036989044     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s100440200005     Document Type: Review

References (237)

1. Suri JS. Two dimensional fast MR brain segmentation using a region-based level set approach. International Journal of Engineering in Medicine and Biology, 2001
2. Suri JS, Setarehdan SK, Singh S. Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology. 2001
3. Hashemi RH, Bradley WG Jr. MRI: The Basics Williams and Wilkins, Baltimore. MD, 1997
4. Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic Resonance Imaging: Physical Principles and Sequence Design. Wiley, New York, 1999
5. Cohen MS, Weiskopf RM. Ultra-fast imaging. Magnetic Resonance in Medicine 1991; 9(1): 1-37
6. Zeng X, Staib LH, Schultz RT, Duncan JS. Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation. IEEE Trans Medical Imaging 1999; 18(10): 927-937
7. Crespo-Facorro B, Kim I, Andreasen NC, Spinks R, O'Leary DS, Bockholt HJ, Harris G, Magnotta VA. Cerebral cortex: a topographic segmentation method using magnetic resonance imaging. Psychiatry Research 2000: 100(2): 97-126
8. DeCarli C. US Patent #: 5,262,943. Method for Quantification of Brain Volume From MRI. November 16 1993
9. Rusinek H, deLeon MJ, Goerge AE, Stylopoulos LA, Chandra R, Smith G, Rand T, Mourino M, Kowalski H. Alzheimer's disease: Measuring loss of cerebral gray matter with MR imaging. Radiology 1991; 178(1): 109-114
10. Arnold D, Riess G, Mathews P, Collins D, Francis G, Antel J. Quantification of disease load and progression in multiple sclerosis by means of proton MR spectroscopy. Proceedings of SMRM. 11th Annual Meeting, Berlin, Society SMRM, 1992; 1911
11. Johnston B, Atkins MS, Mackiewich B, Anderson M. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans Medical Imaging 1996; 14(2): 154-169
12. Vaidyanathan M, Clarke LP, Hall LO, Heidtman C, Velthuizen R, Gosche K, Phuphanich S, Wagner H, Greenburg H, Silbiger ML. Monitoring brain tumor response to therapy using MRI segmentation. Magnetic Resonance Imaging 1997; 13(3): 323-334
13. Clarke LP, Velthuizen RP, Clark M, Gaviria G, Hall LO, Goldgof D, Murtagh K, Phuphanich S, Brem S. MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. Magnetic Resonance Imaging 1998; 16(3): 271-279
14. Höhue KH, Hanson W.A. Interactive 3-D segmentation of MRI and CT volumes using morphological operations. Journal of Computer Assisted Tomography 1992; 16(2): 285-294
15. Galloway RL, Maciunas RJ, Edwards CA. Interactive image guided neurosurgery. IEEE Trans Biomedical Engineering 1992; 39(12): 1126-1231
16. Gerig G, Martin J, Kikinis R, Kubler O, Shenton M, Jolesz FA. Unsupervised tissue type segmentation of 3-D dual MR head data. Image and Vision Computing 1992; 10(6): 349-360
17. Kikinis R, Shenton M, Jolesz F, Gerig G, Martin J. Anderson M, Metcalf D, Guttmann C, McCarley RW, Lorenson W, Cline H. Routine quantitative analysis of brain and CSF with MR imaging. Journal of Magnetic Resonance in Medicine 1992; 2(6): 619-629
18. Griffin LD. The intrinsic geometry of the cerebral cortex. Journal of Theoretical Biology 1994; 166(3): 261-273
19. England MA, Wakely J. A Colour Atlas of the Brain and Spinal Cord, An Introduction to Normal Neuroanatomy. Wolfe Publishing, London, 1991
20. Fischbach GD. Mind and brain. Scientific American 1992; 267(3): 48-57
21. Jones EG, Peters A, Cerebral Cortex, Comparative Structure and Evolution of Cerebral Cortex, Part II. Vol. 8B. Plenum Press. London, 1990
22. Hendelman WJ. Stndent's Atlas of Neuroanatomy. WB Saunders, 1994
23. Mai K. Atlas of the Human Brain. Academic Press. 1997
24. Hofman MA. Size and shape of the cerebral cortex in mammals I, The cortical surface. Brain Behavior Evolution 1985; 27(1): 28-40
25. Hofman MA. On the evolution and geometry of the brain, in mammals. Prog Neurobiology 1989; 32(2): 137-158
26. Todd PH. A geometric model for the cortical folding pattern of simple folded brains. Journal Theoretical Biology 1982; 97(3): 529-538
27. Prothero JW, Sundesten JW. Folding of the cerebral cortex, A scaling model. Brain Behavior Evolution 1984; 24(2-3): 152-167
28. Meyer CR, Bland PH, Pipe J. Retrospective correction of intensity inhomogeneities in MRI. IEEE Trans Medical Imaging 1995; 14(1): 36-41
29. Sled JG, Zijdenbos AP, Evans AC. A non-parametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Medical Imaging 1998: 17(1): 87-97
30. Nyul LG, Udupa JK. On standardizing the MR image intensity scale. Magnetic Resonance Medicine (MRM) 1999; 42(6): 1072-1081
31. Netter FH. Atlas of Human Anatomy. Netter FH, Colacino, S. (eds) Summit, NJ, CIBA GEIGY Corp., 1989
32. Rademacher J, Caviness VS, Steinmetz H. Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping and neurobiology. Cerebral Cortex 1993; 3(4): 313-329
33. Velthuizen RP, Hall LO, Clarke LP. Feature extraction fur MRI segmentation. Journal Neuroimaging 1999; 9(2): 85-90
