Automatic Segmentation of Fibroglandular Tissue

  • Christina Olsén
  • Aamir Mukhdoomi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

In this paper, a segmentation algorithm is proposed which extracts regions depicting fibroglandular tissue in a mammogram. There has been an increasing need for such algorithms due to several reasons, the majority of which are related to the development of techniques for Computer Aided Diagnosis of breast cancer from mammograms. The proposed algorithm consists of a major phase and a post-processing phase. The purpose of the major phase is to calculate the threshold value that yields a segmentation of glandular tissue which is achievable by thresholding. The method by which we calculate this threshold value is based on the principle of minimizing the cross-entropy between two images. The resulting segmentation is then post-processed to remove artifacts such as noise and other unwanted regions. The algorithm has been implemented and evaluated with promising results. In particular, its performance seems to match that of medical professionals specialized in mammography.

Keywords

Input Image Segmentation Algorithm Automatic Segmentation Glandular Tissue Segmented Region 
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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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Christina Olsén
    • 1
  • Aamir Mukhdoomi
    • 1
  1. 1.Department of Computing Science, Umeå University, SE-901 87 UmeåSweden

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