A Differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding
Image entropy thresholding is one of the most widely used technique for multilevel thresholding. The endeavor of this paper is to focus on obtaining the optimal threshold points. Several meta-heuristics are being applied in literatures over the decade, for improving the accuracy and computational efficiency of Minimum Cross Entropy Thresholding (MCET) method. In this paper, we have incorporated a Differential Evolution (DE) based approach towards image segmentation. Results are also compared with modern state-of-art algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Further Mean Structural Similarity Index Measurement (SSIM) and Universal Image Quality Index (UIQI) are also being used for performance evaluation.
KeywordsMultilevel Image Segmentation MCET DE MSSIM UIQI
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