A multi-resolution watershed-based approach for the segmentation of diffusion tensor images

The analysis and visualisation of Di usion Tensor Images (DTI) is still a challenge since it is multi-valued and exploratory in nature: tensors, ber tracts, bundles. This quickly leads to clutter problems in visualisation but also in analysis. In this paper, a new framework for the multi-resolution analysis of DTI is proposed. Based on fast and greedy watersheds operating on a multi-scale representation of a DTI image, a hierarchical depiction of a DTI image is determined conveying a global-to-local view of the brous structure of the analysed tissue. The multi-resolution watershed transform provides a coarse to ne partitioning of the data based on the (in)homogeneity of the gradient eld. With a transversal cross scale linking of the basins (regions), a hierarchical representation is established. This framework besides providing a novel hierarchical way to analyse DTI data, allows a simple and interactive segmentation tool where different bundles can be segmented at di erent resolutions. We present preliminary experimental results supporting the validity of the proposed method