Image Segmentation Using GAC Model Combining with GVF and Balloon Force

Abstract

The geodesic active contour (GAC) model has been widely used due to the high precision of edge detection and the continuity of boundaries. However, compared to the gradient vector flow (GVF) field, it usually has poor performance in the object boundaries with concave shape. In this paper, the improvements that integrate the good performance of the GAC model and the GVF field are described in detail and the GAC_GVF&B model combining the GAC model with the GVF field and a balloon force is proposed. The experimental results demonstrate that the proposed method is insensitive to the positions of initial curves and can segment complex-shape or multiobjects images correctly with higher convergence.

A Level Set-Based Model for Image Segmentation under Geometric Constraints and Data Approximation

“…We can see that our method gives the best results, although it remains sensitive to noise. It is, of course, possible to improve the modeling in the case of applications to image segmentation of noisy images by adding, for instance, the gradient vector flow in the modeling, as stressed in [30]. Left: the MPA is perfectly segmented on the initial image (we have considered 2 points as geometric conditions).…”