21 March 2014 A multi-view approach to multi-modal MRI cluster ensembles
Author Affiliations +
Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90341Q (2014); doi: 10.1117/12.2042327
Event: SPIE Medical Imaging, 2014, San Diego, California, United States
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carlos Andrés Méndez, Paul Summers, Gloria Menegaz, "A multi-view approach to multi-modal MRI cluster ensembles", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341Q (21 March 2014); doi: 10.1117/12.2042327; https://doi.org/10.1117/12.2042327
PROCEEDINGS
13 PAGES


SHARE
KEYWORDS
Magnetic resonance imaging

Image segmentation

Diffusion tensor imaging

Tissues

Tumors

Electroluminescent displays

Data modeling

Back to Top