34. Menhardt W, Schmidt KH. Computer vision on MRI. Pattern Recognition Letters 1998; 8(2): 73-85
35. Stytz MR, Frieder G, Frieder O. Three-dimensional medical imaging: algorithms and computer systems. ACM Computer Survey, 1991: 23(4): 421-499
36. Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hal LO, Thatcher RW, Silbiger ML. Review of MRI segmentation: Methods and Applications. Magnetic Resonance Imaging 1995; 13(3): 343-368
37. Golay X, Kollias S, Stoll G, Meier D, Yalavanis A. A new correlation-based fuzzy logic clustering algorithm for fMRI. Magnetic Resonance in Medicine 1998; 40(2): 249-260
38. Fu KS, Mui JK. A survey on image segmentation. Journal of Pattern Recognition 1981; 13(1): 3-16
39. Haralick RM, Shapiro LG. A survey: image segmentation techniques. Journal of Computer Vision, Graphics and Image Processing 1985; 2(1): 100-132
40. Davis LS. A survey of edge detection techniques. Computer Graphics and Image Processing 1975; 4(3): 248-270
41. Binford TO. Survey of model-based image analysis systems. International Journal of Robotics Research 1982; 1(1): 18-64
42. Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognition 1993: 26(9): 1277-1294
43. Suri JS. Computer vision, pattern recognition, and image processing in left ventricle segmentation: last 50 years. Journal of Pattern Analysis and Applications 2000; 3(3): 209-242
44. Kong T, Rosenfield A. Digital topology: introduction and survey. Computer Vision, Graphics and Image Processing 1989; 48(3): 357-393
45. Saeed N. Magnetic resonance image segmentation using pattern recognition and applied to image registration and quantification. NMR in Biomedicine 1998; 11(4-5): 157-167
46. Barillot C, Gibaud B, Lis O, Luo LM, Bouliou A, LeCerten G, Collorec R. Coatrieux JL. Computer graphics in medicine: a survey. CRC Critical Reviews in Biomedical Engineering 1988; 15(4): 269-307
47. Barillot C. Surface and volume rendering techniques to display 3-D data, an overview of basic principles shows advances in display techniques. IEEE Engineering in Medicine and Biology 1993:12(1): 111-119
48. Zucker SW. Region growing: childhood and adolescence. Computer Graphics and Image Processing (CGIP) 1976; 5(3): 382-399
49. Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Analysis and Machine Intelligence 1994; 16(6): 641-647
50. Justice RK, Stokely EM, Strobel JS, Ideker RE, Smith WM. Medical image segmentation using 3-D seeded region growing. Proceedings of SPIE - The International Society for Optical Engineering 1997; 3034: 900-910
51. Liu A, Pizer S, Eberly D, Morse B, Rosenman J, Chaney E, Bullitt F, Carrasco V. Volume registration using the 3-D core. Proceedings of SPIE - The International Society for Optical Engineering 1994; 2359: 217-226
52. Crimson WEL, Ettinger GJ, Kapur T, Leventon ME, Wells III WM, Kikinis P. Utilizing segmented MR1 data in image guided surgery. International Journal of Pattern Recognition and Artificial Intelligence 1998; 11(8): 1367-1397
53. Talairach J, Szikla G, Tournoux P, Prosalentis A, Bornas-Ferrier M. Atlas d'Anatomie Stiriotaxique du Tilenciphale. Masson (Thieme Medical Publishers), 1967
54. Talairach J, Tournoux P. Co-planar Stereotactic Atlas of the Human Brain, 3-D Proportional System, An Approach to Cerebral Imaging. Georg Thieme Verlag, 1988
55. Bohm C, Grertz, T, Thurfjell L. The role of anatomic information in quantifying functional neuroimaging data. Journal of Neural Transmission (Suppl.) 1992; 37: 67-78
56. Seitz R, Bohm C, Greitz T, Roland P, Eriksson L, Blomqvist B, Rosenqvist B, Nordell B. Accuracy and precision of the computerized brain Atlas programme for localization and quantification in PET. Journal Cerebral Blood Flow Metabolics 1990: 10(4): 443-457
57. Evans, AC, Marrett S, Torrescorzo J, Ku S, Collins L. MRI-PET correlation in the 3-D using a VOI atlas. Journal Cerebral Blood Flow Metabolics 1991: 11(2): A69-78
58. Bajcsy R, Kosvacis S. Multiresolution elastic matching. Computer Vision, Graphics and Image Processing 1989; 46(1): 1-21
59. Van Essen DC, Drury HA. Structural and functional analyses of human cerebral cortex using a surface based atlas. Journal of Neuroscience 1997; 17(18): 7079-7102
60. Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping 1995: 3(3): 190-208
61. Gibaud B, Garlatti S, Barillot C, Faure E. Computerised brain atlases vs. decision support systems: a methodological approach. Artificial Intelligence in Medicine 1998; 14(1): 83-100
62. Sandor S, Leahy R. Surface-based labelling of cortical anatomy using a deformable atlas. IEEE. Trans Medical Imaging 1997; 16(1): 41-54
63. Sandor SR. Atlas Guided Deformable Models for Automatic Labelling of MR Brain Images. PhD Thesis, University of Southern California, Los Angles, CA, 1994
