Hair detection in dermoscopic images using Percolation

The automatic analysis of dermoscopy images is often impaired by artifacts such as air bubbles, specular reflections or dark hair covering the skin lesions. Consequently, an important pre-processing step includes their detection and elimination. The most common and probably the most compromising of these artifacts is the presence of hair and therefore specific algorithms are required for its detection. This paper proposes a method for the detection of hair in dermoscopy images based on an efficient percolation algorithm for image processing recently proposed in [1]. The percolation algorithm locally processes image points by taking into account the intensity and connectivity of neighboring pixels. A cluster of connected points is thus obtained and the shape of this cluster is subsequently analyzed. If the cluster has a shape that is approximately linear then the image point is classified as hair. The performance of the proposed method was investigated on real dermoscopy images and compared with the DullRazor software [2]. Our results indicate that the method provides effective hair detection outperforming the DullRazor method by more than 10%, both in terms of false positive and false negative rates.