Brain Tissue Segmentation for Diffusion Tensor Imaging (DTI) Data using Multi-Tensor Estimation

Seiji Kumazawa¹, Takashi Yoshiura¹, Hiroshi Honda¹, Fukai Toyofuku¹

To study the cortical/subcortical diffusivity in neurological diseases, brain tissue segmentation methods based on DTI data have been proposed. However, in these methods, fiber crossing regions might be misclassified into gray matter region because of denoting low FA values. We present a new brain tissue segmentation method based on DTI data. The features of our method include the conducting of the segmentation in DTI space without any registration, and using multi-tensor estimation for fiber crossing regions. The results of the digital phantom experiment and human DTI data demonstrate that our method was able to perform a reasonable segmentation for brain tissue on DTI data.