Boundary detection of black skin tumors using an adaptive radial-based approach

Melanoma diagnosis greatly relies on the observation of some characteristic features on skin tumors. Similarly, a computer- based diagnostic system has been designed to detect these features. However, specificity and sensitivity of features often rely on their location within the tumor. Locating the border of lesions is therefore of utmost importance. Segmentation of tumors is not easy because of high variability of coloration from one tumor to another and because of numerous 'artifacts' such as hairs, or skin lines. An adaptive and supervised approach was consequently chosen to segment lesions. Dermatologists were asked to hand-outline borders of lesions and, after an automatic selection of a point inside the tumor, radial intensity profiles were generated. The location of border was associated with each of them. A neural network was taught to predict border from these labeled profiles. Our approach has been found efficient and robust in such a way that human correction of automatic segmentation are most of the time insignificant. Features such as asymmetry of texture and inhomogeneity of colors can subsequently be observed and their clinical significance evaluated.