Review of MR image segmentation techniques using pattern recognition

Medical Physics

Volumn 20, Issue 4, 1993, Pages 1033-1048

  Bezdek, J C ^a   Hall, L O ^b   Clarke, L P ^c  

^a UNIVERSITY OF WEST FLORIDA   (United States)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

^c H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER MODEL; IMAGE ENHANCEMENT; IMAGE PROCESSING; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; REVIEW; STATISTICAL ANALYSIS; TECHNOLOGY;

ALGORITHMS; BAYES THEOREM; BIOPHYSICS; CLUSTER ANALYSIS; HUMAN; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION; SUPPORT, NON-U.S. GOV'T; SUPPORT, U.S. GOV'T, NON-P.H.S.; SUPPORT, U.S. GOV'T, P.H.S.;

EID: 0027275316     PISSN: 00942405     EISSN: NA     Source Type: Journal    
DOI: 10.1118/1.597000     Document Type: Article

References (90)

1. L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. L. Silbiger, and J. C. Bezdek, “A comparison of neural networks and fuzzy clustering techniques in segmenting magnetic resonance images of the brain,” IEEE Trans. Neural Networks 3, 672–683 (1992).
2. M. Just and M. Thelen, “Tissue characterization with T1, T2 and proton density values: Results in 160 patients with brain tumors,” Radiology 169, 779–785 (1988).
3. K. Gohogan, E. L. Spitznagel, W. A. Murphy, M. W. Vannier, W. T. Dixon, D. J. Gersell, S. L. Rossnick, W. G. Totty, J. M. Destouet, D. L. Rickman, T. A. Spraggins, and R. L. Butterfield, “Multispectral analysis of MR images of the breast,” Radiology 163, 703–707 (1987).
4. M. W. Vannier, C. M. Speidel, and D. L. Rickman, “Magnetic resonance imaging multispectral tissue classification,” News Physiol. Sci. 3, 148-154 (1988).
5. M. Jungke, W. von Seelen, G. Bielke, S. Meindl, G. Krone, M. Grigat, P. Higer, and P. Pfannenstiel, “Information processing in nuclear magnetic resonance imaging,” Mag. Res. Imag. 6, 683–693 (1988).
6. M. W. Vannier, T. K. Pilgram, C. M. Speidel, L. R. Neumann, D. L. Rickman, and L. D. Schertz, “Validation of magnetic resonance imaging (MRI) multispectral tissue classification,” Comput. Med. Imag. Graph. 15, 217–223 (1991).
7. M. W. Vannier, R. L. Butterfield, D. Jordan, W. Murphy, R. Levitt, and M. Gado, “Multispectral analysis of magnetic resonance images,” Radiology 154, 221–224 (1985).
8. D. Schellenberg, W. C. Naylor, and L. P. Clarke, “Application of artificial neural networks for tissue classification from multispectral magnetic resonance images of the head,” Chapel Hill, NC (IEEE Computer Society, Los Alamitos, CA, 1990).
9. J. P. Windham, D. J. Peck, and H. Soltanian-Zadeh, “A vector subspace method for tissue characterization using MRI sequences,” Med. Phys. 8, 619 (1991).
10. L. P. Clarke, “Comparison of Bayesian maximum likelihood (MLM) and artificial neural network for supervised tissue classification and 3-D segmentation by MRI,” Med. Phys. 18, 673 (1991).
11. A. M. Bensaid, L. O. Hall, L. P. Clarke, and R. P. Velthuizen, “MRI segmentation using supervised and unsupervised methods,” in Proceedings of the 13th Annual IEEE Engineering, Medicine, and Biology Conference, Orlando, FL (IEEE, New York, 1991).
12. P. Clarke, R. P. Velthuizen, S. Phuphanich, J. D. Schellenberg, J. A. Arrington, and M. L. Silbiger, “MRI: Stability of three supervised segmentation techniques,” Mag. Res. Imag. 11(1), 95–106 (1993).
13. M. I. Kohn, N. K. Tanna, G. T. Herman, S. M. Resnick, P. D. Mozley, R. E. Gur, A. Alavi, R. A. Zimmerman, and R. C. Gur, “Analysis of brain and cerebrospinal fluid volumes with MR imaging,” Radiology 178, 115–122 (1991).
14. R. K. Berger, F. W. Wehrli, and C. H. Charles, “Reproducibility of relaxation and spin density parameters in phantoms and human brain measured by MR imaging at 1.5 T,” Mag. Res. Imag. 3, 649–992 (1986).
