Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature.


  • Keywords

    Breast mammogram;micro-calcification; benign; malignant; K-mean;, GLC;, SVM

  • References

      [1] WHO Summary report on HPV & cervical cancer statistics in India (18/03/2008)

      [2] Ferlay J, Soerjomataram I, Ervik M, et al.,” Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 11”. Lyon, France: International Agency for Research on Cancer,GLOBOCAN 2012 v1.0.

      [3] Bray F, Ren JS, Masuyer E, “Estimates of global cancer prevalence for 27 sites in the adult population in 2008. 2013”; Int J Cancer.; 132(5):1133-45. 2013.


      [5] R. M. Nishikawa, “Current status and future directions of computer-aided diagnosis in mammography”, Computerized Medical Imaging and Graphics, vol. 31, no. 4–5, pp. 224–235, 2007.

      [6] F. van Gelderen, “Understanding X-Rays: A Synopsis of Radiology”. Springer, 2004.

      [7] S. R. Amendolia, M. G. Bisogni, U. Bottigli, A. Ceccopieri, P. Delogu, M. E. Fantacci, A. Marchi, V. M. Marzulli, R. Palmiero, and S. Stumbo, “The CALMA project: a CAD tool in breast radiography,” Nuclear Inst. and Methods in Physics Research, A, vol. 460, no. 1, pp. 107–112, 2001.

      [8] Jinshan Tang, R. M. Rangayyan, Jun Xu, I. El Naqa, and Yongyi Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 236–251, 2009.

      [9] A. Papadopoulos, D. Fotiadis, and L. Costaridou, “Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques,” Computers in biology and medicine, vol. 38, no. 10, pp. 1045–1055, 2008.

      [10] H. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey,” Pattern Recognition, vol. 36, no. 12, pp. 2967–2991, 2003.

      [11] G. Tourassi, R. Vargas-Voracek, D. CatariousJr, and C. Floyd Jr, “Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information,” Medical Physics, vol. 30, p. 2123, 2003.

      [12] J. M. Park, E. A. Franken Jr, M. Garg, L. L. Fajardo, and L. T. Niklason, “Breast tomosynthesis: present considerations and future applications,” Radiographics, vol. 27, no. S1, p. S231, 2007.

      [13] L. Moy, K. Elias, V. Patel, J. Lee, J. Babb, H. Toth, and C. Mercado, “Is breast MRI helpful in the evaluation of inconclusive mammographic findings?,” Am. J. Roentgenol., vol. 193, no. 4, p. 986, Oct. 2009.

      [14] Li Jin, Wang Yan Wei, Wang Lei, WeiJie, Wang Da Da “Industrial X-Ray Image Enhancement Algorithm based on Adaptive Histogram and Wavelet” , 2011 The 6th International Forum on Strategic Technology 978-1-4577-0399-7111 ©2011IEEE pp 836-839.

      [15] Yu Guang Zhang, Wen Lu, Fu Yun Cheng, Li Song “A Edge Detection Method for microcalfication Clusters in Mammograms”, Science and Technology Development Project of Taishan Medical University (No.1240)

      [16] K.J. McLoughlin, P.J. Bones and N. Karssemeijer, “Noise equalization for detection of microcalcification clusters in direct digital mammogram images”, IEEE Transactions on Medical Imaging, vol.23, no.3, pp.313320, 2004.

      [17] NCI Cancer Fact Sheets. (2007). [Online]. Available:

      [18] T.Wang and N. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Trans. Med. Imag., vol. 17, no. 4, pp. 498–509, Aug. 1998.

      [19] M. Morton, D. Whaley, K. Brandt, and K. Amrami, “Screening mammograms: Interpretation with computer-aided detection Prospective evaluation”, Radiology, vol. 239, no. 2, pp. 375–383, 2006.

      [20] R. Brem, J. Baum, M. Lechner, S. Kaplan, S. Souders, L. Naul, and J. Hoffmeister, “Improvement in sensitivity of screening mammography with computer-aided detection: A multi institutional trial,” Amer. J. Roentgenol., vol. 181, no. 3, pp. 687–693, 2003.

      [21] Y. Peng, B. Yao, and J. Jiang, “Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis,” Artif. Intell. Med., vol. 37, no. 1, pp. 43–53, 2006.

      [22] M. P. Sampat and A. C. Bovik, “Detection of spiculated lesions in mammograms,” in Proc. 25th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2003, vol. 1, pp. 810–813.

      [23] R. Campanini, D. Dongiovanni, E. Iampieri, N. Lanconelli, M. Masotti, G. Palermo, A. Riccardi, and M. Roffilli, “A novel featureless approach to mass detection in digital mammograms based on support vector machines,” Phys. Med. Biol., vol. 49, no. 6, pp. 961–975, 2004.

      [24] N. H. Eltonsy, G. D. Tourassi, and A. S. Elmaghra by, “A concentric morphology model for the detection of masses in mammography,” IEEE Trans. Med. Imag., vol. 26, no. 6, pp. 880–889, Jun. 2007.

      [25] R. Bellotti, F. D. Carlo, S. Tangaro, G. Gargano, G. Maggipinto, M. Castellano, R. Massafra, D. Cascio, F. Fauci, R. Magro, G. Raso, A. Lauria, G. Forni, S. Bagnasco, P. Cerello, E. Zanon, S. C. Cheran, E. L. Torres, U. Bottigli, G. L. Masala, P. Oliva, A. Retico, M. E. Fantacci, R. Cataldo, I. D. Mitri, and G. D. Nunzio, “A completely automated CAD system for mass detection in a large mammographic database,” Med. Phys., vol. 33, no. 8, pp. 3066–3075, 2006.

      [26] JawadNagi, “The Application Of Image Processing And Machine Learning Techniques For Detection And Classification Of Cancerous Tissues In Digital Mammograms” Faculty Of Computer Science and Information Technology University Of Malaya Kuala Lumpur,


      [28] R. C. Gonzalez and R. E. Woods, “Digital Image Processing”. Reading, MA: Addison Wesley, reprint, 1992.

      [29] R. M. Nishikawa, “Current status and future directions of computer- aided diagnosis in mammography,” Computerized Medical Imaging and Graphics, vol. 31, no. 4–5, pp. 224–235, 2007.

      [30] Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B. “Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review”, Clin Imaging. 2013 May-Jun; 37(3):420-6. doi: 10.1016/j.clinimag.2012.09.024. Epub 2012 Nov 13.

      [31] Filipe Soares, Filipe Janela, Manuela Pereira, JoãoSeabra and Mário M. Freire, “ 3D Lacunarity in Multifractal Analysis of Breast Tumor Lesions in Dynamic Contrast Enhanced Magnetic Resonance Imaging”, IEEE Transactions on Image Processing, Volume 22, Issue 11, pp. 4422-4435, 2013.




Article ID: 11411
DOI: 10.14419/ijet.v7i2.16.11411

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.