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Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm algorithm (ILSSA) method that combines iterative mapping and local escaping operator to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses two-dimensional (2D) Kapur\u2019s entropy as the objective function and employs non-local means 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds.<\/jats:p>","DOI":"10.1093\/jcde\/qwad006","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T02:56:12Z","timestamp":1673492172000},"page":"655-693","source":"Crossref","is-referenced-by-count":13,"title":["Salp swarm algorithm with iterative mapping and local escaping for multi-level threshold image segmentation: a skin cancer dermoscopic case study"],"prefix":"10.1093","volume":"10","author":[{"given":"Shuhui","family":"Hao","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou 325035 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changcheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou 325035 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou 325035 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7714-9693","authenticated-orcid":false,"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou 325035 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingzhi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University , Ningbo, Zhejiang 315020 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abeer D","family":"Algarni","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University , P.O. 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