{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-skin-lesion-segmentation-on","title":"Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging","arxiv_id":"1808.06759","date":"2018-08-21","proceeding":null,"authors":["Diego Patiño","Jonathan Avendaño","John Willian Branch"],"abstract":"We present a superpixel-based strategy for segmenting skin lesion on\ndermoscopic images. The segmentation is carried out by over-segmenting the\noriginal image using the SLIC algorithm, and then merge the resulting\nsuperpixels into two regions: healthy skin and lesion. The mean RGB color of\neach superpixel was used as merging criterion. The presented method is capable\nof dealing with segmentation problems commonly found in dermoscopic images such\nas hair removal, oil bubbles, changes in illumination, and reflections images\nwithout any additional steps. The method was evaluated on the PH2 and ISIC 2017\ndataset with results comparable to the state-of-art.","url_abs":"http://arxiv.org/abs/1808.06759v1","url_pdf":"http://arxiv.org/pdf/1808.06759v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-skin-lesion-segmentation-on","repo_url":"https://github.com/dipaco/mole-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"automatic-skin-lesion-segmentation-on","repo_url":"https://github.com/dipaco/superpixel-skin-lesion-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"skin-lesion-segmentation","task_name":"Skin Lesion Segmentation"},{"task_slug":"superpixels","task_name":"Superpixels"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}