Yuanzhi Cheng, Xin Hu, Yadong Wang, Jinke Wang, Shinichi Tamura
Journal of Electronic Imaging, Vol. 23, Issue 01, 013007, (January 2014) https://doi.org/10.1117/1.JEI.23.1.013007
TOPICS: Image segmentation, Liver, Arteries, 3D image processing, Computed tomography, Image processing, Image filtering, Detection and tracking algorithms, Image processing algorithms and systems, Chest
Vessel centerline detection is very important in many medical applications. In the noise and low-contrast regions, most existing methods may only produce an incomplete and disconnected extraction of the vessel centerline if no user guidance is provided. A robust and automatic method is described for extraction of the vessel centerline. First, we perform small vessel enhancement by processing with a set of line detection filters, corresponding to the 13 orientations; for each voxel, the highest filter response is kept and added to the image. Second, we extract vessel centerline segment candidates by a thinning algorithm. Finally, a global optimization algorithm is employed for grouping and selecting vessel centerline segments. We validate the proposed method quantitatively on a number of synthetic data sets, the liver artery and lung vessel. Comparisons are made with two state-of-the-art vessel centerline extraction methods and manual extraction. The experiments show that our method is more accurate and robust that these state-of-the-art methods and is, therefore, more suited for automatic vessel centerline extraction.