64. Ferrant M, Cuisenaire O, Macq Benoit M. Multi object segmentation of brain structures in 3-D MRI using a computerized atlas. Proceedings of SPIE - The International Society for Optical Engineering, Medical Imaging 1999; 3661: 986-995
65. Lim KO, Pfefferbaum A. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. Journal of Computer Assisted Tomography (JCAT) 1989; 13(4): 388-593
66. Harris GJ, Barta PE, Pengk LW, Lee S, Brettschneider PD, Shah A, Henderer JD, Schlaepfer TE, Pearlson GD: MR volume segmentation of gray matter and white matter using manual thresholding: dependence on image brightness. American Journal of Neuro Radiology (AJNR) 1994; 15(2): 225-230
67. Falco A, Uudpa JK, Samarasekera S, Sharma S, Hirsch BE, De Lotufo RA. User-steered image segmentation paradigms: live wire and live lane. Graphical Models and Image Processing 1998; 60(4): 233-260
68. Weszka JS, Rosenfeld A. Threshold evaluation techniques. IEEE Trans System, Man and Cybernetics 1978; 8(8): 622-629
69. Geman S, Geman D. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Trans Pattern Analysis and Machine Intelligence 1984; 6(6): 721-741
70. Geman S, Geman D, Graffigne C, Dong P. Boundary detection by constrained optimization. IEEE Trans Pattern Analysis and Machine Intelligence 1990; 12(7): 609-627
71. Lee CK. Automated Boundary Tracing Using Temporal Information. PhD Thesis, Department of Electrical Engineering, University of Washington, Seattle, 1994
72. Sheehan FH, Haralick RM, Suri IS, Shao Y. US Patent #: 5,734,739, Method for Determining the Contour of an In Vivo Organ Using Multiple Image Frames of the Organ. March 1998
73. Zavaljevski A, Dhawan AP, Gaskil M, Ball W, Johnson ID. Multi level adaptive segmentation of multi-parameter MR brain images. Comput Med Imag Graph 2000; 24(2): 87-98
74. Santago P, Gage HD. Quantification of MR brain images by mixture density and partial volume modelling. IEEE Trans Medical Imaging 1993; 12: 566-574
75. Shattuck DW, Leahy RM. BrainSuite: An automated cortical surface identification tool. Medical Image Computing and Computer Assisted Intervention (MICCAI) 2000; 50-61
76. Wells III WM, Grimson WEL, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Medical Imaging 1992; 15(4): 429-442
77. Gerig G, Kubler O, Jolesz FA. Nonlinear anisotropic filtering of MRI data. IEEE Trans Medical Imaging 1992; 11(2): 221-232
78. Joshi M, Cui J, Doolittle K, Joshi S, Van Essen D, Wang L, Miller MI. Brain segmentation and the generation of cortical surfaces. Neuroimage 1999: 9(5): 461-476
79. Dempster AD, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal Royal Statistical Society 1977; 39: 1-37
80. Kao Y-H, Surenson JA, Bahn MM, Winkler SS. Dual-echo MRI segmentation using vector decomposition and probability technique: A two tissue model. Magnetic Resonance in Medicine 1994; 32(3): 342-357
81. Kapur T. Model based three dimensional Medical Image Segmentation. PhD Thesis, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, May 1999
82. Li S. Markov Random Field Modeling in Computer Vision. Springer-Verlag, 1995
83. Held K, Rota Kopps E, Krause B, Wells W, Kikinis R, Muller-Gartner H. Markov random field segmentation of brain MR images. IEEE Trans Medical Imaging 1998; 16(6): 878-887
84. Bezdek JC, Hall LO. Review of MR image segmentation techniques using pattern recognition. Medical Physics 1993; 20(4): 1033-1048
85. Hall LO, Bensaid AM. A comparison of neural networks and fuzzy clustering techniques in segmenting MRI of the brain. IEEE Trans Neural Networks 1992; 3(5): 672-682
86. Pham DL, Prince JL, Dagher AP, Xu C. An automated technique for statistical characterization of brain tissues in magnetic resonance imaging. International Journal of Pattern Recognition and Artificial Intelligence 1997; 11(8): 1189-1211
87. Pham DL, Prince JL. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognition Letters 1999; 20(1): 57-68
88. Pham DL, Prince JL. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Medical Imaging 1999; 18(9): 737-752
89. Ostergaard LR, Larsen OV. Applying voting to segmentation of MR images. 2nd International Workshop on Statistical Techniques in Pattern Recognition, Sydney, Australia (Advances in Pattern Recognition, Lecture Notes in Computer Science 1451. August 11-13, 1998; 795-804
90. Duda RO, Mart PE. Pattern Classification and Scene Analysis. Wiley, 1973
91. Rosenfeld A, Kak A. Digital Picture Processing. Academic Press 1982
92. Jain AK, Murty MM, Flynn PJ. Data clustering: a review. ACM Computing Surveys Sept. 1999; 31(3): 264-323
93. Kandel A. Fuzzy Techniques in Pattern Recognition. Wiley. 1982
94. Acton PD, Pilowsky LS, Kung HF, Ell PJ. Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering. European Journal of Nuclear Medicine 1999; 26(6): 581-590
95. Serra J. Image Analysis and Mathematical Morphology. Academic Press. 1982
96. Sternberg SR. Grayscale morphology. Computer Vision, Graphics, Image Processing 1986; 35(3): 333-355
