Performance measure based segmentation techniques for skin cancer detection

Abstract

Skin Cancer is a very common form of cancer which initially starts with investigation and analysis going through biopsy and examination. Doing analysis is the most challenging task as it depends on appearance of skin lesion. Computer Aided Diagnostic (CAD) system have been developed for skin cancer detection which goes through various phases starting from pre-processing, segmentation, feature extraction & selection and classification of cancer type. Segmentation is an important as well as difficult phase which extracts the lesion from non-lesion area depending on variation in terms of color, texture, size and shape. In this paper, different segmentation techniques have been discussed, Otsu thresholding as Pixel Based Segmentation, Canny edge detection as Edge based Segmentation, Watershed as Region Based Segmentation and K-Means as Clustering based Segmentation. The performance of techniques have been measured by Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Structure Similarity Index Measure (SSIM) using MATLAB.