Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions

Abstract

The segmentation of skin lesions is crucial to the early and accurate identification of skin cancer by computerized systems. It is difficult to automatically divide skin lesions in dermoscopic images because of challenges such as hairs, gel bubbles, ruler marks, fuzzy boundaries, and low contrast. We proposed an effective method based on K-means and a trainable machine learning system to segment regions of interest (ROI) in skin cancer images. The proposed method was implemented in several stages, including grayscale image conversion, contrast image enhancement, artifact removal with noise reduction, skin lesion segmentation from image using K-means clustering, and ROI segmentation from unwanted objects using a trainable machine learning system. The proposed model has been evaluated using the ISIC 2017 publicly available dataset. The proposed method obtained a 90.09 accuracy rate, outperforming several methods in the literature.