A Fuzzy Kohonen’s Competitive Learning Algorithm for 3D MRI Image Segmentation

Kohonen’s self-organizing feature map (SOFM) is a two-layer feed-forward competitive learning network, and has been used as a competitive learning clustering algorithm in brain MRI image segmentation. However, most brain MRI images always present overlapping gray-scale intensities for different tissues. In this paper, fuzzy methods are integrated with Kohonen’s competitive algorithm to overcome this problem (we will name the algorithm F_KCL). The F_KCL algorithm fuses the competitive learning with fuzzy c-means (FCM) cluster characteristic and can improve the segment result effectively. Moreover, in order to enhancing the robustness to noise and outliers, a kernel induced method is exploited in our study to measure the distance between the input vector and the weights (KF_KCL). The efficacy of our approach is validated by extensive experiments using both simulated and real MRI images.

[1]  B M Dawant,et al.  Brain segmentation and white matter lesion detection in MR images. , 1994, Critical reviews in biomedical engineering.

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

[3]  Miin-Shen Yang,et al.  Generalized Kohonen's competitive learning algorithms for ophthalmological MR image segmentation. , 2003, Magnetic resonance imaging.

[4]  D. Kennedy,et al.  Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. , 1989, IEEE transactions on medical imaging.

[5]  Benoit M. Dawant,et al.  Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study , 1993, IEEE Trans. Medical Imaging.

[6]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[7]  Dao-Qiang Zhang,et al.  A novel kernelized fuzzy C-means algorithm with application in medical image segmentation , 2004, Artif. Intell. Medicine.

[8]  Guido Gerig,et al.  Unsupervised tissue type segmentation of 3D dual-echo MR head data , 1992, Image Vis. Comput..

[9]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

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

[11]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[12]  Chung-Chih Lin,et al.  Model Free Functional MRI Analysis Using Kohonen Clustering Neural Network , 1999, IEEE Trans. Medical 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]  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).

[16]  Alan C. Evans,et al.  MRI Simulation Based Evaluation and Classifications Methods , 1999, IEEE Trans. Medical Imaging.

[17]  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.

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

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

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

[21]  R. Edelman,et al.  Magnetic resonance imaging (2) , 1993, The New England journal of medicine.

[22]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[23]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.