97. Vincent L, Soille P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans Pattern Analysis and Machine Intelligence 1991; 13(6): 583-598
98. Vincent LM, Masters BR. Morphological image processing and network analysis of cornea endothelial cell images. Proceedings of SPIE - The International Society for Optical Engineering, Image Algebra and Morphological Image Processing III 1992; 1769: 212-226
99. Lee J, Haralick RM, Shapiro LG. Morphologic edge detection. IEEE Journal of Robotics and Automation 1987; 3(2): 142-156
100. Bomans M, Höhne K-H, Tiede U, Martin R. 3-D segmentation of MR images of the head for 3-D display. IEEE Trans Medical Imaging 1990; 19(2): 177-37
101. Hildreth EC. The detection of intensity changes by computer and biological vision systems. Computer Vision, Graphics and Image Processing 1983; 22(1): 1-27
102. Höhne KH, Bomans M, Tiede U, Riemer M. Display of multiple 3-D objects using the generalized voxel model. Proceedings of SPIE - The International Society for Optical Engineering 1988; 914: 850-854
103. Beuchen S. Segmentation tools in mathematical morphology. Proceedings of SPIE - The International Society for Optical Engineering, Image Algebra and Morphological Image Processing 1990; 1350: 70-84
104. Suri JS, Kumar S, Chou Y-Y. Studying cytology of the uterine cervix on Win.98/NT platform using imaging techniques: a carcinoma-situ and cancerous cell detection and scoring. Proceedings of the First Meeting of Engineering in Medicine and Biology, Atlanta, GA 1999; 2: 1133
105. Robert L, Malandain G. Fast binary image processing using binary decision diagrams. Computer Vision and Image Understanding 1998; 72(1): 1-9
106. Malandain G, Bertrand G, Ayache N. Topological segmentation of discrete surfaces. International Journal of Computer Vision 1997; 10(2): 183-197
107. Bertrand G, Everat JC, Couprie M. Image segmentation through operators based upon topology, Journal of Electronic Imaging 1997; 6(4): 395-405
108. Lemieux L, Hagemann G, Krakow K, Woermann FG. Fast, accurate and reproducible automatic segmentation of the brain in T1-weighted volume magnetic resonance image data. Magnetic Resonance in Medicine 1999; 42(1): 127-135
109. Lemieux L, Hagemann G, Krakow K, Woermann FG. Fast, automatic segmentation of the brain in Tl-weighted volume magnetic resonance image data. Proceedings of SPIE - The International Society for Optical Engineering, Medical Imaging, Image Processing 1999; 3661: 152-160
110. Sancor S, Leahy R. A 3-D morphological algorithm for automated labelling of the cortex in magnetic resonance images. AAAI Spring Symposium Applications of Computer Vision in Medical Image Processing. Palo Alto, CA, March 1994
111. Stokking R. Integrated Visualization of Functional and Anatomical Brain Images PhD Thesis, Utrecht University Hospital, Utrecht, The Netherlands, 1998
112. Sckolowska E, Newell JA. Multilayered image representation structure and application in recognition of parts of brain anatomy. Pattern Recognition Letters 1986; 4(4): 223-230
113. Sckolawska E, Newell JA. Recognition of the anatomy using a symbolic structural model of a CT image of the brain. Proceedings of the Second International Conference on Image Processing and Applications (ICIPA). 1986: 233-237
114. Sckolowska E, Newell JA, Raya SP. Low-level segmentation of 3-D magnetic resonance images: A rule based system. IEEE Trans Medical Imaging 1990; 9(1): 327-337
115. Sonka M, Tadikonda SK, Collins SM. Knowledge based interpretation of MR brain images. IEEE Trans Medical Imaging 1996; 15(4): 443-452
116. Dhawan AP, Juvvadi S. Knowledge-based analysis and understanding of medical images. Computer Methods Programs Biomed 1990; 33(4): 221-239
117. Dhawan AP, Arata L. Knowledge-based multi-modality three-dimensional analysis of the brain. American Journal of Physiological Imaging 1992; 7(3/4): 210-219
118. Clark M. Knowledge-Guided Processing of Magnetic Resonance Images of the Brain. PhD Thesis, Department of Computer Science and Engineering, University of South Florida, Tampa, 2000
119. Suri JS, Tsai J-P. Expert system for low level segmentation of microscopic images of human embryos. Proceedings of the World Congress of Expert Systems, Orlando, FL 1991; 308-313
120. Suri JS, Tsai J-P. Knowledge-based analysis of computer tomography of human chest. Proceedings of the World Congress of Expert Systems, Orlando, FL 1991; 2671-2678
121. Haralick RM, Shanmugan K. Textural features for image classification. IEEE Trans Systems Man and Cybernetics 1973; 3(6): 141-152
122. Peleg SH, Naor J, Hartley R, Aunir D. Multiple resolution texture analysis and classification. IEEE Trans Pattern Analysis and Machine Intelligence 1984; 6(4): 518-523
123. Cross GR, Jain AK. Markov random field texture models. IEEE Trans Pattern Analysis and Machine Intelligence 1983: 5(1): 25-39
