In the context of cardiac applications, the primary goal of coronary vessel analysis often consists in supporting the diagnosis
of vessel wall anomalies, such as coronary plaque and stenosis. Therefore, a fast and robust segmentation of the coronary
tree is a very important but challenging task.
We propose a new approach for coronary artery segmentation. Our method is based on an earlier proposed progressive
region growing. A new growth front monitoring technique controls the segmentation and corrects local leakage by retrospective
detection and removal of leakage artifacts. While progressively reducing the region growing threshold for the
whole image, the growing process is locally analyzed using criteria based on the assumption of tubular, gradually narrowing
vessels. If a voxel volume limit or a certain shape constraint is exceeded, the growing process is interrupted. Voxels
affected by a failed segmentation are detected and deleted from the result. To avoid further processing at these positions, a
large neighborhood is blocked for growing.
Compared to a global region growing without local correction, our new local growth control and the adapted correction
can deal with contrast decrease even in very small coronary arteries. Furthermore, our algorithm can efficiently handle
noise artifacts and partial volume effects near the myocardium. The enhanced segmentation of more distal vessel parts was
tested on 150 CT datasets. Furthermore, a comparison between the pure progressive region growing and our new approach