In this paper, we put forward a new pre–processing scheme for automatic analysis of dermoscopic images. Our contributions are two-fold. First, we present a procedure, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: these include shading induced by imaging non-flat skin surfaces and the effect of light-intensity falloff toward the edges of the dermoscopic image. This procedure is shown to facilitate the detection and removal of artifacts such as hairs as well. Second, we present a novel simple yet effective greyscale conversion approach that is based on physics and biology of human skin. Our proposed greyscale image provides high separability between a pigmented lesion and normal skin surrounding it. Finally, using our pre–processing scheme, we perform segmentation based on simple grey-level thresholding, with results outperforming the state of the art.
Ali Madooei, Mark S. Drew, Maryam Sadeghi, M. Stella Atkins, "Automated Pre–processing Method for Dermoscopic Images and its Application to Pigmented Skin Lesion Segmentation" in Proc. IS&T 20th Color and Imaging Conf., 2012, pp 158 - 163, https://doi.org/10.2352/CIC.2012.20.1.art00028