Cluster Medical Image Segmentation using Morphological Adaptive Bilateral Filter based BSA Algorithm

The main aim for this research work is to develop an Adaptive BSA (Backtracking Optimized Search Algorithm) method to solve the optimization problem in image segmentation. In adaptive optimization algorithms, the probability of intersection and mutation depends on the value of the appropriate solution to improve convergence performance. Because of its memory function and simple structure, BSA has powerful features to find a globally optimized solution. However, the algorithm is not yet sufficient to strike a balance between exploration and exploitation of a medical image. Therefore, an improved adaptive tracking and search algorithm has been proposed together with morphological operations, where adaptive bilateral filter will improve the sharpness of edges of a unique region for obtaining global digital optimization in order to reach the cluster image segmentation. The proposed work shows better color quality-based image segmentation for the detection of tumors in medical images. The proposed optimization algorithm results show better performance, when compared to the basic BSA optimization method.