Shannon and Non-Shannon Measures of Entropy for Statistical Texture Feature Extraction in Digitized Mammograms

Abstract

This paper deals with the problem of texture feature extraction in digital mammogram. For texture feature extraction, gray level histogram moments statistical texture analysis method is normally used. Entropy is an important texture feature, which is computed based on this method, to build a robust descriptor towards correctly classifying abnormal and normal regions of mammograms. Entropy measures the randomness of intensity distribution. In most feature descriptors, Shannon's measure is used to measure entropy. In this paper non-Shannon measures are used to measure entropy. Non-Shannon entropies have a higher dynamic range than Shannon entropy over a range of scattering conditions, and are therefore useful in estimating scatter density and regularity. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). The Results of this study are quite promising. This work is a part of developing a computer aided decision (CAD) system for early detection of breast cancer.

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