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This article 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. In the present work 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 In medical imaging a single voxel may be composed of a mixture of tissue types due to the finite spatial resolution of imaging devices. This phenomenon is known as the partial volume effect (PVE) and it is mainly observable at tissue type boundaries. PVE is an important factor in the study of small brain structures or highly convoluted brain regions such as those within the cerebral cortex. In fact, ignoring this effect can lead to volume measurement errors in the range of 20 -60% (1). In addition to complicating MRI-based morphometry, PVE causes severe difficulties in modeling intensities of different tissue types. For example, modeling the histogram by a simple mixture of Gaussians (MoG) does not work properly because a large part of the voxels is affected by PVE. These complications have motivated the development of more realistic intensity models and parameter estimation schemes taking the PVE into account.Most of the PVE modeling methods model both pure tissue and mixed voxels using different intensity distributions. One of the first attempts to statistical partial volume (PV) modeling was proposed by Santago and Gage (2). They modeled pure tissues assuming equal variance Gaussian distributions (motivated by the MR physics) while PV voxel intensities followed a different distribution derived based on the parameters of the pure distributions. Laidlaw et al. (3) used a similar model but incorporated a more complex method that took into account neighborhood voxels. Ruan et al. (4) concluded that PV voxels can be generally modeled by using Gaussian distributions. However, this approach has the drawback of not being able to compute PV fractions, since there is no explici...

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Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

patients with different neurological outcomes. Methods: We studied 49 patients who had suffered a severe TBI and 10 healthy control subjects using 18F-FDG-PET. The patients were divided into three groups: the MCS&VS group (n=17), which included patients who were in a vegetative or a minimally conscious state; the In-PTA group (n=12), which included patients in post-traumatic amnesia (PTA); and the Out-PTA group (n=20), which included patients who had recovered from PTA. SPM5 software was used to determine the metabolic differences between the groups. FDG-PET images were normalized and four regions of interest were generated around the thalamus, precuneus and the frontal and temporal lobes. The groups were parameterized using the Student's T-test. Principal component analysis was used to obtain an intensityestimated-value per subject to correlate the function between the structures.Results: Differences in glucose metabolism in all structures were related to the neurological outcome, and the most severe patients showed the most severe hypometabolism. We also found a significant correlation between the cortico-thalamocortical metabolism in all groups. Conclusions: Voxel-based analysis suggests a functional correlation between these four areas and decreased metabolism was associated with less favorable outcome. Higher levels of activation of the corticocortical connections appear to be related to better neurological conditions. Differences in the thalamo-cortical correlations between patients and controls may be related to traumatic dysfunction due to focal or diffuse lesions.3