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In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it(More)
Mutual information has been successfully used as an effective similarity measure for multimodal image registration. However, a drawback of the standard mutual information-based computation is that the joint histogram is only calculated from the correspondence between individual voxels in the two images. In this paper, the normalized mutual information(More)
BACKGROUND AND PURPOSE Early prediction of motor outcome is of interest in stroke management. We aimed to determine whether lesion location at DTT is predictive of motor outcome after acute stroke and whether this information improves the predictive accuracy of the clinical scores. MATERIALS AND METHODS We evaluated 60 consecutive patients within 12 hours(More)
BACKGROUND AND PURPOSE The quantification and clinical significance of WD in CSTs following supratentorial stroke are not well understood. We evaluated the anisotropy by using DTI and signal-intensity changes on conventional MR imaging in the CST to determine whether these findings are correlated with limb motor deficit in patients with MCA ischemic stroke.(More)
In this paper we propose a two-step mutual information-based algorithm for medical image segmentation. In the first step, the image is structured into homogeneous regions, by maximizing the mutual information gain of the channel going from the histogram bins to the regions of the partitioned image. In the second step, the intensity bins of the histogram are(More)
Multimodal visualization aims at fusing different data sets so that the resulting combination provides more information and understanding to the user. To achieve this aim, we propose a new information-theoretic approach that automatically selects the most informative voxels from two volume data sets. Our fusion criteria are based on the information channel(More)
Two new similarity measures for rigid image registration, based on the normalization of Jensen's difference applied to Rényi and Tsallis-Havrda-Charvát entropies, are introduced. One measure is normalized by the first term of Jensen's difference, which in our proposal coincides with the marginal entropy, and the other by the joint entropy. These measures(More)