AUTHORS: Wessam S. Elaraby, Ahmed H. Madian, Mahmoud A. Ashour, Ibrahim Farag, Mohammad Nassef

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ABSTRACT: Recently, fractional edge detection algorithms have gained focus of many researchers. Most of them concern on the fractional masks implementation without optimization of threshold levels of the algorithm for each image. One of the main problems of the edge detection techniques is the choice of optimal threshold for each image. In this paper, the genetic algorithm has been used to enhance the selection of the threshold levels of the edge detection techniques for each image automatically. A fully automatic way to cluster an image using K-means principle has been applied to different fractional edge detection algorithms to extract required number of thresholds. A performance comparison has been done between different fractional algorithms with and without genetic algorithm. Evaluation of the noise performance upon the addition of salt and pepper noise is measured through the peak signal to noise ratio (PSNR) and bit error rate (BER) simulated by using MATLAB

KEYWORDS: - Edge Detection, Fractional Systems, Soft Computing Techniques, Biomedical, Genetic Algorithm, clustering-Kmean

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