Lévy–Cauchy arithmetic optimization algorithm combined with rough K-means for image segmentation

Abstract

Rough K-Means (RKM) is a well-known unsupervised clustering algorithm based on rough set logic that is utilized in a wide range of applications. However, when dealing with complex problems like image segmentation, it is frequently trapped in local optima during execution, resulting in undesirable segmentation results. To handle the issue in a realistic computing time, this study develops a Lévy–Cauchy Arithmetic Optimization Algorithm (LCAOA), an enhanced form of AOA, for performing rough clustering. The Levy flight and Cauchy distribution help in exploration and exploitation, respectively, in the proposed LCAOA. Therefore, well-balanced exploration and exploitation have been incorporated into LCAOA, which is a major problem of classical AOA. Opposition-based learning is also incorporated into LCAOA to maintain an efficient population during the optimization process. As the segmentation efficacy is somewhat dependent on the selection of color spaces caused by the non-illumination of regions, the suggested method employs the CIELab color space. The suggested method is compared to conventional and Nature-Inspired Optimization Algorithms (NIOA)-based state-of-the-art image segmentation techniques over traditional color images, color pathology images, and leaf images. The proposed clustering methodology outperforms all other examined clustering algorithms, according to the results of the experiments. For example, proposed LCAOA-based rough clustering gives average Feature Similarity Index (FSIM) values of 0.9513, 0.9688, and 0.9769 for traditional color images with 4, 6, and 8 clusters, respectively. The proposed technique is associated with an average FSIM value of 0.9525 for cluster number 2 in images of oral pathology. Lastly, for leaf images, the proposed approach yields a mean FSIM value of 0.9759 with an accuracy of greater than 97% for cluster number 2.