Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images - Silveira, M.; Nascimento, J.C.; Marques, J.S.; Marcal, A.R.S.; Mendonca, T.; Yamauchi, S.; Maeda, J.; Rozeira, J. (IEEE Journal of Selected Topics in Signal Processing, 2009)
In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set (EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.