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Original Research
Improved cerebellar tissue classification on magnetic resonance images of brain
Article first published online: 22 APR 2009
DOI: 10.1002/jmri.21734
Copyright © 2009 Wiley-Liss, Inc.
Additional Information
How to Cite
Datta, S., Tao, G., He, R., Wolinsky, J. S. and Narayana, P. A. (2009), Improved cerebellar tissue classification on magnetic resonance images of brain. J. Magn. Reson. Imaging, 29: 1035–1042. doi: 10.1002/jmri.21734
Publication History
- Issue published online: 22 APR 2009
- Article first published online: 22 APR 2009
- Manuscript Accepted: 9 JAN 2009
- Manuscript Received: 31 OCT 2008
Funded by
- National Institutes of Health. Grant Number: EB002095
- Abstract
- Article
- References
- Cited By
Keywords:
- magnetic resonance imaging;
- segmentation;
- brain;
- cerebellum
Abstract
Purpose
To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.
Materials and Methods
Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices.
Results
The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (±2.34) and 93.04 (±2.41), demonstrating excellent performance of the proposed methodology.
Conclusion
The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object. J. Magn. Reson. Imaging 2009;29:1035–1042. © 2009 Wiley-Liss, Inc.