Breast Mass Classification using Statistical and Local Binary Pattern Features

Millions of women are suffering from breast cancer today. Breast cancer can be treated effectively when detected early. Mammography is broadly recognized as the most effective imaging modality for an early detection of breast cancer abnormalities. Computer-aided diagnosis systems are very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. In this paper, two techniques are proposed based on statistical and LBP features using support vector machine (SVM) and the k-nearest neighbor (KNN) classifiers. The evaluation of the system is applied on Digital Database for Screening Mammography (DDSM). The system classifies normal from abnormal cases with high accuracy rate.

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

[2]  Asoke K. Nandi,et al.  Toward breast cancer diagnosis based on automated segmentation of masses in mammograms , 2009, Pattern Recognit..

[3]  F Levi,et al.  Mortality from major cancer sites in the European Union, 1955-1998. , 2003, Annals of oncology : official journal of the European Society for Medical Oncology.

[4]  K. Revathy,et al.  A neural network approach to mammogram image classification using fractal features , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[5]  S Issac Niwas,et al.  Wavelet based feature extraction method for breast cancer cytology images , 2010, 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA).

[6]  Salim Lahmiri,et al.  DWT and RT-based approach for feature extraction and classification of mammograms with SVM , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[7]  A. Malagelada.  Automatic mass segmentation in mammographic images , 2007 .

[8]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

[9]  Samir Brahim Belhaouari,et al.  Breast cancer diagnosis in digital mammogram using multiscale curvelet transform , 2010, Comput. Medical Imaging Graph..

[10]  Qi Zhang,et al.  Medical Image Retrieval Using Local Binary Patterns with Image Euclidean Distance , 2009, 2009 International Conference on Information Engineering and Computer Science.

[11]  Ranadhir Ghosh,et al.  A Modular Framework for Multi category feature selection in Digital mammography , 2004, ESANN.

[12]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[13]  Arnau Oliver,et al.  Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms , 2005, IbPRIA.

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

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

[16]  Geraldo Braz Junior,et al.  Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM. , 2009, Computers in biology and medicine.

[17]  Baoying Wang,et al.  Feature selection and analysis on mammogram classification , 2009, 2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.

[18]  Sung-Nien Yu,et al.  Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features , 2010, Expert Syst. Appl..

[19]  Bijay Ketan Panigrahi,et al.  Feature Extraction for Classification of Microcalcifications and Mass Lesions in Mammograms , 2009 .