Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel

Abstract

The main challenge in an automated diagnostic system for the early diagnosis of melanoma is the correct segmentation and classification of moles, often occluded by hair in images obtained with a dermoscope. Hair occlusion causes segmentation algorithms to fail to identify the correct nevus border, and can cause errors in estimating texture measures. We present a new method to identify hair in dermoscopic images using a universal approach, which can segment both dark and light hair without prior knowledge of the hair type. First, the hair is amplified using a universal matched filtering kernel, which generates strong responses for both dark and light hair without prejudice. Then we apply local entropy thresholding on the response to get a raw binary hair mask. This hair mask is then refined and verified by a model checker. The model checker includes a combination of image processing (morphological thinning and label propagation) and mathematical (Gaussian curve fitting) techniques. The result is a clean hair mask which can be used to segment and disocclude the hair in the image, preparing it for further segmentation and analysis. Application on real dermoscopic images yields good results for thick hair of varying colours, from light to dark. The algorithm also performs well on skin images with a mixture of both dark and light hair, which was not previously possible with previous hair segmentation algorithms.