The gradient vector flow (GVF) deformable model was introduced by Xu and Prince as an effective approach to overcome the limited capture range problem of classical deformable models and their inability to progress into boundary concavities. It has found many important applications in the area of medical image processing. The simple iterative method proposed in the original work on GVF, however, is slow to converge. A new multigrid method is proposed for GVF computation on 2D and 3D images. Experimental results show that the new implementation significantly improves the computational speed by at least an order of magnitude, which facilitates the application of GVF deformable models in processing large medical images.
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering