A Pattern Recognition Approach to Zonal Segmentation of the Prostate on MRI

  • Geert Litjens
  • Oscar Debats
  • Wendy van de Ven
  • Nico Karssemeijer
  • Henkjan Huisman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)


Zonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that can differentiate between the two zones: anatomical, intensity and texture. It is evaluated against a multi-parametric multi-atlas based method using 48 multi-parametric MRI studies. Three observers are used to assess inter-observer variability and we compare our results against the state of the art from literature. Results show a mean Dice coefficient of 0.89 ± 0.03 for the central gland and 0.75 ± 0.07 for the peripheral zone, compared to 0.87 ± 0.04 and 0.76 ± 0.06 in literature. Summarizing, a pattern recognition approach incorporating anatomy, intensity and texture has been shown to give good results in zonal segmentation of the prostate.


prostate MRI segmentation voxel classification atlas 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geert Litjens
    • 1
  • Oscar Debats
    • 1
  • Wendy van de Ven
    • 1
  • Nico Karssemeijer
    • 1
  • Henkjan Huisman
    • 1
  1. 1.Radboud University Nijmegen Medical CentreNijmegenThe Netherlands

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