Improved algorithm for gradient vector flow based active contour model using global and local information

Abstract

Active contour models are used to extract object boundary from digital image, but there is poor convergence for the targets with deep concavities. We proposed an improved approach based on existing gradient vector flow methods. Main contributions of this paper are a new algorithm to determine the false part of active contour with higher accuracy from the global force of gradient vector flow and a new algorithm to update the external force field together with the local information of magnetostatic force. Our method has a semidynamic external force field, which is adjusted only when the false active contour exists. Thus, active contours have more chances to approximate the complex boundary, while the computational cost is limited effectively. The new algorithm is tested on irregular shapes and then on real images such as MRI and ultrasound medical data. Experimental results illustrate the efficiency of our method, and the computational complexity is also analyzed.

Figure 1

Extracted boundary with deep concavity using GVF based ACM. (a) Object and initial active contour. (b) Result after 150 iterations.

Figure 2

External force field of GVF and stopped active contour.

Figure 3

The locally enlarged regions of external force field.

Figure 4

Judgements of false parts in active contour by [22] and our new method.

Figure 5

Magnetic force field of the false active contour.

Figure 6

External force field at the entrance of deep concavity.

Figure 7

Results on object with one deep concavity.

Figure 8

Results on object with multiple deep concavities.

Figure 9

Experiments on real images with complex objects.

Figure 10

Experiments on MRI and ultrasound images.