Improving skin lesion segmentation in dermoscopic images by thin artefacts removal methods

In dermoscopic images, various thin artefacts naturally appear, most usually in the form of hairs. While trying to find the border of the skin lesion, these artefacts effect the lesion segmentation methods and also the subsequent classification. Currently, there is a lot of research focus in this area and various methods are presented both for skin lesion segmentation and thin artefacts removal. In this paper, we investigate into three different thin artefacts removal methods and compare their results using two different skin lesion segmentation methods. The segmentation results are compared with ground truth segmentation. In addition, we introduce our novel artefacts removal method, which combined with the Expectation Maximization image segmentation outperforms all the tested methods.