Automatic segmentation of infantile hemangiomas within an optimally chosen color space

Abstract

Infantile hemangiomas (IH) are benign vascular tumors, most of them appearing in the first weeks and developing until six months of age. The evaluation of the lesion size is usually made by the physician through manual measurement, which is inaccurate. This paper presents an algorithm for the automatic segmentation of the hemangioma region, relying on the Maximum a Posteriori (MAP) classification method. The segmentation result is improved by regularization with discrete Markov fields (MAP-Markov). Then, a further improvement is performed, by eliminated distant non-hemangioma pixels. The optimal color space is chosen before segmentation, from five different color spaces, by iteratively computing the segmentation error 10 times on each color space and each of the 40 images from the database. The segmentation performance is evaluated in terms of border error.