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.
KeywordsSegmentation Result Radial Basis Function Kernel Fuzzy Method Neuron Excitation Segment Result
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