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Proceedings Paper

A multi-view approach to multi-modal MRI cluster ensembles
Author(s): Carlos Andrés Méndez; Paul Summers; Gloria Menegaz
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Paper Abstract

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.

Paper Details

Date Published: 21 March 2014
PDF: 13 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341Q (21 March 2014); doi: 10.1117/12.2042327
Show Author Affiliations
Carlos Andrés Méndez, Univ. degli Studi di Verona (Italy)
Paul Summers, European Institute of Oncology (Italy)
Gloria Menegaz, Univ. degli Studi di Verona (Italy)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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