Cerebrovascular segmentation of TOF-MRA based on seed point detection and multiple-feature fusion

Abstract

The accurate extraction of cerebrovascular structures from time-of-flight (TOF) data is important for diagnosis of cerebrovascular diseases and planning and navigation of neurosurgery. In this study, we proposed a cerebrovascular segmentation method based on automatic seed point detection and vascular multiple-feature fusion. First, the brain mask in the T1-MR image is detected to enable the extraction of the TOF brain structure by simultaneously acquiring the TOF image and its corresponding T1-MRI. Second, local maximum points are detected on three maximum-intensity projections of TOF-MRA data and then be traced back in three-dimensional space to detect seed points for the initialization of vascular segmentation. Third, the TOF-MRA image and its corresponding vesselness image are fused to enhance vascular features on the basis of fuzzy inference for the extraction of whole cerebrovascular structures, particularly miniscule cerebral vessels. Finally, detected seed points and multiple-feature fused enhanced images are provided to the procedure of region growing, and cerebrovascular structures are segmented. Experimental results show that compared with traditional methods, the proposed method has higher accuracy for vascular segmentation and can avoid over- and under-segmentations. The proposed cerebrovascular segmentation method is not only effective but also accurate. Therefore, it has potential clinical applications.