A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images

  • Shane M. Butler
  • Geoffrey I. Webb
  • Rob A. Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2903)


X-ray mammography is the current clinical method for screening for breast cancer, and like any technique, has its limitations. Several groups have reported differences in the X-ray scattering patterns of normal and tumour tissue from the breast. This gives rise to the hope that X-ray scatter analysis techniques may lead to a more accurate and cost effective method of diagnosing beast cancer which lends itself to automation. This is a particularly challenging exercise due to the inherent complexity of the information content in X-ray scatter patterns from complex hetrogenous tissue samples. We use a simple naïve Bayes classier as our classification system. High-level features are extracted from the low-level pixel data. This paper reports some preliminary results in the ongoing development of this classification method that can distinguish between the diffraction patterns of normal and cancerous tissue, with particular emphasis on the invention of features for classification.


Knowledge discovery and data mining Applications 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Shane M. Butler
    • 1
  • Geoffrey I. Webb
    • 1
  • Rob A. Lewis
    • 2
  1. 1.School of Computer Science and Software EngineeringMonash UniversityAustralia
  2. 2.School of Physics and Materials EngineeringMonash UniversityAustralia

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