15. H. Xiaoping, N. Alperin, D. N. Levin, K. K. Tan, and M. Mengeot, “Visualization of MR angiographic data with segmentation and volume rendering techniques,” J. Mag. Res. Imag. 1, 539–546 (1991).
16. G. Gerig, J. Martin, R. Kikinis, O. Kiibler, M. Shenton, and F. A. Jolesz, “Automating segmentation of dual-echo MR head data,” in Proceedings of the 12th International Conference on Information Processing in Medical Imaging, Wye, UK, July 1991 (Springer-Verlag, Berlin, 1991), pp. 175–187.
17. W. Menhardt and K. H. Schmidt, “Computer vision on magnetic resonance images,” Pattern Recog. Lett. 8, 73–85 (1988).
18. J. Rajanayagam, M. Fabry, and J. C. Gore, “In-vivo quantitation of water content in muscle tissue by NMR imaging,” Mag. Res. Imag. 9, 621–625 (1991).
19. K. K. Tan, D. N. Levin, C. A. Pelizzari, and G. T. Y. Chen, “Inter-active stereotactic localization of brain anatomy,” Radiology 177(P), 217–351 (1990).
20. D. N. Levin, X. Hu, K. K. Tan, S. Galhotra, and C. Pellizzari, “The brain: Integrated three-dimensional display of MR and PET images,” Radiology 172, 783–789 (1989).
21. N. M. Alpert, J. F. Bradshaw, D. Kennedy, and J. A. Correia, “The principal axes transformation—A method for image registration,” J. Nucl. Med. 31, 717–1722 (1990).
22. M. A. King, M. Coleman, B. C. Penney, and S. J. Glick, “Activity quantitation in SPECT: A study of pre-reconstruction Metz filtering and use of the scatter degradation factor,” Med. Phys. 18, 184–189 (1991).
23. W. Qian, M. Kallergi, and L. P. Clarke, “Order statistic neural network hybrid filters for gamma camera bremsstrahlung image restoration,” IEEE Trans. Med. Imag. (in press, 1993).
24. J. W. Clarke, “Neural network modelling,” Phys. Med. Biol. 36, 1259—1317 (1991).
25. D. T. Glennon, L. P. Clarke, R. P. Velthuizen, and M. L. Silbiger, “MR image non-uniformity correction techniques,” in Proceedings of the 13th Annual IEEE Engineering, Medicine, and Biology Conference, Oct. 31-Nov. 3, Orlando, FL (IEEE, New York, 1991).
26. P. A. Bottomley, T. H. Foster, R. E. Angersinger, and L. M. Pfeifer, “A review of normal hydrogen NMR relaxation times and relaxation mechanisms from 1–100 MHz. Dependence of tissue type, NMR frequency, temperature, species excision and age,” Med. Phys. 11, 425–448 (1984).
27. E. L. Chaney and S. M. Pizer, “Defining anatomical structures from medical images,” J. Three Dim. Treatment Planning Sem. Rad. Oncol. 2, 215–225 (1992).
28. A. Smith and J. Purdy, “Three dimensional photon treatment planning,” Int. J. Rad. Oncol. Biol. Phys. 21, 1–253 (1991).
29. Z. Liang, “Tissue classification and segmentation of MR images,” IEEE IMB Mag. 12, 81–85 (1993).
30. L. P. Clarke, “MRI: Automatic segmentation techniques,” J. Mag. Res. Imag. 3, 31 (1993).
31. L. P. Clarke, G. J. Blanie, K. Doi, M. J. Yaffe, F. Shtem, and G. S. Brown, “Digital mammography, cancer screening: Factors important for image compression,” Proceedings of the Space and Earth Science Data Compression Workshop, April 2, 1993, Snowbird, UT, NASA Conference Publication (NASA, Washington, DC, 1993) 63–74.
32. L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–352 (1965). pp.
33. W. Stallings, “Fuzzy set theory versus Bayesian statistics,” IEEE Trans. SMC 7, 216–219 (1977).
34. B. Y. Lindley, “Scoring rules and the inevitability of probability,” (with seven commentaries/replies) Int. Stat. Rev. 50, 1–26 (1982).
35. P. Cheeseman, “An Inquiry into Computer Understanding,” Comp. Intell. (with 22 commentaries/replies) 4, 57–142 (1988).
36. B. Kosko, “Fuzziness and probability,” Int. J. Gen. Systems 17, 211—240 (1990).
37. D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications (Academic, New York, 1980).
38. G. Klir, and T. Folger, Fuzzy Sets, Uncertainty and Information (Prentice-Hall, Englewood Cliffs, NJ, 1988).
39. H. Zimmermann, Fuzzy Set Theory—and its Applications, 2nd ed. (Kluwer, Boston, 1990).
