MultiView Cluster Ensembles for MultiModal MRI Segmentation

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 aiming at heterogeneity assessment in tumoral lesions. 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 unsupervised base segmentations into an unified partition of the voxel-based data. The methodology is demonstrated with simulated data 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.