SKIN LESION SEGMENTATION BY PIXEL BY PIXEL APPROACH USING DEEP LEARNING

Abstract

Skin lesion segmentation is an imperative step for image analysis and visualization task. Manual segmentation by an expert operator is too time-consuming and its accuracy may be degraded by different human operators. An automatic segmentation method is therefore required and one of the important parts in any classification system. In this work, more accurate skin lesion segmentation by Pixel-by-Pixel (PbP) approach using deep learning is presented. Before employing PbP approach, dermoscopic images are prepared for more accurate segmentation by Top-Hat Transform (THT) which removes the hair in the skin regions. The PbP approach has four stages; study the training images consists of skin lesions, construction of deep learning network followed by training it and finally evaluate the network with testing images. The evaluation of PbP approach is carried out using PH2 database images. Results of PbP approach in terms of Jaccard Index (JI), Accuracy (Acc) and DIce Coefficients (DIC) show the effectiveness of the system for skin lesion segmentation.