40. R. Duda and P. Hart, Pattern Classification and Scene Analysis (Wiley Interscience, New York, 1973).
41. J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles (Addison-Wesley, Reading, MA, 1974).
42. P. Devijver, and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall, Englewood Cliffs, NJ, 1982).
43. O. Kubler and G. Gerig, “Segmentation and analysis of multidimensional data-sets in medicine,” in Proceedings of the NATO Conference on 3-D Imaging in Medicine: Algorithms, Systems and Applications, edited by K. H. Hohne, H. Fuchs, and S. M. Pizer (Springer-Verlag, New York, 1990), pp. 63–81.
44. J. M. Coggins, “A multiscale description of image structure for segmentation of biomedical images, in Proceedings of the First International Conference on Visualization in Biomedical Computing, edited by N. F. Ezquerra, F. Garcia, and R. Arkin, (IEEE, Piscataway, NJ, 1990), pp. 123–130.
45. S. K. Pal and R. A. King, “Image enhancement using smoothing with fuzzy set,” IEEE Trans. Syst. Man Cybern. 11, 494–501 (1981).
46. S. K. Pal and B. Chakraborty, “Fuzzy set theoretic measures for automatic feature evaluation,” IEEE Trans. Syst. Man Cybern. 16, 754–760 (1986).
47. E. Ruspini, “A new approach to clustering,” Inf. Control 15, 22–32 (1969).
48. J. C. Bezdek and S. K. Pal, Fuzzy Models for Pattern Recognition (IEEE, Piscataway, NJ, 1992).
49. R. Krisnapuram and J. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Systems 1(2), 98–110 (1993).
50. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum, New York, 1981).
51. A. Kandel, Fuzzy Techniques in Pattern Recognition (Wiley Interscience, New York, 1982).
52. S. K. Pal and D. K. Dutta Majumder, Fuzzy Mathematical Approach to Pattern Recognition (Wiley, New York, 1986).
53. J. Hartigan, Clustering Algorithms (Wiley, New York, 1975).
54. D. Titterington, A. Smith, and U. Makov, Statistical Analysis of Finite Mixture Distributions (Wiley, New York, 1985).
55. Y. H. Pao, Adaptive Pattern Recognition and Neural Networks (Addison-Wesley, Reading, MA, 1989).
56. T. Kohonen, Self-Organization and Associative Memory, 3rd ed. (Springer-Verlag, Berlin, 1989).
57. M. Arbib, Brains, Machines and Mathematics, 2nd ed. (Springer-Verlag, Berlin, 1987).
58. DARPA Neural Network Study (AFCEA, Fairfax, VA, 1988).
59. R. C. Gonzalez and P. Wintz, Digital Image Processing, 2nd ed. (Addison-Wesley, Reading, MA, 1987).
60. D. Kerr and J. Bezdek, “Edge detection using a fuzzy neural network,” Proc. SPIE 1710, 510–521 (1992).
61. E. Tsao, W. C. Lin, C. T. Chen, J. Bezdek, and N. Pal “A neural network system for medical image understanding,” in Proceedings of the 5th Annual FLAIRS Conference, edited by M. Fishman (Flairs, Ft. Lauderdale, 1992), pp. 24–28
62. J. Bezdek, “Integration and generalization of LVQ and c-means clustering,” Proc. SPIE 1826, 280–299 (1992).
63. E. Tsao, J. Bezdek, and N. Pal, “Fuzzy Kohonen clustering networks,” in Proceedings of the First IEEE Conference on Fuzzy Systems (IEEE, Piscataway, NJ, 1992), pp. 1035–1043.
64. P. Clarke, R. P. Velthuizen, S. Phuphanich, J. D. Schellenberg, J. A. Arrington, and M. Silbiger, “MRI: Stability of three supervised segmentation techniques,” Mag. Res. Imag. 11(1), 95–106 (1993).
65. T. Cover, and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory 13, 21–27 (1967).
66. J. Bezdek, S. Chuah, and D. Leep, “Generalized k-nearest neighbor rules,” Fuzzy Sets Syst. 18, 237–256 (1986).
67. J. Keller, M. Gray, and J. Givens, “A fuzzy k-nearest neighbor algorithm,” IEEE Trans. Syst. Man Cybem. 15, 580–585 (1985).
68. S. E. Fahlman, “Faster learning variations on back-propagation: An empirical study,” in Proceedings of the 1988 Connectionist Models Summer School (Morgan Kaufmann, San Mateo, CA, 1988).
69. S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,” in Advances in Neural Information Processing Systems II, pp. edited by D. Touretzky (Morgan-Kaufman, San Mateo, CA, 1990), 524–532.
