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Link to original content: https://doi.org/10.1007/s11042-015-3017-3
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A comparison of different Gabor feature extraction approaches for mass classification in mammography

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Abstract

We investigate the performance of six different approaches for directional feature extraction for mass classification problem in digital mammograms. These techniques use a bank of Gabor filters to extract the directional textural features. Directional textural features represent structural properties of masses and normal tissues in mammograms at different orientations and frequencies. Masses and micro-calcifications are two early signs of breast cancer which is a major leading cause of death in women. For the detection of masses, segmentation of mammograms results in regions of interest (ROIs) which not only include masses but suspicious normal tissues as well (which lead to false positives during the discrimination process). The problem is to reduce the false positives by classifying ROIs as masses and normal tissues. In addition, the detected masses are required to be further classified as malignant and benign. The feature extraction approaches are evaluated over the ROIs extracted from MIAS database. Successive Enhancement Learning based weighted Support Vector Machine (SELwSVM) is used to efficiently classify the generated unbalanced datasets. The average accuracy ranges from 68 to 100 % as obtained by different methods used in our paper. Comparisons are carried out based on statistical analysis to make further recommendations.

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Acknowledgments

This project was supported by NSTIP strategic technologies programs, grant number 08-INF325-02 in the Kingdom of Saudi Arabia.

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Authors and Affiliations

  1. Department of Computer Science, Comsats Institute of Information Technology, Attock, Pakistan

    Salabat Khan

  2. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Muhammad Hussain & Hatim Aboalsamh

  3. Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA

    George Bebis

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  1. Salabat Khan
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  2. Muhammad Hussain
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  3. Hatim Aboalsamh
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  4. George Bebis
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Correspondence to Salabat Khan.

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Khan, S., Hussain, M., Aboalsamh, H. et al. A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimed Tools Appl 76, 33–57 (2017). https://doi.org/10.1007/s11042-015-3017-3

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