Kapur’s Entropy and Cuckoo Search Algorithm Assisted Segmentation and Analysis of RGB Images

Background/Objectives: In this paper, Cuckoo Search (CS) algorithm based image multi-thresholding is proposed for optimal segmentation of RGB image by maximizing the entropy value in Kapur's method. Methods/Statistical Analysis: The aim of the paper is to search for an optimized threshold value for image segmentation using CS algorithm where fitness function is designed based on entropy of the image. The capability of CS assisted segmentation with Kapur's function is established in comparison with Firefly and PSO optimization algorithms using the universal image superiority measures existing in the literature. Findings: Results of this study show that CS with Kapur's function offers better performance measure, whereas Firefly and PSO optimization algorithms offers earlier convergence with comparatively lower CPU time. Applications/Improvements: In future, proposed method can be implemented for the medical image analysis.