New Algorithm Techniques for Efficient Analysis of Tissue Images

Abstract

The importance of global features in the analysis of tissue images cannot be overemphasized especially in texture image classification and retrieval. This paper presents different techniques for detection, classification and analysis of diseases pattern in medical images. The research work studies the structure of tissue images; and extracts the similarity features characterized by the Holder exponent for pattern classification. Features from multi-fractal descriptors have been extracted and combined with features from fractal descriptors to generate new descriptors for efficient analysis of images. The experimental procedures have been tested with different extracted features during the classification process to determine the appropriate image features that could yield maximum detection accuracy. The results showed that the descriptors extracted from different features could improve the performance of the models. Our findings in this paper have greatly demonstrated the importance of global features in the analysis of tissue pattern.