124. Lachmann F, Barillot C. Brain tissue classification from MRI data by means of texture analysis. Medical Imaging VI: Image Processing, Proceedings of SPIE - The International Society for Optical Engineering, Medical Imaging 1992: 1652: 72-83
125. Eklundh JO, Yamamoto H, Rosenfeld A. A relaxation method for multispectral pixel classification. IEEE Trans Pattern Analysis and Machine Intelligence 1980; 2(1): 72-75
126. Ehricke HH. Problems and approaches for tissue segmentation in 3-D MR imaging. Proceedings of SPIE - The International Society for Optical Engineering, Medical Imaging, Image Processing 1990; 1233: 128-137
127. Reddick WE, Glass JO, Cook EN, Elkin TD, Deaton RJ. Automated segmentation and classification of multispectral MRI of brain using artifical neural networks. IEEE Trans Medical Imaging 1997: 16(6): 911-918
128. Clark JW. Neural network modelling. Phys Med Biol 1991; 36(10): 1259-317
129. Wang Y, Adah T, Kung S, Szabo Z. Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach. IEEE Trans Image Processing 1998; 7(8): 1165-1181
130. Chiou GI, Hwang J-N. A neural network based stochastic active contour model (NNS-SNAKE) for contour finding of distinct features. IEEE Trans Image Processing 1985; 4(10): 1407-1416
131. Suri JS. Active contour Vs. learning: computer vision techniques in CT. MR and Xray cardiac imaging. Proceedings of the Fifth International Conference in Pattern Recognition and Information Processing, Minsk, Belarus, 1999: 273-277
132. Koster ASE. Linking Models for Multiscale Image Segmentation. PhD Thesis, Utrecht University, Utrecht. The Netherlands, 1995
133. Vincken KL. Probabilistic Multiscale Image Segmentation by the Hyperstack. PhD Thesis, Utrecht University, Utrecht, The Netherlands, 1995
134. Niessen WJ. Multiscalc Medical Image Analysis. PhD Thesis, Image Sciences Institute, Utrecht University, Utrecht, The Netherlands, 1997
135. Koster ASE, Vincken KL, De Graff CN, Zander OC, Viergever MA. Heuristis linking models in multiscale image segmentation. Computer Vision and Image Understanding 1997: 65(3): 382-402
136. Vincken KL, Koster ASH, Viergever MA. Probabilistic multiscale image segmentation. IEEE Trans Pattern Analysis and Machine Intelligence 1497: 2(19): 109-120
137. Niessen W, Romeny B, Viergever MA. Geodesic deformable models for medical image analysis. IEEE. Trans, Medical Imaging 1998; 17(4): 634-641
138. Niessen W, Vincken KL, Weickert J, Ter Haar RBM. Viergever MA. Multiscale segmentation of three-dimensional MR brain images. 1999: 31(2/3): 185-202
139. Suri JS, Haralick RM, Sheehan FH. Effect of edge detection, pixel classification, classification edge fusion over LV calibration, a two stage automatic system. Proceedings of the 10th Scandinavian Conference on Image Analysis (SCIA), Finland 1997; 1: 197-204
140. Haralick RM. Digital step edges from zero crossing of second directional derivatives. IEEE Trans Pattern Analysis and Machine Intelligence 1984; 6(1): 58-68
141. Haralick RM. Cubic facet model edge detector and ridge valley detector: implementation details. Gelsema ES, Kanal LN (eds), Pattern Recognition in Practice II In: Elsevier, 1986; 81-90
142. Teo PC, Shapiro G, Wandell BA. Creating connected representations of cortical gray matter for functional MRI visualisation. IEEE Trans Medical imaging 1997; 10(6): 852-863
143. Cline HE, Dumoulin CL, Hart HR, Lorensen WE, Ludke S. 3-D reconstruction of the brain from MRI using a connectivity algorithm. Magnetic Resonance in Medicine 1987; 5(5): 345-352
144. Canns J. A computational approach to edge detection. IEEE Trans Pattern Analysis and Machine Intelligence 1986; 8(6): 679-698
145. Monga O, Deriche E, Malandain G, Cocquerez JP. Recursive filtering and edge tracking: two primary tools for 3 D edge detection. Image and Vision Computing 1991; 4(9): 203-214
146. Deriche R. Recursively implementing the Gaussian and its derivatives. In: Srinivasan V, Ong SH, Ang YH (eds), Proceedings Second International Singapore Conference on Image Processing, Singapore, 1992: 263-267
147. Kennedy DN, Filipeck PA, Caviness VS. Anatomic segmentation and volumetric calculations in nuclear MR imaging. IEEE Trans Medical Imaging 1989; 8(1): 1-7
148. Diuric PM, Fwu I-K. Boundary detection in noisy vector fields. Proceedings of SPIE - The International Society for Optical Engineering. Medical Imaging, Image Processing, 1995: 2434: 730-741
149. Xuan J, Adali T, Wang Y. Segmentation of MR brain image: Integrating region growing and edge detection. Proceedings of the IEEE International Conference on Image Processing, Washington, DC 1995: 3: 544-547
150. Zuniga O, Haralick RM. Integrated directional derivative gradient operator. IEEE Trans Systems, Man and Cybernetics 1987: 17(3): 508-517
151. Haralick RM, Shapiro LG. Computer and Robot Vision. Addison Wesley, 1991
152. Kass M, Witkin A, Terzopolous D. Snakes: active contour models. International Journal of Computer Vision 1988; 1(4): 321-331
