Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.

Deformable models are used extensively in image processing, computer vision, and medical imaging applications, particularly to delineate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This chapter presents an external force for deformable models, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional deformable model external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding deformable model is formulated directly from a dynamic force equation rather than a energy minimization formulation. Using several two-dimensional examples and two three-dimensional examples, we show that GVF has a large capture range and is able to move deformable models into ...

Image segmentation is one of the substantial techniques in the field of image processing. It is excessively used in the field of medicine provides visual means for identification, inspection and tracking of diseases for surgical planning and simulation. Active contours or snakes are used extensively for image segmentation and processing applications, particularly to locate object boundaries. Active contours are regarded as promising and vigorously researched model-based approach to computer assisted medical image analysis. However, its utility is limited due to poor convergence of concavities and small capture range. Many subsequent models have been introduced in order to overcome these problems. This paper reviews the traditional model, the Gradient vector flow (GVF) model and the balloon model for different images and proposes a model which can provide the most accurate segmentation.