Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/29575
Title: Tracking of coronary arteries in angiogram sequence by structural matching of junctions
Authors: WANG YUMEI
Keywords: computer vision, coronary arteries, junction tracking, x-ray angiograms, image processing, junction graph
Issue Date: 15-Aug-2011
Source: WANG YUMEI (2011-08-15). Tracking of coronary arteries in angiogram sequence by structural matching of junctions. ScholarBank@NUS Repository.
Abstract: Coronary artery disease is the most common form of heart disease and is a leading cause of death worldwide. The standard diagnostic tool for coronary artery disease is x-ray angiography. Angiogram sequences are routinely captured for diagnosis and treatment of coronary artery diseases. As angiograms are 2D images, it is useful to reconstruct the 4D (3D-plus-time) structure of coronary arteries to better assist the cardiologist in diagnosis and treatment. To achieve this goal, it is necessary to track the coronary arteries in the angiogram sequences. However, it is a very difficult and challenging task because the arteries become visible and later invisible as contrast agent flows through them. Moreover, they change shape over time due to the motion of the heart and the chest. To address these issues, this thesis proposes a novel method that automatically tracks major junctions of the blood vessels. It automatically extracts the blood vessel branches and junctions. A junction is characterized by a descriptor vector of the angles and widths of every branch of the junction. Junctions are tracked by matching their descriptors in successive angiogram frames. Also, an augmented graph is constructed to represent the connectivity patterns of the junctions in each frame. Then, the augmented graphs are used to disambiguate between possible candidate matches and estimate the locations of missing junctions. The algorithm is applied to 6 angiogram sequences that are taken for one patient in different view points. Test results show that the algorithm can correctly track most of the junctions most of the time.
URI: http://scholarbank.nus.edu.sg/handle/10635/29575
Appears in Collections:Master's Theses (Open)

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