153. Singh A, von Kurowski L, Chiu MY. Cardiac MR image segmentation using deformable models. Proceedings of SPIE - The International Society for Optical Engineering, Medical Imaging. 1993: 1905: 8-28
154. Richens D, Rougon N, Bloch I, Mousseaux E. Segmentation by deformable contour of MRI sequences of the LV for quantitative analysis. 4th IEEE International Conference on Imago Processing and its Applications. The Netherlands 1992; 393-396
155. Atkins MS, Mackiewich BT. Fully automatic segmentation of the brain in MRI. IEEE Trans Medical Imaging Feb. 1998; 17(1): 98-107
156. Chalana V, Kim YM, Haynor D. A multiple active contour model for cardiac boundary detection in echocardiographic sequences. IEEE. Trans Medical Imaging 1996: 15(3): 290-298
157. Clarysse P, Poupon F, Barbier B, Magnin IE. 3-D boundary extraction of the left ventricular by a deformable model with a-priori information. Proceedings of the IEEE International Conference in Image Processing 1995; 2: 492-495
158. Miller IV, Breen DE, Lorenson WE, O'Bara RM. Geometrically deformable models: a method for extracting closed geometric models from volume data. Computer Graphics Proceedings of Computer Graphics 1991; 25(4): 217-226
159. Chen OW, Huang TS. Modeling, analysis and visualization of left ventricle shape and motion by hierarchical decomposition. IEEE Trans Pattern Analysis and Machine Intelligence 1944;16(4): 342-356
160. O'Donel T, Gupta A, Boult TE. A periodic generalised cylinder model with local deformations for tracking closed contours exhibiting repeating motion. Proceedings of the 12th IAPR International Conference on Pattern Recognition, Jerusalem, Israel 1994; 1: 397-402
161. Williams DJ, Shah M. A fast algorithm for active contour and curvature estimation. Computer Vision. Graphics and Image Understanding 1992: 55(1): 14-26
162. Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models: their training and application. Computer Vision and Image Understanding 1995; 61(1): 38-59
163. Gang X, Segawa E, Tsuji S. Robust active contours with insensitive parameters. Pattern Recognition 1994: 27(7): 879-884
164. Ranganath S. Analysis of the effects of snake parameters on contour extraction. Proceedings of the 2nd International Conference on Automation, Robotics. Computer Vision (CARCV), Singapore, 1992: 4.5.1-4.5.5
165. Amini A, Tehrani S. Weymouth T. Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints. Proceedings Second International Conf. Computer Vision (ICCV), Tarpon Springs, II 1988: 93-99
166. Szekely G, Lelemen A, Brechbuhler C, Greig G. Segmentation of 2 D and 3 D objects from MRI volume data using con strained elastic deformation of flexible Fourier surface models. Journal of Medical Image Analysis 1996; 1(1): 19-34
167. Bardinet E, Cohen LD, Ayache N. Analyzing the deformation of the LV of the heart with a parametric deformation model. Research Report 2797. INRIA, Sophia-Antipolis. France, 1996
168. Bardinet F, Cohen LD, Ayache N. Tracking and motion analysis of the left ventricle with deformable superquadratics. Medical Image Analysis (MIA) 1996; 1(2): 129-149
169. Ayache N. Medical computer vision, virtual reality and robotics. Image and Vision Computing 1993; 13(4): 295-313
170. Staib LH, Sinusas A, Cardiac. SPECT restoration using MR-based support constraints. Proceedings of the IEEE International Conference on Image Processing, IEEE Press, Los Alamitos, CA 1995; 480-483
171. Nastar C, Ayache N. Fast segmentation, tracking and analysis, of deformable objects. Proceedings of the Fourth International Conference on Computer Vision (ICCV), Berlin. Germany 1993; 275-279
172. Staib LH, Duncan JS. Parametrically deformable contour models. Proceedings of International Conference in Computer Vision (ICCV) 1989: 98-102
173. Ip HHS, Shen D. An affine invariant active contour model (Al-snake) for model-based segmentation, Image and Vision Computing 1998; 16(2): 135-146
174. Duta N, Sonka M. Segmentation and interpretation of MR brain images: an improved active shape model. IEEE Trans Medical Imaging 1998; 17(6): 1049-1062
175. Lipson P, Yuille AL, O'Keefe D, Cavanaugh J, Taffe J, Rosenthal D. Deformable templates for feature extraction from medical images. In: Faugeras O (ed). Computer Vision, Proceedings First European Conference in Computer Vision, France 1990; 413-417
176. Menet S, Saint Mare P, Medioni G. Active contour models: overview, implementation and applications. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics Conference Proceedings 1990; 929: 194-199
177. Samadani R. Adaptive snakes: control of damping and material parameters. Proceedings of SPIE - The International Society for Optical Engineering, Geometric Method in Computer Vision. Medical Imaging, San Diego, CA 1991; 1570: 202-213
