Thalamic Nuclei Clustering on High Angular Resolution Diffusion Images.

Thalamic nuclei can be distinguished by their characteristic fiber orientations, which influence the diffusion. Fiber orientations are relatively aligned within a nucleus due to the fact that the cerebrocortical striations within a nucleus all target the same region of cortex. The number of thalamic nuclei reported with histological methods varies with the method employed, although most cyto/myeloarchitec stains identify 14 major nuclei. We present a new approach for thalamic nuclei segmentation on High Angular Diffusion Resolution Images (HARDI), performed with a constrained k-means clustering. As described by John D.Carew[1], it is possible to classify HARDI data based on the shape of the diffusion, thanks to the complex information coming from them. Mette R. Wiegell [2] proposed a thalamic nuclei clustering with k- means on diffusion tensor images, using a combination of a voxel distance and a diffusion tensor distance. In the same way, we use the k-mean algorithm with a weighted sum of two distances to cluster the thalamic nuclei on HARDI data