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Abstract #1600

A Multi-Resolution Watershed-Based Approach for the Segmentation of Diffusion Tensor Images

Paulo Rodrigues1, Andrei Jalba2, Pierre Fillard3, Anna Vilanova1, Bart M. ter Haar Romeny1

1Biomedical Image Analysis, Eindhoven University of Technology, Eindhoven, Noord Brabant, Netherlands; 2Department of Computer Science, Eindhoven University of Technology, Eindhoven, Noord Brabant, Netherlands; 3CEA, Paris, France


The investigation of Diffusion Tensor Imaging (DTI) data is of complex and exploratory nature: tensors, fiber tracts, bundles. This quickly leads to clutter problems in visualization as well as in analysis. We propose a new framework for the multi-resolution analysis of DTI. Based on fast and greedy watersheds operating on a multi-scale representation of a DTI image, a hierarchical depiction of such image is determined conveying a global-to-local view of the fibrous structure of the analysed tissue. We present a simple and interactive segmentation tool, where different bundles can be segmented at different resolutions.

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