Skin Images Segmentation

Abstract

Problem statement: Image segmentation is a fundamental step in many a pplications of image processing. Skin cancer has been the most common of all new cancers detected each year. At early stage detection of skin cancer, simple and ec onomic treatment can cure it mostly. An accurate segmentation of skin images can help the diagnosis to define well the region of the cancer. The principal approach of segmentation is based on thre sholding (classification) that is lied to the probl em of the thresholds estimation. Approach: The objective of this study is to develop a method to segment the skin images based on a mixture of Beta distribu tions. We assume that the data in skin images can be modeled by a mixture of Beta distributions. We u sed an unsupervised learning technique with Beta distribution to estimate the statistical parameters of the data in skin image and then estimate the thresholds for segmentation. Results: The proposed method of skin images segmentation was implemented and tested on different skin images. We obtained very good results in comparing with the same techniques with Gamma distribution. Conclusion: The experiment showed that the proposed method obtained very good results but it requires m ore testing on different types of skin images.