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- Title
Fusion of local and global features for classification of abnormality in mammograms.
- Authors
PHADKE, ANURADHA C.; REGE, PRITI P.
- 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%.
- Subjects
BREAST cancer diagnosis; MAMMOGRAMS; RADIOLOGISTS; HEALTH outcome assessment; SUPPORT vector machines
- Publication
Sādhanā: Academy Proceedings in Engineering Sciences, 2016, Vol 41, Issue 4, p385
- ISSN
0256-2499
- Publication type
Article
- DOI
10.1007/s12046-016-0482-y