Vessel boundary extraction based on a global and local deformable physical model with variable stiffness.
Reliable and efficient vessel cross-sectional boundary extraction is very important for many medical magnetic resonance (MR) image studies. General purpose edge detection algorithms often fail for medical MR images processing due to fuzzy boundaries, inconsistent image contrast, missing edge features, and the complicated background of MR images. In this regard, we present a vessel cross-sectional boundary extraction algorithm based on a global and local deformable model with variable stiffness. With the global model, the algorithm can handle relatively large vessel position shifts and size changes. The local deformation with variable stiffness parameters enable the model to stay right on edge points at the location where edge features are strong and at the same time, fit a smooth contour at the location where edge features are missing. Directional gradient information is used to help the model to pick correct edge segments. The algorithm was used to process MR cine phase-contrast images of the aorta from 20 volunteers (over 500 images) with excellent results.
Hu
Y L
YL
Allegheny University of the Health Sciences, Pittsburgh, PA, USA. hu@pgh.auhs.edu
Rogers
W J
WJ
Coast
D A
DA
Kramer
C M
CM
Reichek
N
N
D000465:Algorithms; D001011:Aorta / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted