Segmentation Of Skin Lesion And Cancer Using Fast Fuzzy C Means Clustering

Abstract

The aim of the project is to classify skin lesion and cancer. The objective of this project is to provide an efficient way to segment the skin cancer images. A novel method is suggested that includes color and texture for the segmentation of skin lesions from unaffected skin region in an image. This project proposes a novel approach for classification of skin lesion and cancer images. The proposed work comprises of Pre-Processing, Segmentation, Feature extraction and Classification. In the Pre-Processing stage, Anisotropic diffusion Filter is implemented to remove noise and undesired structures from the images. In the Segmentation stage Fast Fuzzy C Means clustering method is involved in order to acquire a contour by means of the gradient flow that reduces an energy function with a distance standardize term and an external energy that acquire motion of the zero level set regarding desired locations. The Gray level Co-occurrence Matrix (GLCM) and bandlet transform are used to estimate the features of the segmented image. The convolution neural network classifier is employed for the classification task, utilizing feature vectors derived from gray level co-occurrence (GLCM) features. Accuracy, sensitivity and specificity are evaluated with the use of the classification results. An automated Matlab tool is developed for classification of skin lesion and cancer.