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.