Microcalcification and Macrocalcification Detection in Mammograms Based on GLCM and ODCM Texture Features Using SVM Classifier
( Vol-4,Issue-6,June 2017 )

Lincy Golda Careline S, Dr. J. S. Leena Jasmine


Computer-aided detection system, grey level co-occurance matric, optical density co-occurance matrix, feature extraction.


Breast cancer is a common cancer in women and the second leading cause of cancer deaths worldwide. Photographing the changes in internal breast structure due to formation of masses and microcalcification for detection of Breast Cancer is known as Mammogram, which are low dose x-ray images. These images play a very significant role in early detection of breast cancer. Usually in pattern recognition texture analysis is used for classification based on content of image or in image segmentation based on variation of intensities of gray scale levels or colours. Similarly texture analysis can also be used to identify masses and microcalcification in mammograms. However Grey Level Co-occurrence Matrices (GLCM) technique introduced by Haralick was initially used in study of remote sensing images. Radiologists f i n d i t d i f f i c u l t to identify the mass in a mammogram, since the masses are surrounded by pectoral muscle and blood vessels. In breast cancer screening, radiologists usually miss approximately 10% - 30% of tumors because of the ambiguous margins of tumors resulting from long-time diagnosis. Computer-aided detection system is developed to aid radiologists in detecting ma mammographic masses which indicate the presence of breast cancer. In this paper the input image is pre-processed initially that includes noise removal, pectoral muscle removal, thresholding, contrast enhancement and suspicious mass is detected and the features are extracted based on the mass detected. A feature extraction method based on grey level co- occurrence matrix and optical density features called GLCM -OD features is used to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, a support vector machine is used to classify abnormal regions by selecting the individual performance of each feature. The results prove that the proposed system achieves an excellent detection performance using SVM classifier.

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[1] American cancer society, “Breast cancer facts &figures 2015-2016”,Atlanta: American cancer society, Inc.2015.
[2] J. Grim, P. Somol, M. Haindl, and J. Danesˇ, “Computer-aided evaluation of screening mammograms based on local texture models,” Trans. Img. Proc., vol.18, pp. 765–773, Apr. 2009.
[3] Mohamed E. Elmanna, Yasser M. Kadah,“Implementation of Practical Computer Aided Diagnosis System for Classification of Masses in Digital Mammograms”, International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, 2015
[4] Jawad Nagi, Sameem Abdul Kareem, Farrukh Nagi, Syed Khaleel Ahmed,“Automated Breast Profile Segmentation for ROI Detection Using Digital Mammograms”, 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 30th November - 2nd December 2010.
[5] Indra Kanta Maitra, Sanjay Nag and Prof. Samir K. Bandyopadhyay, “Automated digital mammogram segmentation for detection of abnormal masses using binary homogeneity enhancement algorithm”, Indian Journal of Computer Science and Engineering (IJCSE) Vol. 2 No. 3 Jun-Jul 2011.
[6] N. Eltonsy, G. Tourassi, and A. Elmaghraby, “A concentric morphology model for the detection of masses in mammography,” Medical Imaging, IEEE Transactions on, vol. 26, pp. 880 –889, June 2007.
[7] Naga R. Mudigonda, Rangaraj M. Rangayyan and J.E Leo Desautels, “Detection of Breast Masses in Mammograms by Density Slicing and Texture Flow- Field Analysis”, IEEE Transactions On Medical Imaging, Vol. 20, No. 12, December 2001.
[8] S. Xu and C. Pei, “Hierarchical matching for automatic detection of masses in mammograms,” in Electrical and Control Engineering (ICECE), 2011 International Conference on, pp. 4523–4526,September2011.
[9] Shen-Chuan Tai, Zih-Siou Chen, and Wei-Ting Tsai, “An Automatic Mass Detection System in Mammograms based on Complex Texture Features”,IEEE Journal Of Biomedical And Health Informatics,Vol.18.No .2,March 2014.
[10] M. Sameti, R. Ward, J. Morgan-Parkes, and B. Palcic, “Image feature extraction in the last screening mammograms prior to detection of breast cancer,” Selected Topics in Signal Processing, IEEE Journal of, vol. 3, pp. 46 –52,February 2009.
[11] Bhagwati Charan Patel , G. R. Sinha ,“Mammography Feature Analysis and Images”,2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies.
[12] Cheng, H.D., Shi, X.J., Min, R., Hu, L.M., Cai, X.P., Du, “H.N: Approaches for Automated Detection and Classification of Masses in Mammograms”,Pattern Recognition 39(4), 646–668 (2006).
[13] I. Kitanovski, B. Jankulovski, I. Dimitrovski, and S. Loskovska, “Comparison of feature extraction algorithms for mammography images,” in Image and Signal Processing (CISP), 2011 4th International Congress on, vol. 2, pp. 888– 892,2011.
[14] Al Mutaz M. Abdalla, Safaai Dress, Nazar Zaki,“Detection of Masses in Digital Mammogram Using Second Order Statistics and Artificial Neural Network”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 3, June 2011.
[15] Snehal A. Mane, Dr. K. V. Kulhalli, “Mammogram Image Features Extraction and Classification for Breast Cancer Detection”, International Research Journal of Engineering and Technology (IRJET),Volume 2,Issue 7 , October 2015.
[16] Nasseer M. Basheer, Mustafa H. Mohammed, “Classification of Breast Masses in Digital Mammograms Using Support Vector Machines”International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.
[17] M. Hussain, S. Wajid, A. Elzaart, and M. Berbar, “A comparison of svm kernel functions for breast cancer detection,” in Computer Graphics, Imaging and Visualization (CGIV), 2011 Eighth International Conference on, pp. 145–150, 2011.
[18] Xiaoming Liu, Xin Xu, Jun Liu, Zhilin Feng,“ A new automatic method for mass detection in mammography with false positives reduction by supported vector machine”,2011 4th International Conference on Biomedical Engineering and Informatics.
[19] X. Gao, Y. Wang, X. Li, and D. Tao, “On combining morphological component analysis and concentric morphology model for mammographic mass detection,” Information Technology in Biomedicine, IEEE Transactions on, vol. 14, pp. 266 –273, March 2010.
[20] MIAS database, http://
[21] Zhili Chen, Harry Strange, Arnau Oliver, Erika R. E. Denton, Caroline Boggis, and Reyer Zwiggelaar,“Topological Modeling And Classification Of Mammographic Microcalcification Clusters”, IEEE Transactions On Biomedical Engineering, Vol. 62, No. 4, April 2015.
[22] Akshay S. Bharadwaj and Mehmet Celenk, “Detection Of Microcalcification With Top-Hat Transform And The Gibbs Random Fields”, 2015 IEEE.
[23] Luqman Mahmood Mina, Nor Ashidi Mat Isa,“Breast Abnormality Detection In Mammograms Using Artificial Neural Network”, 2015 IEEE , International Conference On Computer, Communication And Control Technology (I4ct 2015).
[24] C.Abirami, R.Harikumar, S.R.Sannasi Chakravarthy, “Performance Analysis And Detection Of Micro Calcification In Digital Mammograms Using Wavelet Features”, IEEE WISPNET 2016 CONFERENCE.
[25] Daniel Ruiz-Fernandez, Senior Member, Ieee, Juan José Galiana-Merino, And Manuela B. Pacheco Lloret, “Influence Of The Surrounded Tissue In The Detection Of Microcalcifications Using Wavelets”, 2015 IEEE.
[26] Juan Wang, Yongyi Yang, and Robert M. Nishikawa, “Quantitative Study Of Image Features Of Clustered Microcalcifications Inforpresentation Mammograms”, ICIP 2016.