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83. 84. 85. 86. 87. 88. 89. 90. 91. Fuzzy Image Processing and Recognition for Ultrasound Image Segmentation. IEEE Trans. Biomed. Eng. 1999, 46(10), pp 1171–1175. ; Cracknell, A. P. Iterative Satellite Image Segmentation by Fuzzy Hit-or-Miss and Homogeneity Index. IEE Proc. Vision, Image and Signal Processing; 2006, 153(2), pp 206–214. Pednekar, A. ; Kakadiaris, I. A. Image Segmentation Based on Fuzzy Connectedness Using Dynamic Weights. IEEE Trans. Image Proc. 2006, 15(6), pp 1555–1562. ; Kakadiaris, I.

Bezdek, J. ; Clarke, L. P. Partially Supervised Clustering for Image Segmentation. Pattern Recog. 1993, 29, pp 1033–1048. 67. Cannon, R. ; Dave, J. ; Bezdek, J. C. Efficient Implementation of the Fuzzy c-Means Clustering Algorithms. IEEE Trans. Patt. Anal. Mach. Learn. 1986, 8, pp 248–255. 68. Smith, N. ; Kitney, R. I. Fast Fuzzy Segmentation of Magnetic Resonance Images: A Prerequisite for Real-Time Rendering Proc. SPIE; 1997, 3034(2), pp 1124–1135. 69. ; Mladenovic, N. Fuzzy J-Means:A New Heuristic for Fuzzy Clustering.

IEEE Trans. Patt. Anal. Mach. Intell 1981, 3, pp 208–210. ; Siy, P. The Ridge-seeking Method for Obtaining the Skelecton of Digital Images. IEEE Trans. Syst. Man Cybern. 1984, 14, pp 524–528. Pal, S. ; Wang, L. Fuzzy Medical Axis Transformation (FMAT): Practical Feasibility. Fuzzy Sets Syst. 1992, 50, pp 15–34. Dyer, C. ; Rosenfeld, A. Thinning Algorithms for Gray-Scale Pictures. IEEE Trans. Patt. Anal. Mach. Intell. 1979, 1, pp 88–89. Pal, S. ; Leigh, A. B. Motion Frame Analysis and Scene Abstraction: Discrimination Ability of Fuzziness Measures.

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24.Fuzzy Systems by John G. Webster (Editor)


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