Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique
This work addresses early skin cancer diagnosis through automated segmentation, but it appears incremental as it builds on existing clustering and feature techniques.
The authors tackled skin lesion segmentation in dermoscopic images to aid in skin cancer classification, proposing a method that uses preprocessing and semi-supervised learning with K-means clustering and color features, achieving results evaluated on ISIC 2017 datasets.
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.