An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer. (December 2022)

• Record Type: Journal Article
• Authors: Wei zhu, ; Liu, Lei; Kuang, Fangjun; Li, Lingzhi; Xu, Suling; Liang, Yingqi
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: Image segmentation -- Skin cancer -- Whale optimization algorithm -- Levy operator -- Chaotic random mutation strategy -- Kapur's entropy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106227
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results. Highlights: An improved Whale Optimization Algorithm (LCWOA) is proposed. Two strategies, Levy operator and chaotic random variation strategy, are proposed to be combined with WOA. The superior performance of LCWOA is verified by comparison with existing WOA variants. An accurate LCWOA-based Kapur's entropy multi-threshold skin cancer image segmentation method is proposed.