Abstract
One of emerging challenges in Medical image analysis is clustering. Fuzzy C-means (FCM) algorithm is one of the most popular clustering algorithms because it is efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima, such a drawback could be overcome by evolutionary algorithms. This paper is dedicated to implement a fuzzy strategy evolutionary approach based to optimize the centers of the clusters by minimizing the objective function of the FCM algorithm. This approach is based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm to find the optimum values of the centers of the clusters in order to classify Magnetic Resonance Imaging (MRI) brain images. The proposed approach has been validated against both simulated and clinical MRI and it has yield competitive results when compared to FCM algorithms. Results show that the proposed algorithm has obtained reasonable segmentation of white matter, gray matter, and cerebrospinal fluid from MRI data.
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