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Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis

  • David Rey
  • Gérard Subsol
  • Hervé Delingette
  • Nicholas Ayache
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1613)

Abstract

Physicians often perform diagnoses based on the evolution of lesions, tumors or anatomical structures through time. The objective of this paper is to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between successive temporal images. In studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this paper we apply this approach to the automatic detection and segmentation of multiple sclerosis lesions in time series of MRI images of the brain.

Keywords

Multiple Sclerosis Automatic Detection Electronic Version Multiple Sclerosis Lesion Jacobian Operator 
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

  • David Rey
    • 1
  • Gérard Subsol
    • 1
  • Hervé Delingette
    • 1
  • Nicholas Ayache
    • 1
  1. 1.EPIDAURE projectINRIA Sophia AntipolisFrance

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