178. Perona P, Malik J. Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Analysis and Machine Intelligence 1990: 12(7); 629-639
179. McInernev T, Terzopoulous D. A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4 D image analysis. Computerized Medical Imaging and Graphics 1995: 1(1): 69-83
180. McInerney T, Terzopoulous D. Deformable models in medical image analysis: a survey. Medical Image Analysis 1996: 1(2): 91-108
181. Sethian JA. An Analysis of Flame Propagation. PhD Thesis, Department of Mathematics. University of California, Berkeley, CA, 1982
182. Osher S, Sethian J. Fronts propagating with curvature dependent speed: algorithms based on Hamiltons Jacobi formulations. Journal Computational Physics 1988; 79(1): 12-49
183. Rouy E, Tourin A. A viscosity solutions approach to shape-from-shading. SIAM Journal of Numerical Analysis 1992; 23(3): 867-884
184. Caselles V, Catte F, Coll T, Dibos F. A geometric model for active contours. Numerische Mathematik 1993: 66: 1-31
185. Chepp DL. Computing minimal surfaces via level set curvature flow. Journal of Computational Physics 1993; 106(1): 77-91
186. Malladi R, Sethian JA. Vemuri BC. Shape modeling with front propagation. IEEE Trans Pattern Analysis and Machine Intelligenie 1995; 17(2): 158-175
187. Cao S, Greenhalgh S. Finite difference solution of the eikonal equation using an efficient. First arrival, wavefront tracking scheme. Geophysics 1994; 59(4): 632-643
188. Kichenassamy S, Kumar A, Olver P, Tannenbaum A, Yezzi A. Corformal curvatures, flows: from phase transitions to active vision Arch Rational Mech Analysis 1996; 134(3): 275-301
189. Vezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A. A geometric snake model for segmentation of medical imagery. IEEE Trans Medical Imaging 1997; 16(2): 199-209
190. Sideiqui K, Lauriere YB, Tannenbaum A, Zucken SW. Area and length minimizing flows for shape segmentation. IEEE Trans Image Processing 1998; 7(3): 433-443
191. Sethian JA. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Material Science. Cambridge University Press, 1999
192. Sethian JA. US Patent #: 6,018,499. Three dimensional scismic imaging of complex velocity structures. Issued January 25 2000
193. Sethian JA. Curvature flow and entropy conditions applied to grid generation, Journal of Computational Physics 1994; 115(2): 440-454
194. Schroeder WJ, Zarge JA, Lorensen WE. Decimation of triangle meshes. Computer Graphics 1987; 26(4): 65-169
195. Boissonnat JD. Shape reconstruction from planar cross sections. Computer Vision. Graphics and Image Processing 1988; 44(1): 1-29
196. Geiger B. Construction et utilisation des modhles d'organes en vue de l'assistance au diagnostic et aux interventions chirurgicales. PhD Thesis, Universiti Sophia Antipolis, France, 1993
197. Carman GJ, Drury HA, Van Essen DC. Computational method for reconstructing and unfolding the cerebral cortex. Cerebral Cortex 1995; 5(6): 506-517
198. Suri S. Brain cavity reconstruction from MRI serial cross sections. University of Illinois, Chicago, MS Thesis, 1990
199. Tatsumi H, Takaoki E, Omura K, Fujito H. A new method of 3-D reconstruction from serial section by computer graphics using meta-balls: Reconstruction of hepatoskeletal system formed by to cells in the cod liver. Comput Biomed Research 1990; 23(1): 37-45
200. MacDonald D, Avis D, Evans AE. Proximity constraints in deformable models for cortical surface identification. Proceedings of the First International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 1998; 650-659
201. MacDonald D, Avis D, Evans AE. Multiple surface identification and matching in Magnetic Resonance images. Proceedings of SPIE - The International Society for Optical Engineering, Bellingham. WA 1994; 2359: 160-169
202. MacDonald D, Kabani N, Avis D, Evans AC. Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage 2000; 12(3): 340-356
203. Davatzikos CA, Prince JL. An active contour model for mapping the cortex. IEEE Trans Medical Imaging 1995; 14(1): 65-80
204. Davatzikos CA, Bryan RN. Using a deformable surface model to obtain a shape representation of cortex. IEEE Trans Medical Imaging 1996; 15(6): 785-795
205. Vaillant M, Davatzikos C. Finding parametric representations of the cortical sulci using an active contour model. Medical Image Analysis 1997: 1(4): 295-315
206. Goualher GL, Barillot C, Bizais Y. Modeling cortical sulci using active ribbons. International Journal of Pattern Recognition and Artificial Intelligence 1997; 11(8): 1295-1315
207. McInereney T, Terzopoulos D. Topologically adaptable snakes. 5th International Conference in Computer Vision (ICCV) 1995; 840-845
208. McInerney T, Terzopoulos D. Medical image segmentation using topologically adaptable surfaces. Proceedings First Joint Conference of Computer Vision, Virtual Reality and Robotics in Medicine and Medical Robotics and Computer Assisted Surgery (CVRMed-MRCAS'97), Grenoble, France. Lecture Notes in Computer Science. Springer-Verlag. 1205 March 1997; 23-32
