Segmentation of diffusion-weighted brain images using expectation maximization algorithm initialized by hierarchical clustering.

Tissue segmentation based on diffusion-weighted images (DWI) provides complementary information of tissue contrast to the structural MRI for facilitating the tissue segmentation. In the previous literatures, DWI-based brain tissue segmentation was carried out using the parametric images, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC). However, the information of directions of neural fibers was very limited in the parametric images. To fully utilize the directional information, we propose a novel method to perform tissue segmentation directly on the DWI raw image data. Specifically, a hierarchical clustering (HC) technique was first applied on the down-sampled data to initialize the model parameters for each tissue cluster followed by automatic segmentation using the expectation maximization (EM) algorithm. The whole brain DWI raw data of five normal subjects were analyzed. The results demonstrated that HC-EM is effective in multi-tissue classification on DWI raw data.