MRI Fuzzy Segmentation of Brain Tissue Using IFCM Algorithm with Genetic Algorithm Optimization
Youness Aliyari Ghassabeh, Nosratallah Forghani, Mohamad Forouzanfar, Mohammad Teshnehlab · 2007
Fuzzy c-mean (FCM) is a common clustering algorithm which is used for segmentation of magnetic resonance (MR) images. However in the case of noisy MR images, efficiency of this algorithm considerably reduces. Recently, researchers have been introduced two new parameters in order to improve performance of traditional FCM in the case of noisy images. New parameters are computed using artificial neural networks and through an optimization problem, where need complex and time consuming computations. In this paper, we present a new method for efficient computation of these two parameters. We used genetic algorithm (GA) optimization method and showed capability of GA for finding optimal values of these parameters. Simplification of computation is advantage of new proposed method. Simulation results using noisy MR images, demonstrated effectiveness of proposed optimization method for noisy MR image segmentation. 1. Introduction