2026-04-29T02:37:46Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/234422024-11-29T07:05:16Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Galmes, Bernat author Moya-Alcover, Gabriel author Bibiloni, Pedro author Varona, Javier author Jaume-i-Capo, Antoni author 2022-05 A robust segmentation method that can be used to perform measurements on toenails is presented. The proposed method is used as the first step in a clinical trial to objectively quantify the incidence of a particular pathology. For such an assessment, it is necessary to distinguish a nail, which locally appears to be similar to the skin. Many algorithms have been used, each of which leverages different aspects of toenail appearance. We used the Hough transform to locate the tip of the toe and estimate the nail location and size. Subsequently, we classified the super-pixels of the image based on their geometric and photometric information. Thereafter, the watershed transform delineated the border of the nail. The method was validated using a 348-image medical dataset, achieving an accuracy of 0.993 and an F-measure of 0.925. The proposed method is considerably robust across samples, with respect to factors such as nail shape, skin pigmentation, illumination conditions, and appearance of large regions affected by a medical condition. Galmes B, Moya-Alcover G, Bibiloni P, Varona J, Jaume-i-Capo A. Geometric-based nail segmentation for clinical measurements. Multimed Tools Appl. 2022 May;81(12):16117-32. 1380-7501 https://hdl.handle.net/20.500.13003/19605 https://hdl.handle.net/20.500.12105/23442 10.1007/s11042-022-12234-2 1573-7721 Multimedia Tools and Applications 2-s2.0-85125527428 763256600007 Toenail Segmentation Medical image Computer vision Machine learning Geometric-based nail segmentation for clinical measurements