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Proceedings Paper

MRI tissue segmentation incorporating a bias field modulated smoothness prior
Author(s): Enmin Song; Valerie A. Cardenas; Diana Sacrey; Robert Blumenfeld; Michael W. Weiner; Colin Studholme
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Paper Abstract

This paper examines a refinement to probabilistic intensity based tissue segmentation methods, which makes use of knowledge derived from an MRI bias field estimate. Intensity based labeling techniques have employed local smoothness priors to reduce voxel level tissue labeling errors, by making use of the assumption that, within uniform regions of tissue, a voxel should be highly likely to have a similar tissue assignment to its neighbors. Increasing the size of this neighborhood provides more robustness to noise, but reduces the ability to describe small structures. However, when intensity bias due to RF field inhomogeneity is present within the MRI data, local contrast to noise may vary across the image. We therefore propose an approach to refining the labeling by making use of the bias field estimate, to adapt the neighborhood size applied to reduce local labeling errors. We explore the use of a radially symmetric Gaussian weighted neighborhood, and the use of the mean and median of the adapted region probabilities, to refine local probabilistic labeling. The approach is evaluated using the Montreal brainweb MRI simulator as a gold standard providing known gray, white and CSF tissue segmentation. These results show that the method is capable of improving the local tissue labeling in areas most influenced by inhomogeneity. The method appears most promising in its application to regional tissue volume analysis or higher field MRI data where bias field inhomogeneity can be significant.

Paper Details

Date Published: 15 May 2003
PDF: 8 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480852
Show Author Affiliations
Enmin Song, Univ. of California/San Francisco (United States)
Valerie A. Cardenas, Univ. of California/San Francisco (United States)
Diana Sacrey, Univ. of California/San Francisco (United States)
Robert Blumenfeld, Univ. of California/San Francisco (United States)
Michael W. Weiner, Univ. of California/San Francisco (United States)
Colin Studholme, Univ. of California/San Francisco (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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