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

Content Provider	Springer Nature Link
Author	Reyad, Yasser A. Berbar, Mohamed A. Hussain, Muhammad
Copyright Year	2014
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.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	38
Issue Number	9
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2014-07-19
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mammographic images Breast cancer detection Wavelets Contourlet Support vector machine Local binary pattern Statistical features Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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