Open AccessBiologically inspired skin lesion segmentation using a geodesic active contour technique
Author(s) -
Kasmi R.,
Mokrani K.,
Rader R. K.,
Cole J. G.,
Stoecker W. V.
Publication year - 2016
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/srt.12252
Subject(s) - active contour model , initialization , artificial intelligence , segmentation , level set (data structures) , computer science , computer vision , pattern recognition (psychology) , image segmentation , lesion , boundary (topology) , subtraction , vector flow , geodesic , noise (video) , mathematics , image (mathematics) , medicine , pathology , mathematical analysis , arithmetic , programming language
Background/Purpose Computer‐aided diagnosis of skin cancer requires accurate lesion segmentation, which must overcome noise such as hair, skin color variations, and ambient light variability. Methods A biologically inspired geodesic active contour ( GAC ) technique is used for lesion segmentation. The algorithm presented here employs automatic contour initialization close to the actual lesion boundary, overcoming the ‘sticking’ 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. Results The basic GAC algorithm was tested on 100 images (30 melanomas and 70 benign lesions), yielding a median XOR border error of 6.7%, comparable to the median inter‐dermatologist XOR border error (7.4%), and lower than the gradient vector flow snake median XOR error of 14.2% on the same image set. On a difficult low‐contrast border set of 1238 images, which included 350 non‐melanocytic lesions, a median XOR error of 23.9% is obtained. Conclusion GAC techniques show promise in attaining the goal of automatic skin lesion segmentation.