Fusion of local and global features for classification of abnormality in mammograms

Mammography is the most widely used tool for the early detection of breast cancer. Computer-based algorithms can be developed to improve diagnostic information in mammograms and assist the radiologist to improve diagnostic accuracy. In this paper, we propose a novel computer aided technique to classify abnormalities in mammograms using fusion of local and global features. The objective of this work is to test the effectiveness of combined use of local and global features in detecting abnormalities in mammograms. Local features used in the system are Chebyshev moments and Haralick’s gray level co-occurrence matrix based texture features. Global features used are Laws texture energy measures, Gabor based texture energy measures and fractal dimension. All types of abnormalities namely clusters of microcalcifications, circumscribed masses, spiculated masses, architectural distortions and ill-defined masses are considered. A support vector machine classifier is designed to classify the samples into abnormal and normal classes. It is observed that combined use of local and global features has improved classification accuracy from 88.75% to 93.17%.

[1]  Oleg Starostenko,et al.  Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transfo , 2009 .

[2]  Rangaraj M. Rangayyan,et al.  Fractal Analysis of Contours of Breast Masses in Mammograms , 2007, Journal of Digital Imaging.

[3]  Priti P. Rege,et al.  Geometric Transform Invariant Texture Analysis with Modified Chebyshev Moments Based Algorithm , 2009, Int. J. Image Graph..

[4]  Priti P. Rege,et al.  Comparison of SVM and ANN classifier for mammogram classification using ICA features , 2014 .

[5]  Vibha Vyas,et al.  Malignancy Texture Classification in Digital Mammograms based on Chebyshev Moments and Logpolar Transformation , 2007 .

[6]  S SHANTHI,et al.  A novel approach for classification of abnormalities in digitized mammograms , 2014 .

[7]  Dipti Prasad Mukherjee,et al.  Recognizing Architectural Distortion in Mammogram: A Multiscale Texture Modeling Approach with GMM , 2011, IEEE Transactions on Biomedical Engineering.

[8]  J. Biswas,et al.  Breast cancer scenario in a regional cancer centre in Eastern India over eight years--still a major public health problem. , 2012, Asian Pacific journal of cancer prevention : APJCP.

[9]  Rangaraj M. Rangayyan,et al.  Detection of Architectural Distortion in Prior Mammograms , 2011, IEEE Transactions on Medical Imaging.

[10]  B. Santhi,et al.  MAMMOGRAM CLASSIFICATION USING MAXIMUM DIFFERENCE FEATURE SELECTION METHOD , 2011 .

[11]  Anuradha C. Phadke,et al.  Classification of Architectural Distortion from Other Abnormalities in Mammograms , 2013 .

[12]  Essam A. Rashed,et al.  Multiresolution mammogram analysis in multilevel decomposition , 2007, Pattern Recognit. Lett..

[13]  SARWESH PARADKAR,et al.  Intelligent detection of microcalcification from digitized mammograms , 2011 .

[14]  R. Besar,et al.  Biomedical Imaging and Intervention Journal Identification of Masses in Digital Mammogram Using Gray Level Co-occurrence Matrices , 2022 .

[15]  Xavier Lladó,et al.  Automatic microcalcification and cluster detection for digital and digitised mammograms , 2012, Knowl. Based Syst..

[16]  L. Costaridou,et al.  Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. , 2007, The British journal of radiology.

[17]  M. Eberl,et al.  BI-RADS classification for management of abnormal mammograms. , 2006, Journal of the American Board of Family Medicine : JABFM.

[18]  T. Balakumaran,et al.  Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering , 2010, ArXiv.

[19]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[20]  Alan C. Bovik,et al.  Detection of spiculated lesions in mammograms , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[21]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.

[22]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[23]  Arianna Mencattini,et al.  Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[24]  Diaz D. Santika,et al.  Classification of Microcalcification Using Dual-Tree Complex Wavelet Transform and Support Vector Machine , 2010, 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies.