Learning Based Segmentation of Skin Lesion from Dermoscopic Images

Segmentation is the pre-requisite process in most of the computer aided diagnosis systems for medical imaging. Presence of different artifacts makes segmentation of skin lesion very difficult. Abnormal growth of artifacts can appear as false positives and can degrade the performance of the diagnosis systems. It can be avoided only when false structures are removed while extracting the lesion. To address this issue, this paper proposes deep leaning for skin lesion segmentation. Within this framework, automated skin lesion segmentation is proposed which achieves high accuracy segmentation of skin lesion. Our proposed architecture is 31 layers deep with same filter size. The validity of the proposed techniques is tested on two publically available databases of PH2 and ISIC 2017. Experimental results show the efficiency of the proposed approaches. The proposed method gives Dice Coefficient of 92.3% for PH2 Dataset while Dice Coefficient of 85.5% for ISIC 2017 Dataset.