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2010 | OriginalPaper | Chapter

26. Texture Features Extraction in Mammograms Using Non-Shannon Entropies

Authors : Amar Partap Singh, Baljit Singh

Published in: Machine Learning and Systems Engineering

Publisher: Springer Netherlands

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Abstract

This paper deals with the problem of texture-features-extraction in digital mammograms using non-Shannon measures of entropy. Texture-features-extraction is normally achieved using statistical texture-analysis method based on gray-level histogram moments. Entropy is important texture feature to measure the randomness of intensity distribution in a digital image. Generally, Shannon’s measure of entropy is employed in various feature-descriptors implemented so far. These feature-descriptors are used for the purpose of making a distinction between normal and abnormal regions in mammograms. As non-Shannon entropies have a higher dynamic range than Shannon’s entropy covering much wider range of scattering conditions, they are more useful in estimating scatter density and regularity. Based on these considerations, an attempt is made to develop a new type of feature-descriptor using non-Shannon’s measures of entropy for classifying normal and abnormal mammograms. Experiments are conducted on images of mini-MIAS (Mammogram Image Analysis Society) database to examine its effectiveness. The results of this study are quite promising for extending the work towards the development of a complete Computer Aided Diagnosis (CAD) system for early detection of breast cancer.

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Metadata
Title
Texture Features Extraction in Mammograms Using Non-Shannon Entropies
Authors
Amar Partap Singh
Baljit Singh
Copyright Year
2010
Publisher
Springer Netherlands
Book
Machine Learning and Systems Engineering

Print ISBN: 978-90-481-9418-6

Electronic ISBN: 978-90-481-9419-3

Copyright Year: 2010

DOI
https://doi.org/10.1007/978-90-481-9419-3_26

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