Multiview cluster ensembles for multimodal MRI segmentation

C. Andrés Méndez, Paul Summers, Gloria Menegaz

Research output: Contribution to journalArticlepeer-review


It has been shown that the combination of multimodal magnetic resonance imaging (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 proposed a multiview consensus clustering methodology for the integration of multimodal MR images into a unified segmentation aiming at heterogeneity assessment in tumoral lesions. Using a variety of metrics and distance functions this multiview imaging approach calculated multiple vectorial dissimilarity-spaces for each MRI modality and it maked use of cluster ensembles to combine a set of unsupervised base segmentations into an unified partition of the voxel-based data. The methodology was demonstrated with simulated data in application to dynamic contrast enhanced MRI and diffusion tensor imaging MR, for which a manifold learning step was implemented in order to account for the geometric constrains of the high dimensional diffusion information.

Original languageEnglish
Pages (from-to)56-67
Number of pages12
JournalInternational Journal of Imaging Systems and Technology
Issue number1
Publication statusPublished - Mar 1 2015


  • clustering
  • diffusion MRI
  • multimodal MRI
  • non-supervised classification
  • segmentation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Computer Vision and Pattern Recognition
  • Software


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