Fuzzy Entropy Based MR Image Segmentation Using Particle Swarm Optimization
نویسنده
چکیده
An image segmentation technique based on fuzzy entropy is applied for MR brain images to detect a brain tumor is presented in this paper. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions, whose member functions of the fuzzy region are Z-function and S-function. The optimal parameters of these membership functions are determined using Particle Swarm Optimization algorithm by maximizing the fuzzy entropy. Through a number of examples, the performance is compared with those using existing entropy-based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive method and Otsu segmentation technique; the results show the proposed fuzzy entropy method integrated with PSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. Index Terms — Fuzzy entropy, image segmentation, threshold; particle swarm optimization —————————— ——————————
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