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Mass Detection in Digital Mammograms Using Optimized Gabor Filter Bank

  • Muhammad Hussain
  • Salabat Khan
  • Ghulam Muhammad
  • George Bebis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

Breast cancer is the second major type of cancer that causes mortality among women. This can be reduced if the cancer is detected at its early stage but the existing methods result in a large number of false positives/negatives. Detection of masses is more challenging. A new method for mass detection is proposed that uses textural properties of masses. A Gabor filter bank is used for this purpose. The decision of how many Gabor filters must be there in the bank and the selection of the appropriate parameters of each individual Gabor filter is critical. Particle swarm optimization (PSO) and a clustering technique are used to design and select the optimal Gabor filter bank. Support vector machine (SVM) is used as an application oriented fitness criteria. The empirical evaluation of the method over 512 ROIs from DDSM database depicts that it yields better performance (99.41%) than the traditional Gabor filter bank and other state-of-the-art methods that exploit texture properties of masses.

Keywords

Support Vector Machine Particle Swarm Optimization Gabor Filter Mass Detection Gabor Feature 
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.

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References

  1. 1.
    Altekruse, S.F., Kosary, C.L., Krapcho, M., et al.: SEER Cancer Statistics Review, pp. 1975–2007. National Cancer Institute, Bethesda (2010)Google Scholar
  2. 2.
    Yufeng, Z.: Breast Cancer Detection with Gabor Features from Digital Mammograms. Algorithms 3(1), 44–62 (2010)CrossRefGoogle Scholar
  3. 3.
    Lladó, X., Oliver, A., Freixenet, J., Martí, R., Martí, J.: A textural approach for mass false positive reduction in mammography. Comp. Medical Imag. and Graph. 33(6), 415–422 (2009)CrossRefGoogle Scholar
  4. 4.
    Esteve, J., Kricker, A., Ferlay, J., Parkin, D.: Facts and figures of cancer in the European Community. Tech. rep., International Agency for Research on Cancer (1993)Google Scholar
  5. 5.
    Mohamed, M.E., Ibrahima, F., Brahim, B.S.: Breast cancer diagnosis in digital mammogram using multiscale curvelet transform, Comp. Medical Imag. and Graph. 34(4), 269–276 (2010)CrossRefGoogle Scholar
  6. 6.
    Zehan, S., George, B., Ronald, M.: On-road Vehicle Detection Using Evolutionary Gabor Filter Optimization. IEEE Trans. on Intell. Transp. System 6(2), 125–137 (2005)CrossRefGoogle Scholar
  7. 7.
    Ville, K., Joni-Kristian, K.: Simple Gabor feature space for invariant object recognition. Pattern Recognition Letter 25(3), 311–318 (2004)CrossRefGoogle Scholar
  8. 8.
    Manjunath, B., Ma, W.: Texture features for browsing and retrieval of image data. IEEE Tran. on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)CrossRefGoogle Scholar
  9. 9.
    Peter, K., Nikolay, P.: Nonlinear Operator for Oriented Texture. IEEE Tran. on I. Proc. 8(10), 1395–1407 (1999)Google Scholar
  10. 10.
    Daugman, J.G.: Two-dimensional spectral analysis of cortical receptive field profiles. Vis. Res. 20, 847–856 (1980)CrossRefGoogle Scholar
  11. 11.
    Zehan, S., George, B., Ronald, M.: Monocular Precrash Vehicle Detection: Features and Classifiers. IEEE Trans. on Image Proc. 15(7), 2019–2034 (2006)CrossRefGoogle Scholar
  12. 12.
    Yu, S., Shiguan, S., Xilin, C., Wen, G.: Hierarchical Ensemble of Global and Local Classifiers for Face Recognition. IEEE Trans. on Image Proc. 18(8), 1885–1896 (2009)CrossRefGoogle Scholar
  13. 13.
    Eberhartand, R.C., Kennedy, J.: Particle Swarm Optimization. In: Proc. of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Vapnik, V.: Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  15. 15.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.J.: The digital database for screening mammography. Int. Work. Dig. Mamm., 212–218 (2000)Google Scholar
  16. 16.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification, Technical report, Department of Computer Science and Information Engineering, National Taiwan University (2010)Google Scholar
  17. 17.
    Hussain, M., Khan, N.: Automatic mass detection in mammograms using multiscale spatial weber local descriptor. In: Proc. IWSSIP 2012, Austria, April 11-13 (to appear, 2012)Google Scholar
  18. 18.
    Lladó, X., Oliver, A., Freixenet, J., Martí, R., Martí, J.: A textural approach for mass false positive reduction in mammography. Computerized Medical Imaging and Graphics 33(6), 415–422 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Muhammad Hussain
    • 1
  • Salabat Khan
    • 3
  • Ghulam Muhammad
    • 2
  • George Bebis
    • 4
  1. 1.Department of Computer ScienceKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Computer Engineering, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  3. 3.National University of Computer and Emerging SciencesIslamabadPakistan
  4. 4.Department of Computer Science and EngineeringUniversity of Nevada at RenoUSA

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