A 3-D vascular connectivity tracking and vascular network extraction toolkit

Kara, Kerim
Angiography is an invasive procedure since contrast medium is injected into circulatory system of patients and the mostly preferred technique is X-ray angiography. For diagnosis, treatment planning, and risk assessment purposes, interventional radiologists utilize visual inspection to determine connectivity relations between vessels. This situation leads angiography to be more invasive, since it requires additional injection of contrast medium and X-ray dose. This thesis work presents a 3-D vascular connectivity tracking toolkit for automated extraction of vascular networks in 3-D medical images. The proposed method automatically extracts the vascular network connected to a user-defined point in a user-defined direction, and requires no further user interaction. The toolkit prevents additional injection of contrast agent and X-ray dose, saves time for the interventional radiologist. While the algorithm is applicable on all 3-D angiography images, performance of the method is observed on 3-D catheter angiography image of cerebrovascular structures. The algorithm iteratively tracks gravity centers of vascular branches in the user-defined direction, preserving connection to the user-defined point. Curvy branches are tracked even if they have discontinuous portions. Since this tracking method does not depend on lumen diameter and intensity differences, branches with stenoses and branches having large intensity difference can be successfully tracked. Skeletonization and junction detection methods are described, which are used to detect the sub branches, indirectly connected to the point. These methods are capable of handling bifurcations, trifurcations, and junctions having more branches. However, false junctions occurring due to superposition of vessels are not eliminated.


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Citation Formats
K. Kara, “A 3-D vascular connectivity tracking and vascular network extraction toolkit,” M.S. - Master of Science, Middle East Technical University, 2011.