70. H. Li and H. S. Yang, “Fast and reliable image enhancement using fuzzy relaxation technique,” IEEE Trans. Syst. Man Cybem. 19, 1276—1281 (1989.)
71. Y. W. Lim and S. U. Lee, “On the color image segmentation algorithm based on the thresholding and fuzzy c-means techniques,” Pattern Recognition 23, 935–952 (1990).
72. C. Li, D. Goldof, and L. O. Hall, “Towards automatic classification and tissue labeling of MR brain images,” in Proceedings of the IAPR Workshop on Structural and Syntactic Pattern Recognition (World Scientific, Singapore, 1992).
73. K. Tanaka and M. Sugeno, “A study on subjective evaluations of printed color images,” Int. J. Approx. Reasoning, 5, 213–222 (1991).
74. R. L. Cannon, J. Dave, and J. Bezdek, “Efficient implementation of the fuzzy c-means clustering algorithms,” IEEE Trans. PAMI 8, 248–255 (1986).
75. A. M. Bensaid, J. C. Bezdek, L. O. Hall, R. P. Velthuizen, and L. P. Clarke, “A partially supervised fuzzy c-means algorithm for segmentation of MR images,” in Proceedings of the SPIE Conference on the Science of Neural Networks, Vol. 1710 (SPIE, Bellingham, WA, 1992), pp. 522–528.
76. W. C. Lin, E. C. K. Tsao, and C. T. Chen, “Constraint satisfaction neural networks for image segmentation,” Pattern Recognition 25, 679–694 (1992).
77. N. Pal, J. Bezdek, and E. Tsao, “Generalized clustering networks and Kohonen’s self-organizing scheme,” IEEE Trans. Neural Networks (in 1993).
78. G. Carpenter and S. Grossberg, “A massively parallel architecture for a self-organizing neural pattern recognition machine,” Comput. Vision Graph. Image Process. 37, 54–115 (1987).
79. S. Hidetomo and J. Toriwaki, “Automatic segmentation of head MRI images by knowledge guided thresholding,” Comput. Med. Imag. Graph. 15, 233–240 (1991).
80. M. B. Merickel, C. S. Carman, J. R. Brookeman, and C. R. Ayers, “Image analysis and quantification of atherosclerosis using MRI,” Comput. Med. Imag. Graph. 15, 207–216 (1991).
81. S. G. Romaniuk and L. O. Hall, “Dynamic neural networks with the use of divide and conquer,” Proc. IJCNN I, 658–663 (1992).
82. M. B. Merickel, J. W. Snell, T. R. Jackson, D. M. Skyba, and W. K. Katz, “Multispectral tissue identification in MR images,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society 13, 1323–1325 (1991).
83. H. A. Koenig and G. Laub, “Tissue discrimination in magnetic resonance 3-D data sets,” Proceedings of the SPIE Meeting in Medical Imaging, Newport Beach, CA (Publisher, City, 1988)
84. M. O’Donnell, J. C. Gore, and W. J. Adams, “Toward an automated analysis system for nuclear magnetic resonance imaging. II. Initial segmentation algorithm,” Med. Phys. 13, 293–297 (1986).
85. H. S. Choi, D. R. Haynor, and Y. Kin, “Partial volume tissue classification of multichannel magnetic resonance images—A mixed model,” IEEE Trans. Med. Imag. 10, 395-407 (1991).
86. S. V. Raman, S. Sarkar, and K. L. Boyer, “Tissue boundary refinement in magnetic resonance images using contour-based scale space matching,” IEEE Trans. Med. Imag. 10, 109–121 (1991).
87. S. V. Raman, S. Sakar, and K. L. Boyer, “A graph-theoretic approach to generating structure hypotheses in cerebral magnetic resonance images,” IEEE Trans. Med. Imag. (submitted Feb. 1991).
88. T. J. Hyman, R. J. Kurland, G. C. Levy, and J. D. Shoop, “Charac-terization of normal brain tissue using seven calculated MRI parameters and a statistical analysis system,” Mag. Res. Med. 11, 22–34 (1989).
89. N. Karssemeijer, “A statistical method for automatic labeling of tissues in medical images,” Machine Vision Appl. 3, 75–86 (1990).
90. M. Jungke, W. von Seeleen, G. Bielke, S. Meindl, G. Krone, M. Grigat, and P. Higer, “Information processing in nuclear magnetic resonance imaging,” Mag. Res. Imag. 6, 683–693 (1988).