{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:25:54Z","timestamp":1768688754868,"version":"3.49.0"},"reference-count":29,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2015,9,25]],"date-time":"2015-09-25T00:00:00Z","timestamp":1443139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2016,5]]},"abstract":"<jats:sec><jats:title>Background\/Purpose<\/jats:title><jats:p>Computer\u2010aided diagnosis of skin cancer requires accurate lesion segmentation, which must overcome noise such as hair, skin color variations, and ambient light variability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A biologically inspired geodesic active contour (<jats:styled-content style=\"fixed-case\">GAC<\/jats:styled-content>) technique is used for lesion segmentation. The algorithm presented here employs automatic contour initialization close to the actual lesion boundary, overcoming the \u2018sticking\u2019 at minimum local energy spots caused by noise artifacts such as hair. The border is significantly smoothed to mimic natural lesions. In addition, features that mimic biological parameters include spectral image subtraction and removal of peninsulas and inlets. Multiple boundary choices borders are created by parameter options used at different steps. These choices can allow future improvement over the basic default border.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The basic <jats:styled-content style=\"fixed-case\">GAC<\/jats:styled-content> algorithm was tested on 100 images (30 melanomas and 70 benign lesions), yielding a median <jats:styled-content style=\"fixed-case\">XOR<\/jats:styled-content> border error of 6.7%, comparable to the median inter\u2010dermatologist <jats:styled-content style=\"fixed-case\">XOR<\/jats:styled-content> border error (7.4%), and lower than the gradient vector flow snake median <jats:styled-content style=\"fixed-case\">XOR<\/jats:styled-content> error of 14.2% on the same image set. On a difficult low\u2010contrast border set of 1238 images, which included 350 non\u2010melanocytic lesions, a median <jats:styled-content style=\"fixed-case\">XOR<\/jats:styled-content> error of 23.9% is obtained.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p><jats:styled-content style=\"fixed-case\">GAC<\/jats:styled-content> techniques show promise in attaining the goal of automatic skin lesion segmentation.<\/jats:p><\/jats:sec>","DOI":"10.1111\/srt.12252","type":"journal-article","created":{"date-parts":[[2015,9,25]],"date-time":"2015-09-25T05:16:51Z","timestamp":1443158211000},"page":"208-222","source":"Crossref","is-referenced-by-count":34,"title":["Biologically inspired skin lesion segmentation using a geodesic active contour technique"],"prefix":"10.1111","volume":"22","author":[{"given":"R.","family":"Kasmi","sequence":"first","affiliation":[{"name":"Faculty of Technology Department of Electrical Engineering LTII Laboratory University of Bejaia Bejaia Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Mokrani","sequence":"additional","affiliation":[{"name":"Faculty of Technology Department of Electrical Engineering LTII Laboratory University of Bejaia Bejaia Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. 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