3D volume segmentation of MRA data sets using level sets: image processing and display.
In this article, we use a level set-based segmentation algorithm to extract the vascular tree from magnetic resonance angiography (MRA) data sets. The classification approach depends on initializing the level sets in the 3D volume, and the level sets evolve with time to yield the blood vessels. This work introduces a high-quality initialization for the level set functions, allowing extraction of the blood vessels in 3D and elimination of non-vessel tissues. A comparison between the 2D and 3D segmentation approaches is made. The results are validated using a phantom that simulates the MRA data and show good accuracy.
Farag
Aly A
AA
Computer Vision and Image Processing Laboratory, University of Louisville, Rm 412, Lutz Hall, Louisville, KY 40292, USA. farag@cvip.uofl.edu
Hassan
Hossam
H
Falk
Robert
R
Hushek
Stephen G
SG
D000465:Algorithms; D002560:Cerebrovascular Circulation; D003626:Data Display; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D018810:Magnetic Resonance Angiography; D008433:Mathematics; D019047:Phantoms, Imaging