3D volume extraction of cerebrovascular structure on brain magnetic resonance angiography data sets

The use of computers in facilitating their processing and analysis has become necessary with the increaseing size and number of medical images. In particular, computer algorithms for the delineation of anatomical structures and other regions of interest, which are called image segmentation, play a vital role in numerous biomedical imaging applications such as the quantification of tissue volumes, diagnosis, localization of pathology, study of anatomical structure, treatment planning, and computer-integrated surgery. In this paper, a 3D volume extraction algorithm was proposed for segmentation of cerebrovascular structure on brain MRA data sets. By using a priori knowledge of cerebrovascular structure, multiple seed voxels were automatically identified on the initially thresholded image. In the consideration of the preserved voxel connectivity—which is defined as 6-connectivity with joint faces, 18-connectivity with joint edges, and 26-connectivity with joint corners— the seed voxels were grown within the cerebrovascular structure area throughout 3D volume extraction process. This algorithm provided better segmentation results than other segmentation methods such as manual, and histogram thresholding approach. This 3D volume extraction algorithm is also applicable to segment the tree-like organ structures such as renal artery, coronary artery, and airway tree from the medical imaging modalities.