209. McInerney T, Terzopoulos D. Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Trans Medical Imaging 1999; 13(10): 840-850
210. Cohen I, Cohen L, Ayache N. Using deformable surfaces to segment 3-D images and inter differential structures. CVGIP: Image Understanding 1992; 56(2): 242-263
211. Terzopoulos D, Witkin A, Kass M. Constraints on deformable models: Recovering 3D shape and non rigid motion. Artificial Intelligence 1988; 36(1): 91-123
212. Dale AM, Fischl B, Sereno MI. Cortical surface based analysis. I. Segmentation and surface reconstruction. NeuroImage 1999: 9(2): 179-194
213. Fischl B, Sereno MI, Dale AM. Cortical surface based analysis-II., inflation, flattening and a surface based coordinate system. Neuro Image 1999; 9(2): 195-207
214. Malladi R, Sethian JA. An O(N log N) algorithm for shape modeling. Applied Mathematics, Proc Nat Acad Sci (PNAS) USA 1996; 93(18): 9389-9392
215. Malladi R, Sethian JA. A real-time algorithm for medical shape recovery. International Conference on Computer Vision 1998; 304-310
216. Suri JS, Singh S, Laxminaryan S, Liu K, Reden L. Shape recovery algorithms using level sets in 2 D/3 D medical imagery: A state-of-the-art review. IEEE Trans Information Technology in Biomedicine 2001
217. Lorenson WE, Clinc H. Marching cubes: A high resolution 3-D surface construction algorithm. ACM Computer Graphics, Proceedings of SIGGRAPH 1987: 21(4): 163-169
218. Cline HE, Lorensen WE, Ludke S. Two algorithms for the three dimensional reconstruction of tomograms. Med Phys 1988; 15(3): 320-327
219. Shu R, Zhou C, Kankanhalli MS. Adaptive marching cubes. The Visual Computer 1993; 11(4): 202-217
220. Gueziec A, Hummel R. Exploiting triangulated surface extraction using tetrahedral decomposition. IEEE Trans Visual and Computational Graphics 1995; 1(4): 328-342
221. Guiziec A. Surface simplification inside a tolerance volume. IBM Research Report RC 20440, 1996
222. Montani C, Scateni R, Scopigno R. Discretized marching cubes. In: Bergeron RD, Kaufman Al: (eds) Visualization Proceedings. IEEE Press 1994; 281-287
223. Sarah F, Gibson F. Constrained elastic surface nets: Generating smooth surfaces from binary segmented data. Proceedings Medical Image, Computing and Artificial Intelligence (MICCAI), MIT 1998; 888-898
224. Van-Gelder A. Wilhems J. Topological considerations in isosurface generation. ACM Trans Graphics 1994; 13(3): 337-375
225. Xu C, Pham DL, Prince JL. Medical image segmentation using deformable models. In: Sonka M. Fitzpatrick JM. SPIE Handbook of Medical Imaging, Vol. 2: Medical Image Processing and Analysis. June 2000
226. Suri JS. Modeling segmentation issues via partial differential equations, level sets, and geometric deformable models: a revisit. In preparation 2001
227. Rettmann ME, Xu C, Pham DL, Prince JL. Automated segmentation of sulcal regions. Proc Medical Image Computing and Computer Assisted Intervention (MICCAI) 1999; 158-167.
228. Rettmann ME, Han X, Prince JL. Watersheds on cortical surface for automated sulcal segmentation. Proc IEEE/SIAM Workshop on Mathematical Morphology and Biomedical Image Analysis (MMBIA) 2000; 20-27
229. Lohmann G, von Cramon DY. Sulcal basins and sulcal strings as new concepts for describing the human cortical topography. IEEE Workshop on Biomedical Image Analysis (B1A) 1998; 24-33
230. Lohmann G, von Cramon PY. Automatic labelling of the human cortical surface using sulcal basins. Medical Image Analysis 2000; 4(3): 179-188
231. Liang J, McInerney T, Terzopoulos D. Interactive medical image segmentation with united snakes. Second International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Cambridge, England, 1999
232. Liang J, McInerney T, Terzopoulos D. United snakes (towards a more perfect union of active contour techniques). IEEE Seventh International Conference on Computer Vision (ICCV99). Kerkyra, Greece, September 1999
233. Paragios N, Deriche R. Geodesic active regions: a new paradigm to deal with frame partition problems in computer vision, Journal of Visual Communication and Image Representation (Special Issue on Partial Differential Equations in Image Processing. Computer Vision and Computer Graphics) 2000
234. http://www.bic.mni.megill.ca/. McConnell Brain Imaging Center. Montreal Neurological Institute, McGill University, Canada
235. Ibsrema. http://neuro www.mgh.harvard.edu/cma/ibsr. The Internet Brain Segmentation Repository, Center for Morphometric Analysis. Massachusetts General Hospital, Boston. MA, USA
236. Jolesz FA. Image guided procedures and the operating room of the future. Radiology 1997; 204(1): 601-612
237. Jolesz FA, Kikinis R. The vision of image-guided computerized surgery: the high tech operating room. In: Taylor RH, Lavalle S, Burdea GC (eds). Computerized Integrated Surgery. MIT Press 1994; 717-721