NDLI: A multi-view approach to consensus clustering in multi-modal MRI

Content Provider	IEEE Xplore Digital Library
Author	Mendez, C.A. Menegaz, G. Summers, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Radiol., Eur. Inst. of Oncology, Milan, Italy (Summers, P.) \|\| Dept. of Comput. Sci., Univ. of Verona, Verona, Italy (Mendez, C.A.; Menegaz, G.)
Abstract	It has been shown that the combination of multi-modal MRI images can improve the discrimination of diseased tissue. The fusion of dissimilar imaging data for classification and segmentation purposes however, is not a trivial task, as there is an inherent difference in information domains, dimensionality and scales. This work proposes a multi-view 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 MRI modality and makes use of cluster ensembles to combine a set of un-supervised base segmentations into an unified partition of the voxel-based data. The methodology is demonstrated in application to 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.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	6627
Ending Page	6631
File Size	308328
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854882
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Measurement Manifolds Kernel Diffusion tensor imaging DCE-MRI Cluster Ensembles Clustering Classification Segmentation DTI-MR
Content Type	Text
Resource Type	Article

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