Title: Design, analysis and classifier evaluation for a CAD tool for breast cancer detection from digital mammograms

Authors: Subodh Srivastava; Neeraj Sharma; Sanjay Kumar Singh; Rajeev Srivastava

Addresses: School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, IIT (BHU), Varanasi 221005, UP, India ' School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, IIT (BHU), Varanasi 221005, UP, India ' Department of Computer Science & Engineering, Indian Institute of Technology, Banaras Hindu University, IIT (BHU), Varanasi 221005, UP, India ' Department of Computer Science & Engineering, Indian Institute of Technology, Banaras Hindu University, IIT (BHU), Varanasi 221005, UP, India

Abstract: In this paper, the design, analysis, and classifier evaluation for a computer aided diagnostics (CAD) tool for early breast cancer detection from mammograms is presented. The design steps of a CAD tool include enhancement, segmentation, feature extraction and selection, and classification of images. A contrast limited histogram equalisation method is used for image enhancement followed by cropping of region of interests. The fuzzy C-means method is used for segmenting abnormalities present in the images. A total of 88 hybrid features are extracted for each image. For feature selection, minimum redundancy and maximum relevancy approach has been used. For decision making, the various classifiers examined for their efficacy, for 322 images available in MIAS database, include SVMs for its various kernel choices, k-NN, and ANN. Finally, it is observed that the SVM classifier for the MLP kernel choice is performing better in comparison to all other classifiers in consideration along with the other design steps as above.

Keywords: breast cancer detection; CAD tools; computer aided diagnostics; hybrid features; MRMR feature selection; SVM; support vector machines; k-NN; k-nearest neighbour; ANNs; artificial neural networks; classifier evaluation; digital mammograms; image enhancement; image segmentation; feature extraction; image classification; histogram equalisation; fuzzy C-means.

DOI: 10.1504/IJBET.2013.058447

International Journal of Biomedical Engineering and Technology, 2013 Vol.13 No.3, pp.270 - 300

Received: 04 Jun 2013
Accepted: 30 Nov 2013
Published online: 27 Sep 2014 *

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