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Automated Segmentation of MS Lesions from Multi-channel MR Images

  • Koen Van Leemput
  • Frederik Maes
  • Fernando Bello
  • Dirk Vandermeulen
  • Alan Colchester
  • Paul Suetens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1679)

Abstract

Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.

Keywords

Multiple Sclerosis Human Expert Manual Segmentation Multiple Sclerosis Lesion Spatial Correspondence 
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-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Koen Van Leemput
    • 1
  • Frederik Maes
    • 1
  • Fernando Bello
    • 2
  • Dirk Vandermeulen
    • 1
  • Alan Colchester
    • 2
  • Paul Suetens
    • 1
  1. 1.Medical Image Computing, Radiology-ESATKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Neurosciences Medical Image Analysis Group, Electronic Engineering LaboratoryUniversity of Kent of CanterburyCanterbury KentUK

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