Breast Density Segmentation: A Comparison of Clustering and Region Based Techniques

  • A. Torrent
  • A. Bardera
  • A. Oliver
  • J. Freixenet
  • I. Boada
  • M. Feixes
  • R. Martí
  • X. Lladó
  • J. Pont
  • E. Pérez
  • S. Pedraza
  • J. Martí
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5116)

Abstract

This paper presents a comparison of two clustering based algorithms and one region based algorithm for segmenting fatty and dense tissue in mammographic images. This is a crucial step in order to obtain a quantitative measure of the density of the breast. The first algorithm is a multiple thresholding algorithm based on the excess entropy, the second one is based on the Fuzzy C-Means clustering algorithm, and the third one is based on a statistical analysis of the breast. The performance of the algorithms is exhaustively evaluated using a database of full-field digital mammograms containing 150 CC and 150 MLO images and ROC analysis (ground-truth provided by an expert). Results demonstrate that the use of region information is useful to obtain homogeneous region segmentation, although clustering algorithms obtained better sensitivity.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • A. Torrent
    • 1
  • A. Bardera
    • 2
  • A. Oliver
    • 1
  • J. Freixenet
    • 1
  • I. Boada
    • 2
  • M. Feixes
    • 2
  • R. Martí
    • 1
  • X. Lladó
    • 1
  • J. Pont
    • 3
  • E. Pérez
    • 3
  • S. Pedraza
    • 3
  • J. Martí
    • 1
  1. 1.Computer Vision and Robotics GroupUniversity of GironaCataloniaSpain
  2. 2.Graphics & Imaging LaboratoryUniversity of GironaCataloniaSpain
  3. 3.Department of RadiologyHospital Josep Trueta of GironaCataloniaSpain

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