Abnormal mass classification in breast mammography using rotation invariant LBP

We present a novel approach for abnormal breast mass classification from digitized mammography images. The proposed framework exploits rotation invariant uniform Local Binary Pattern (LBP) as texture feature. These features are classified using Support Vector Machine (SVM). In addition, we take advantage of the breast mammograms taken from multiple views or angles. We classify breast scans from ‘cranial-caudal’ view and ‘mediolateral-oblique’ view separately, and combine these classification scores to make an improved diagnosis. This reduces the classification error, and achieves higher recognition rate than that of either views individually. The proposed computer aided diagnosis system was evaluated on DDSM (Digital Database for Screening Mammography) data set, and was able to achieve a classification accuracy of 74%.

[1]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Matti Pietikäinen,et al.  Computer Vision Using Local Binary Patterns , 2011, Computational Imaging and Vision.

[3]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[5]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[6]  Tasnuva Hassan,et al.  Handwritten Bangla numeral recognition using Local Binary Pattern , 2015, 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).

[7]  Brijesh Verma,et al.  Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer , 2010, Expert Syst. Appl..

[8]  J Ryan,et al.  Mammographic screening for breast cancer. , 2003, The New England journal of medicine.

[9]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  A. Kandaswamy,et al.  Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms , 2007, Comput. Medical Imaging Graph..

[11]  Shie Mannor,et al.  Robustness and Regularization of Support Vector Machines , 2008, J. Mach. Learn. Res..

[12]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[13]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[14]  S. Duffy,et al.  The value of the second view in screening mammography. , 1996, The British journal of radiology.

[15]  Zohreh Azimifar,et al.  Contourlet-based mammography mass classification using the SVM family , 2010, Comput. Biol. Medicine.

[16]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .