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Comparison of Statistical, LBP, and Multi-Resolution Analysis Features for Breast Mass Classification

  • Systems-Level Quality Improvement
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Abstract

Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Mammography is broadly recognized as an effective imaging modality for the early detection of breast cancer. Computer-aided diagnosis (CAD) systems are very helpful for radiologists in detecting and diagnosing abnormalities earlier and faster than traditional screening programs. An important step of a CAD system is feature extraction. This research gives a comprehensive study of the effects of different features to be used in a CAD system for the classification of masses. The features are extracted using local binary pattern (LBP), which is a texture descriptor, statistical measures, and multi-resolution frameworks. Statistical and LBP features are extracted from each region of interest (ROI), taken from mammogram images, after dividing it into N×N blocks. The multi-resolution features are based on discrete wavelet transform (DWT) and contourlet transform (CT). In multi-resolution analysis, ROIs are decomposed into low sub-band and high sub-bands at different resolution levels and the coefficients of the low sub-band at the last level are taken as features. Support vector machines (SVM) is used for classification. The evaluation is performed using Digital Database for Screening Mammography (DDSM) database. An accuracy of 98.43 is obtained using statistical or LBP features but when both these types of features are fused, the accuracy is increased to 98.63. The CT features achieved classification accuracy of 98.43 whereas the accuracy resulted from DWT features is 96.93. The statistical analysis and ROC curves show that methods based on LBP, statistical measures and CT performs equally well and they not only outperform DWT based method but also other existing methods.

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Acknowledgments

This work is supported by NSTIP strategic technologies programs, grant number 08-INF325-02 in the Kingdom of Saudi Arabia. We are thankful to the reviewer for their valuable comments.

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Authors and Affiliations

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

    Yasser A. Reyad

  2. Department of Computer Engineering and Sciences, Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt

    Mohamed A. Berbar

  3. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Muhammad Hussain

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  1. Yasser A. Reyad
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  3. Muhammad Hussain
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Correspondence to Yasser A. Reyad.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Reyad, Y.A., Berbar, M.A. & Hussain, M. Comparison of Statistical, LBP, and Multi-Resolution Analysis Features for Breast Mass Classification. J Med Syst 38, 100 (2014). https://doi.org/10.1007/s10916-014-0100-7

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