Mass Description for Breast Cancer Recognition

  • Imene Cheikhouhou
  • Khalifa Djemal
  • Hichem Maaref
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

In this paper, we present a robust shape descriptor named the Mass Descriptor based on Occurrence intersection coding (MDO) using the contour fluctuation detection. This descriptor allows a good characterization of the breast lesions and so a good classification performance. The efficiency of the proposed descriptor is evaluated on known Digital Database for Screening Mammography DDSM using the area under the Receiver Operating Characteristics (ROC) curve analysis. Results show that the specified descriptor has proven its performance in breast mass recognition using Support Vector Machine (SVM) classifier.

Keywords

Support Vector Machine Digital Database Benign Mass Radial Length Occurrence Number 
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.

References

  1. 1.
    American College of Radiology BI-RADS (Breast Imaging Reporting and Data System) Frensh Edition realized by SFR, 3rd edn. (2003)Google Scholar
  2. 2.
    Sahiner, B.S., Chan, H.P., Petrick, N., Helvie, M.A., Hadjiiski, L.M.: Improvement of mammographic mass characterization using spiculation measures and morphological features. Med. Phys. 28(7), 1455–1465 (2001)CrossRefGoogle Scholar
  3. 3.
    Rangayyan, R.M., Mudigonda, N.R., Desautels, J.E.L.: Boundary modelling and shape analysis methods for classification of mammographic masses. Med. Biol. Eng. Comput. 38, 487–496 (2000)CrossRefGoogle Scholar
  4. 4.
    Rangayyan, R.M., Nguyen, T.M.: Fractal Analysis of Contours of Breast Masses in Mammograms. Journal of Digital Imaging 20(3), 223–237 (2007)CrossRefGoogle Scholar
  5. 5.
    El-Faramawy, N.M., Rangayan, R.M., Desautels, J.E.L., Alim, O.A.: Shape factors for analysis of breast tumors in mammograms. In: Canadian Conference on Electrical and Computer Engineering, pp. 355–358 (1996)Google Scholar
  6. 6.
    Kilday, J., Palmieri, F., Fox, M.D.: Classifying mammographic lesions using computer-aided image analysis. IEEE Trans. Med. Imaging 20, 664–669 (1993)CrossRefGoogle Scholar
  7. 7.
    Adler, W., Lausen, B.: Bootstrap estimated true and false positive rates and ROC curve. Journal of Computational Statistics and Data Analysis, 1–12 (2008)Google Scholar
  8. 8.
    Barman, H., Granlund, G., Haglund, L.: Feature extraction for computer aided analysis of mammograms. In: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), pp. 1339–1356 (1993)Google Scholar
  9. 9.
    Sim, D.G., Kim, H.K., oh, D.I.: Translation Scale and Rotation Invariant Texture Descriptor for Texture based Image Retrieval. In: International Conference on Image processing proceedings, vol. 3, pp. 742–745 (2000)Google Scholar
  10. 10.
    Vapnik, V.: Statistical learnig theory. Wiley, New York (1998)Google Scholar
  11. 11.
    Vyas, V.S., Rege, P.P.: Radon Transform application for rotation invariant texture analysis using Gabor filters. In: Proceedings of NCC-2007, IIT Kanpur, pp. 439–442 (2007)Google Scholar
  12. 12.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The Digital Database for Screening Mammography. In: 5th International Workshop on Digital Mammography, Toronto, Canada (2000)Google Scholar
  13. 13.
    Chen, C.Y., Chiou, H.J., Chou, Y.C., Wang, H.K., Chou, S.Y., Chiang, H.K.: Computer-aided Diagnosis of Soft Tissue Tumors on High-resolution Ultrasonography with Geometrical and Morphological Features. Academic Radiology, 618–626 (2009)Google Scholar
  14. 14.
    Hadjiiski, L., Chan, H.P., Sahiner, B., Helvie, M.A., Roubidoux, M.A., Blane, C., Paramagul, C., Petrick, M.N., Bailey, J., Klein, K., Foster, M., Patterson, S., Adler, A., Nees, A., Shen, J.: Improvement in Radiologists Characterization of Malignant and Benign Breast Masses on Serial Mammograms with Computer-aided Diagnosis: An ROC Study. Radiology, 255–265 (2004)Google Scholar
  15. 15.
    Shen, L., Rangayyan, R.M., Desautels, J.E.L.: Detection and classification of mammographic calcifications. Intemational Journal of Pattern Recognition and Artificial Intelligence 7(6), 1403–1416 (1993)CrossRefGoogle Scholar
  16. 16.
    Cheikhrouhou, I., Djemal, K., Sellami, D., Maaref, H., Derbel, N.: Empirical Descriptors Evaluation for Mass Malignity Recognition. In: The First International Workshop on Medical Image Analysis and Description for Diagnosis Systems - MIAD’09, Porto, Portugal, January 16-17, pp. 91–100 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Imene Cheikhouhou
    • 1
  • Khalifa Djemal
    • 1
  • Hichem Maaref
    • 1
  1. 1.IBISC LaboratoryEvry Val d’Essonne UniversityEvryFrance

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