Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique

TL;DR

This paper presents an automatic skin lesion segmentation method using semi-supervised learning, combining preprocessing with noise removal and clustering-based segmentation to aid early skin cancer diagnosis.

Contribution

It introduces a semi-supervised approach integrating K-means clustering and color feature analysis for skin lesion segmentation in dermoscopic images.

Findings

Effective noise removal in preprocessing

Successful clustering-based segmentation

Validated on ISIC 2017 dataset

Abstract

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.