A Modified Fuzzy Kohonen's Competitive Learning Algorithms Incorporating Local Information for MR Image Segmentation

A modified FKCL (MFKCL) algorithm for automatic segmentation of MR brain images is proposed in this paper. This algorithm is an extension of traditional fuzzy Kohonen's competitive learning algorithm. In our method, a factor that can estimate the effect of the neighbor pixels to the central pixel is introduced into the objective function of the standard FKCL algorithm as the local information. The local information is applied to trail off the effect of noise to the result of MRI segmentation. Experiments with simulated MR data and real MR data show that our algorithm can resist not only the little, but also the heavy noise compared with standard FKCL segmentation and other reported methods.

[1]  Jian Yu,et al.  Optimality test for generalized FCM and its application to parameter selection , 2005, IEEE Transactions on Fuzzy Systems.

[2]  Andrea Schenone,et al.  A fuzzy clustering based segmentation system as support to diagnosis in medical imaging , 1999, Artif. Intell. Medicine.

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

[4]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[5]  E T Bullmore,et al.  A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images. , 1999, Magnetic resonance imaging.

[6]  Kai-Hsiang Chuang,et al.  Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means , 1999, IEEE Transactions on Medical Imaging.

[7]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[9]  Mohammed Yakoob Siyal,et al.  An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI , 2005, Pattern Recognit. Lett..

[10]  Jun Kong,et al.  A Fuzzy Kohonen’s Competitive Learning Algorithm for 3D MRI Image Segmentation , 2006 .

[11]  Edwin N. Cook,et al.  Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks , 1997, IEEE Transactions on Medical Imaging.

[12]  W. Reddick,et al.  A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors. , 1998, Magnetic resonance imaging.

[13]  W E Phillips,et al.  Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. , 1995, Magnetic Resonance Imaging.

[14]  W E Reddick,et al.  Hybrid artificial neural network segmentation of precise and accurate inversion recovery (PAIR) images from normal human brain. , 2000, Magnetic resonance imaging.

[15]  Jian Yu,et al.  Analysis of the weighting exponent in the FCM , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Miin-Shen Yang,et al.  Alternative c-means clustering algorithms , 2002, Pattern Recognit..

[17]  Tianzi Jiang,et al.  Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images , 2003, NeuroImage.