Case study: reconstruction, visualization and quantification of neuronal fiber pathways
Proceedings of the conference on Visualization '01
Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces
IEEE Transactions on Visualization and Computer Graphics
Stability-based validation of clustering solutions
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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We present a slicing-based coherence measure for clusters of DTI integral curves. For a given cluster, we probe samples from the cluster by slicing it with a plane at regularly spaced locations parametrized by curve arc lengths. Then we compute a stability measure based on the spatial relations between the projections of the curve points in individual slices and their change across the slices. We demonstrate its use in refining agglomerative hierarchical clustering results of DTI curves that correspond to neural pathways. Expert evaluation shows that refinement based on our measure can lead to improvement of clustering that is not possible directly by using standard methods.