Open AccessAutomatic dirt trail analysis in dermoscopy images
Author(s) -
Cheng Beibei,
Joe Stanley R.,
Stoecker William V.,
Osterwise Christopher T. P.,
Stricklin Sherea M.,
Hinton Kristen A.,
Moss Randy H.,
Oliviero Margaret,
Rabinovitz Harold S.
Publication year - 2013
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2011.00602.x
Subject(s) - dirt , artificial intelligence , computer science , computer vision , pattern recognition (psychology) , cartography , geography
Background Basal cell carcinoma ( BCC ) is the most common cancer in the US . Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCC s. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. Methods In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. Results For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network‐based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave‐one‐out approach. Conclusion Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation.