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Brain Tissue Classification with Automated Generation of Training Data Improved by Deformable Registration

  • Daniel Schwarz
  • Tomas Kasparek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Methods of tissue classification in MRI brain images play a significant role in computational neuroanatomy, particularly in automated ROI-based volumetry. A well-known and very simple k-NN classifier is used here without the need for user input during the training process. The classifier is trained with the use of tissue probability maps which are available in selected digital atlases of brain. The influence of misalignement between images and the tissue probability maps on the classifier’s efficiency is studied in this paper. Deformable registration is used here to align the images and maps. The classifier’s efficiency is tested in an experiment with data obtained from standard Simulated Brain Database.

Keywords

image analysis image registration MRI computational neuroanatomy brain tissue classification atlas-based segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Schwarz
    • 1
  • Tomas Kasparek
    • 2
  1. 1.Institute of Biostatistics and Analyses, Masaryk University, Kamenice 3, 625 00 BrnoCzech Republic
  2. 2.Clinic of Psychiatry, Faculty Hospital Brno, Jihlavska 20, 625 00 BrnoCzech Republic

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