Radiographic images fractional edge detection based on genetic algorithm

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 get the optimal threshold levels for each image to enhance the edge detection of the fractional masks. 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 to be optimized by the genetic algorithm. 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. © 2018, Intelligent Network and Systems Society.