Contrast based lesion segmentation on DermIS and DermQuest datasets.
In this companion paper, we present the results obtained by our contrast based approach for lesion segmentation on two datasets used by amelard et al. (Amelard, J. Glaister, A. Wong and D. A. Clausi, High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description, IEEE Transactions on Biomedical Engineering, 62(3) (2015), 820-831). The first is a subset of a DermIS database (Dermatology Information System, available online: http://www.dermis.net) that contains 43 macroscopic photographs with lesion diagnosed as melanoma and 26 diagnosed as non melanoma. The second is a subset of the DermQuest database (Available online: http://www.dermquest.com) that contains 76 images of melanoma lesions and 61 images of non melanoma lesions. The images are subject to various artifacts like drastic shadow effect and the variation of illumination. We also provide the ground truth segmentation to evaluate visually the accuracy of our results.