Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation

Neuro-fuzzy approach have attracted considerable attention in the computational intelligence and segmentation algorithms have been increasingly in developed in improving the accuracy of medical diagnosis. Fuzzy set attempts to represent the human perception whereas neural network attempt to emulate the architecture and information representation scheme of human brain. In this paper a comparative study on the performance of the FCM and the variant fuzzy competitive learning algorithms including the generalized Kohonen’s competitive learning (GKCL)-based algorithms (KCL, fuzzy KCL (FKCL), fuzzy soft KCL (FSKCL)) and the learning vector quantization (LVQ)-based algorithms (LVQ, fuzzy LVQ (FLVQ), fuzzy soft LVQ (FSLVQ)) for MR image segmentation is presented. The performance of the algorithms are evaluated using the standard image quality indices such as MSE (mean squared error) and IQI (image quality index) and the results indicate that the soft versions of fuzzy competitive learning algorithms produces more promising results and require less CPU time than the other learning algorithms. Further, the LVQ-based algorithms have better performance according to the values of MSE and IQI as compared to the KCL based algorithms and the FCM algorithm.