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Fusion of local and global features for classification of abnormality in mammograms

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Abstract

Mammography is the most widely used tool for the early detection of breast cancer. Computer-based algorithms can be developed to improve diagnostic information in mammograms and assist the radiologist to improve diagnostic accuracy. In this paper, we propose a novel computer aided technique to classify abnormalities in mammograms using fusion of local and global features. The objective of this work is to test the effectiveness of combined use of local and global features in detecting abnormalities in mammograms. Local features used in the system are Chebyshev moments and Haralick’s gray level co-occurrence matrix based texture features. Global features used are Laws texture energy measures, Gabor based texture energy measures and fractal dimension. All types of abnormalities namely clusters of microcalcifications, circumscribed masses, spiculated masses, architectural distortions and ill-defined masses are considered. A support vector machine classifier is designed to classify the samples into abnormal and normal classes. It is observed that combined use of local and global features has improved classification accuracy from 88.75% to 93.17%.

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Acknowledgements

This work was funded by Board of College and University Development, University of Pune. Authors are grateful to Board of College and University Development for funding the project. The authors are also thankful to oncologists Dr. Shekhar Kulkarni and Dr. Aparna Atre for their valuable inputs. Authors would like to thank the reviewers for giving guidelines for improvement in the quality of the manuscript.

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  1. Department of Electronics and Telecommunication, College of Engineering, Pune, 411005, India

    Anuradha C Phadke & Priti P Rege

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  1. Anuradha C Phadke
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  2. Priti P Rege
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Correspondence to Anuradha C Phadke.

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Phadke, A.C., Rege, P.P. Fusion of local and global features for classification of abnormality in mammograms. Sādhanā 41, 385–395 (2016). https://doi.org/10.1007/s12046-016-0482-y

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