Advertisement

Multiple Network CGP for the Classification of Mammograms

  • Katharina Völk
  • Julian F. Miller
  • Stephen L. Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)

Abstract

This paper presents a novel representation of Cartesian genetic programming (CGP) in which multiple networks are used in the classification of high resolution X-rays of the breast, known as mammograms. CGP networks are used in a number of different recombination strategies and results are presented for mammograms taken from the Lawrence Livermore National Laboratory database.

Keywords

Evolutionary algorithms Cartesian genetic programming mammography 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Kopans, D.B.: Lippincott Williams & Wilkins (2006)Google Scholar
  3. 3.
    Gonzalez, R., Woods, R.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  4. 4.
    Kim, J., Park, H.: Statistical Textural Features for Detection of Microcalcifications in Digitized Mammograms. IEEE Transactions Medical Imaging 18(3), 231–238 (1999)CrossRefGoogle Scholar
  5. 5.
    Fu, J.C., Lee, S.K., Wong, S.T.C., Yeh, J.Y., Wang, A.H., Wu, H.K.: Image segmentation feature selection and pattern classification for mammographic microcalcifications. Computerized Medical Imaging and Graphics 29, 419–429 (2005)CrossRefGoogle Scholar
  6. 6.
    Gavrielides, M., Lo, J., Vargas-Voracek, R., Floyd, C.: Segmentation of suspicious clustered microcalcifications in mammograms. Medical Physics 27 (2000)Google Scholar
  7. 7.
    Andolina, V., Lille, S., Willison, K.: Mammographic Imaging: A Practical Guide. Lippincott-Raven (1993)Google Scholar
  8. 8.
    Chan, H., Sahiner, B., Lam, K., Petrick, N., Helvie, M., Goodsitt, M., Adler, D.: Computerized analysis of mammographic microcalcifications in morphological and texture feature space. Medical Physics 25(10) (1998)Google Scholar
  9. 9.
    Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, 2967–2991 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Cai, X., Smith, S.L., Tyrrell, A.M.: Benefits of Employing an Implicit Context Representation on Hardware Geometry of CGP. In: Moreno, J.M., Madrenas, J., Cosp, J. (eds.) ICES 2005. LNCS, vol. 3637, pp. 143–154. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Walker, J., Walker, J.A., Miller, J.F., Cavill, R.: A Multi-chromosome Approach to Standard and Embedded Cartesian Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 903–910. ACM Press, New York (2006)Google Scholar
  13. 13.
    Lawrence Livermore National Laboratory database, https://www.llnl.gov/
  14. 14.
    Miller, J.F., Smith, S.L.: Redundancy and Computational Efficiency in Cartesian Genetic Programming. IEEE Transactions on Evolutionary Computation 10, 167–174 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Katharina Völk
    • 1
  • Julian F. Miller
    • 1
  • Stephen L. Smith
    • 1
  1. 1.Department of ElectronicsThe University of YorkHeslingtonUK

Personalised recommendations