Automatic segmentation of melanoma using superpixel region growing technique

Abstract

Melanoma is the most life threatening type of cancer which contributes to the highest mortality rate. Early detection of melanoma facilitates better prognosis and increases survival rates. High infiltration of melanoma and advanced digital imaging technologies have exhilarated concern among the public, calling for initial screenings. However, melanoma screening is considered as a non trivial problem even by expert medical practitioners, in spite of several diagnostic algorithms. There is an enthralling need for automated melanoma detection systems due to the surge in the melanoma population and lack of trained dermatologists. Computational models for Melanoma detection are based on learning from the Region of Interest (RoI). Nevertheless, identification of RoI itself poses several challenges due to the diverse structural and chromatic features on the surface of the skin. This paper proposes a superpixel region growing based approach for segmentation of the melanoma region for further analysis. It is based on the Gaussian Mixture Model superpixels which segment the candidate image into accurate homogenous regions. The superiority of the system is demonstrated with performance metrics and comparisons on a standard dataset. This system is an impending solution to perform melanoma screenings with ease.