Robust MRI Brain Tissue Parameter Estimation
This work addresses the problem of the tissue type parameter
estimation in brain MRI in the presence of partial volume effects.
Automatic MRI brain tissue classification is hampered by partial volume
effects that are caused by the finite resolution of the acquisition
process. Due to this effect intensity distributions in brain MRI cannot
be well modeled by a simple mixture of Gaussians and therefore more
complex models have been developed. Unfortunately, these models do not
seem to be robust enough for clinical conditions, as the quality of the
tissue classification decreases rapidly with the image quality. Also,
the application of these methods for pathological images with unmodeled
intensities (e.g. MS plaques, tumors, etc.) remains uncertain.
We have developed a new robust method for brain tissue characterization is
presented, treating the partial volume affected voxels as outliers of
the pure tissue distributions. The proposed method estimates the tissue
characteristics from a reduced set of intensities belonging to a
particular pure tissue class. This reduced set is selected by using a
trimming procedure based on local gradient information and
distributional data. This feature makes the method highly tolerant of a
large amount of unexpected intensities without degrading its
performance. The proposed method has been evaluated using both
synthetic and real MR data and compared with state-of-the-art methods
showing the best results in the comparative
Details can be found in:
Manjón j.V., Tohka J., García-Martí G., Carbonell-Caballero J., Lull J.J., Martí-Bonmatí L., Robles M. Robust MRI Brain Tissue Parameter Estimation by Multistage Outlier Rejection. Magnetic Resonance in Medicine, 59:866-873, 2008.
Demo images and the source code of the method can be found here.