Transition region-based approach for skin lesion segmentation

Abstract

Skin melanoma is a skin disease that affects nearly 40% of people globally. Manual detection of the area is a time-consuming process and requires expert knowledge. The application of computer vision techniques can simplify this. In this article, a novel unsupervised transition region-based approach for skin lesion segmentation for melanoma detection is proposed. The method starts with the Gaussian blurring of the green channel dermoscopic image. Further, the transition region is extracted using local variance features and a global thresholding operation. It achieves the region of interest (binary mask) using various morphological operations. Finally, the melanoma regions are segregated from normal skin regions using the binary mask. The proposed method is tested using the DermQuest dataset along with the ISIC 2017 dataset, and it achieves better results as compared to other state-of-the-art methods in effectively segmenting the melanoma regions from the normal skin regions.