Advertisement

A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in Digital Mammograms

  • Alessandro Bevilacqua
  • Renato Campanini
  • Nico Lanconelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

In this paper, we investigate the improvement obtained by applying a distributed genetic algorithm to a problem of parameter optimization in medical images analysis. We setup a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform. The optimization of this scheme requires multiple runs on a set of 40 images, in order to obtain relevant statistics.We aim to evaluate how fluctuations of some parameters values of the detection method influence the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some percents previous results obtained after having “hand tuned” these parameters for a long time. At last, we have been able to find out parameters not influencing performance at all.

Keywords

Genetic Algorithm Gray Level Multiresolution Analysis True Signal Wall Clock Time 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bazzani, A., Bevilacqua, A., Bollini, D., Brancaccio, R., Campanini, R., Lanconelli, N., Romani, D.: System for automatic detection of clustered microcalcifications in digital mammograms. Int. J. Mod. Phys. C 11 (2000) 901–912Google Scholar
  2. 2.
    Dhawan, A.P., Chitre, Y., Kaiser-Bonasso, C., Moskowitz, M.: Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans.Med. Imag. 15 (1996) 246–259CrossRefGoogle Scholar
  3. 3.
    Dokur, Z., Olmez, T., Yazgan, E.: Classification of MR and CT images using genetic algorithms. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 20 (1998)Google Scholar
  4. 4.
    Anastasio, M., Yoshida, H., Nagel, R., Nishikawa, R.M., Doi, K.: A Genetic Algorithm-Based Method for Optimizing the Performance of a Computer-Aided Diagnosis Scheme for Detection of Clustered Microcalcifications in Mammograms. Med. Phys. 25 (1998) 1559–1566CrossRefGoogle Scholar
  5. 5.
    Yoshida, H., Anastasio, M., Nagel, R., Nishikawa, R.M., Doi, K.: Computer-Aided Diagnosis for Detection of Clustered Microcalcifications in Mammograms: Automated Optimization of Performance Based on Genetic Algorithm. Proceedings of IWCAD 1997, (Elsevier Science B.V., The Netherlands) (1997)Google Scholar
  6. 6.
    Ema, T., Doi, K., Nishikawa, R.M., Jiang, Y., Papaioannou, J.: Image feature analysis and Computer Aided Diagnosis in digital radiography: reduction of false-positive clustered microcalcifications using local edge-gradient analysis. Med. Phys. 22 (1995) 161–169CrossRefGoogle Scholar
  7. 7.
    Cantú-Paz, E.: A survey of Parallel Genetic Algorithms. Report No. 97003, (Univ. of Illinois, Urbana, 1997) (1997)Google Scholar
  8. 8.
    Bevilacqua, A.: A dynamic load balancing method on a heterogeneous cluster of workstations. Informat. 23 (1999) 49–56Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alessandro Bevilacqua
    • 1
    • 2
  • Renato Campanini
    • 2
    • 3
  • Nico Lanconelli
    • 2
    • 3
  1. 1.Department of Electronics, Computer Science and SystemsUniversity of BolognaBolognaItaly
  2. 2.INFN (National Institute for Nuclear Physics)BolognaItaly
  3. 3.Department of PhysicsUniversity of BolognaBolognaItaly

Personalised recommendations