A multi-view approach to multi-modal MRI cluster ensembles

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 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 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

A multi-view approach to multi-modal MRI Cluster Ensembles for heterogeneity assessment of tumoral lesions

Generally, a discriminating a strategy based on a unique imaging modality is unable to appropriately differentiate normal from cancerous tissue, thus suggesting the use of a multi-modal view of the tissue for clinical assessment. Moreover, since many tumors, such as human glioma, are characterized by heterogeneous histopathology or have locally evolved to different stages of tumor progression, it is important to obtain a complete coverage of the lesion and its composing subregions. Recent work has proved that the combined multi-modal information yields improved discrimination of diseased tissue. However, its exploitation is still in its infancy since the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, as there is an inherent difference in information domains, dimensionality and scales