2017/03/13 by S. M. Jaisakthi, Jaisakthi, S. M., Chandrabose Aravindan +4
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #Skin Protection and Aging #cs.CV
paper · pdf · doi:10.48550/arxiv.1703.04301
4 pages with 1 figure
arxiv created 2017/03/13 · openalex publication_date 2017/03/13 · arxiv updated 2017/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Skin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an automatic technique for skin lesion segmentation in dermoscopic images which helps in classifying the skin cancer types. The proposed method comprises of two major phases (1) preprocessing and (2) segmentation using semi-supervised learning algorithm. In the preprocessing phase noise are removed using filtering technique and in the segmentation phase skin lesions are segmented based on clustering technique. K-means clustering algorithm is used to cluster the preprocessed images and skin lesions are filtered from these clusters based on the color feature. Color of the skin lesions are learned from the training images using histograms calculations in RGB color space. The training images were downloaded from the ISIC 2017 challenge website and the experimental results were evaluated using validation and test sets.