A United Image Force for Deformable Models and Direct Transforming Geometric Active Contorus to Snakes by Level Sets

TL;DR

This paper introduces a fusion of electrostatic and heat diffusion image forces to improve segmentation accuracy and speed, and demonstrates that Geometric Active Contours can be derived from Snakes models using level sets.

Contribution

It proposes a novel fusion scheme of image forces for precise, fast object boundary extraction and establishes the mathematical relationship between Snakes and GAC models.

Findings

Fusion of electrostatic and heat diffusion forces enhances segmentation performance.

GAC can be directly derived from Snakes model with similar functions.

Level sets limit GAC's rotation ability in some cases.

Abstract

A uniform distribution of the image force field around the object fasts the convergence speed of the segmentation process. However, to achieve this aim, it causes the force constructed from the heat diffusion model unable to indicate the object boundaries accurately. The image force based on electrostatic field model can perform an exact shape recovery. First, this study introduces a fusion scheme of these two image forces, which is capable of extracting the object boundary with high precision and fast speed. Until now, there is no satisfied analysis about the relationship between Snakes and Geometric Active Contours (GAC). The second contribution of this study addresses that the GAC model can be deduced directly from Snakes model. It proves that each term in GAC and Snakes is correspondent and has similar function. However, the two models are expressed using different mathematics. Further, since losing the ability of rotating the contour, adoption of level sets can limits the usage of GAC in some circumstances.