A three-dimensional (3-D) segmentation technique is proposed, which separates the 3-D reconstructed volume data into several sub-regions having similar characteristics. This method is based on iteratively merging and splitting the sub-regions in the space of diffusion tensor fields. The superquadrics have been used as a figure of merit to measure the similarity between the neighboring voxels. By using the segmented 3-D in the magnetic resonance angiography data, we can efficiently visualize the vascular structure without any prior information as well as improve the medical diagnosis and therapy.