A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine

  • Omar S. Soliman
  • Aboul Ella Hassanien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)


This article introduces a computer aided diagnosis scheme using support vector machine, in conjunction with moment-based feature extraction. An application of ultrasound breast cancer imaging has been chosen and computer aided diagnosis scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The introduced scheme starts with a preprocessing phase to enhance the quality of the input breast ultrasound images and to reduce speckle without destroying the important features of input ultrasound images for diagnosis. This is followed by performing the seeded-threshold growing region algorithm in order to identify the region of interest and to detect the boundary of the breast pattern. Then, moment-based features are extracted. Finally, a support vector machine classifier were employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented scheme, we present tests on different breast ultrasound images. The experimental results obtained, show that the overall accuracy offered by the employed support vector machine was 98.1%, whereas classification ratio using neural network was 92.8%.


Breast Cancer Support Vector Machine Ultrasound Image Digital Mammography Suspicious Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Saeys, Y., Inza, I., Larraaga, P.: A review of feature selection techniques in bioinformatics. Journal of Bioinformatics 23(19), 2507–2517 (2007)CrossRefGoogle Scholar
  2. 2.
    Eadie, L.H., Taylor, P., Gibson, A.P.: A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. European Journal of Radiology (2011)Google Scholar
  3. 3.
    Cheng, H.D., Shan, J., Ju, W., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn. 43, 299–317 (2009)CrossRefGoogle Scholar
  4. 4.
    Gohring, J.T., Dale, P.S., Fan, X.: Detection of HER2 breast cancer biomarker using the opto-fluidic ring resonator biosensor. Sensors and Actuators, B: Chemical 146(1), 226–230 (2010)CrossRefGoogle Scholar
  5. 5.
    Shih, F.Y., Cheng, S.: Automatic seeded region growing for color image segmentation. Image and Vision Computing 23(10), 877–886 (2005)CrossRefGoogle Scholar
  6. 6.
    Zaim, A.: Automatic Segmentation of the Prostate from Ultrasound Data Using Feature-Based Self Organizing Map. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 1259–1265. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Kelly, K.M., Dean, J., Lee, S.-J., Comulada, W.S.: Breast cancer detection: radiologists’ performance using mammography with and without automated whole-breast ultrasound. Eur. J. of Radiology 20, 2557–2564 (2010)CrossRefGoogle Scholar
  8. 8.
    Kelly, K.M., Dean, J., Lee, S.-J., Scott Comulada, W.: Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur. J. of Radiology 20, 734–742 (2010)CrossRefGoogle Scholar
  9. 9.
    Flusser, J.: Moment Invariants in Image Analysis. World Academy of Science. Engineering and Technology 11, 376–381 (2005)Google Scholar
  10. 10.
    Sree, S.V., Ng, E.Y.-K., Acharya, R.U., Faust, O.: Breast imaging: A survey. World J. Clinical Oncology 2(4), 171–178 (2011)CrossRefGoogle Scholar
  11. 11.
    Luo, S.-T., Cheng, B.-W.: Diagnosing Breast Masses in Digital MammographyUsing Feature Selection and Ensemble Methods. J. Medical System (2010)Google Scholar
  12. 12.
    Tumen, R.S., Acer, M.E., Sezgin, T.M.: Feature extraction and classifier combination for image-based sketch recognition. In: Proc. SBIM 2010, pp. 63–70 (2010)Google Scholar
  13. 13.
    Talebi, M., Ayatollahi, A., Kermani, A.: Medical ultrasound image segmentation using genetic active contour. J. Biomedical Science and Engineering 4, 105–109 (2011)CrossRefGoogle Scholar
  14. 14.
    Huang, M.-L., Hung, Y.-H., Chen, W.-Y.: Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis. J. Medical System 34, 865–873 (2010)CrossRefGoogle Scholar
  15. 15.
    Alemn-Flores, M., Alemn-Flores, P., Lvarez-Len, L., Fuentes-Pavn, R., Santana-Montesdeoca, J.M.: Filtering, Segmentation and Feature Extraction in Ultrasound Evaluation of Breast Lesions. In: Bildverarbeitung fr die Medizin, pp. 168–172 (2008)Google Scholar
  16. 16.
    Mancas, M., Gosselin, B., Macq, B.: Segmentation using a region-growing thresholding. In: Image Processing: Algorithms and Systems, pp. 388–398 (2006)Google Scholar
  17. 17.
    Mat-Isa, N.A., Mashor, M.Y., Othman, N.H.: Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer. International Journal of The Computer, the Internet and Management 13(1), 61–70 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Omar S. Soliman
    • 1
  • Aboul Ella Hassanien
    • 1
    • 2
  1. 1.Faculty of Computers and InformationCairo UniversityCairoEgypt
  2. 2.Scientific Research Group in EgyptEgypt

Personalised recommendations