New Fuzzy Clustering Algorithm Applied to RMN Image Segmentation

of cerebral tissues from magnetic resonance nuclear imaging (MRN) 3D of the head are described in this paper. This procedure doesn't make any assumption nor on the number of classes nor on the shape of the density. Indeed, this last is estimated by a non parametric method, it is about the method of the Parzen's Kernel. A new objective function is proposed to improve the FCM algorithm by the addition of one term of entropy aiming to maximize the number of "good" ordering. A supplementary correction is operated by a probabilistic procedure said of fuzzy relaxation including the probabilities of the neighboring points. The validation of the algorithm is made on simulated data and on real cerebral imaging RMN.

[1]  Hong Yan,et al.  An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation , 2003, IEEE Transactions on Medical Imaging.

[2]  Geoffrey C. Fox,et al.  Constrained Clustering as an Optimization Method , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Xavier Descombes,et al.  An unsupervised clustering method using the entropy minimization , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

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

[5]  Francesco Masulli,et al.  A neural bootstrap for the possibilistic C-mean algorithm , 1997 .

[6]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[7]  Hichem Frigui,et al.  A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Lawrence O. Hall,et al.  Mri Segmentation Using Supervised And Unsupervised Methods , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[9]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[11]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  James C. Bezdek,et al.  A mixed c-means clustering model , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[13]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[14]  Xiaomin Liu,et al.  A Least Biased Fuzzy Clustering Method , 1994, IEEE Trans. Pattern Anal. Mach. Intell..