Improved tissue segmentation by including an MR acquisition model

Authors:
Dirk H. J. Poot;Marleen De Bruijne;Meike W. Vernooij;M. Arfan Ikram;Wiro J. Niessen
Affiliations:
BIGR, Erasmus Medical Center, Rotterdam, The Netherlands;BIGR, Erasmus Medical Center, Rotterdam, The Netherlands and Department of Computer Science, University of Copenhagen, Denmark;Department of Epidemiology & Department of Radiology, Erasmus MC, Rotterdam;BIGR and Department of Epidemiology & Department of Radiology, Erasmus Medical Center, Rotterdam, The Netherlands;BIGR, Erasmus Medical Center, Rotterdam, The Netherlands and Delft University of Technology, Delft, The Netherlands
Venue:
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Year:
2011

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Abstract

This paper presents a new MR tissue segmentation method. In contrast to most previous methods the image formation model includes the point spread function of the image acquisition. This allows optimal combination of images acquired with different contrast weighting, resolutions, and orientations. The proposed method computes the regularized maximum likelihood partial volume segmentation from the images. The quality the resulting segmentation is studied with a simulation experiment and by testing the reproducibility of the segmentation on repeated brain MRI scans. Our results demonstrate improved segmentation quality, especially at tissue edges.