SEGMENTATION OF SKIN LESION AND CANCER USING FAST FUZZY C MEANS CLUSTERING

Authors

  • Ms.S.Premalatha

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.

Published

2020-10-16

Issue

Section

Articles