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21 March 2014 A multi-view approach to multi-modal MRI cluster ensembles
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It has been shown that the combination of multi-modal MRI images improve the discrimination of diseased tissue. However the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, there is an inherent difference in information domains, dimensionality and scales. This work proposes a multiview consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each one of the MRI modalities and makes use of the concepts behind cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data. The methodology is specially designed for combining DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information.
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Carlos Andrés Méndez, Paul Summers, and Gloria Menegaz "A multi-view approach to multi-modal MRI cluster ensembles", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341Q (21 March 2014);

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