A Qualitative Approach on De-Noising and Segmentation Algorithms for Melanoma Images

Abstract

This paper presents a novel approach on preprocessing and segmentation algorithms of skin cancer images. Medical image processing involves preprocessing, segmentation, feature extraction & classification, and detection. Preprocessing is first step in image processing applications. Image resizing, de-noising, and hair removal are important stages in preprocessing. In this paper, De-noising of skin cancer images is the first approach and cancerous image segmentation is the second approach. As a process of preprocessing, skin cancer image de-noising is performed using median filter and Wiener filter in filtering method and in transform process, fixed form thresholding algorithm using biorthogonal wavelet (4.4)  at 5-level decomposition is implemented. The de-noised images were under segmentation process by edge detection algorithm, sub-band thresholding (LVL_MMC) algorithm, and partitioning in hierarchical tress (SPIHT) using MATLAB 2015. In de-noising, better results are obtained by median filter in terms of the performance metrics like MSE and PSNR, whereas in segmentation algorithms sub-band thresholding algorithm gives better results in terms of minimum number of pixels.