Some studies on statistical texture analysis methods and their application

Abstract

The main aim of texture analysis is to capture the tonal information of the image, since texture is a major property of an image that represents important information about the arrangement of features. Texture determines the overall visual smoothness of the image. Texture is measured statistically using a sliding window through out the image. In this study, the different methods of statistical texture analysis and comparisons have been done. As an application of texture analysis in medical image classification, mammogram images have been studied. In the first study, a new statistical method of texture analysis has been presented, which is focused on texture characterization and discrimination with features like Local Binary Pattern Operator, Texture Spectrum Operator and Entropy Based Local Descriptor. The second study of this thesis presents a new classification technique named Unsupervised Hybrid Classification for Texture Analysis (UHCTA) that integrates different unsupervised methods. The third study of this thesis presents an image classification method based on Pixel by Pixel with maximum likelihood estimates that must be compared to a single window classification not only to monochrome images but with the color images too. Texture analysis has been very much used in medical image problems. The fourth study of this thesis presents the application of both supervised and unsupervised statistical method for the detection of Microcalcification on Mammogram. On the whole, the texture analysis methods proposed and used in this dissertation are very useful in providing higher classification accuracy. The results suggest that the proposed classification methods for texture analysis perform significantly better in the way of incorporating improved accuracy measures. The approach would definitely increase its usability and adds more to the future prospects of improving classification in texture analysis for many applications. newline newline newline