Gradient Vector Flow External Force Research Articles

Image segmentation using active contours with image structure adaptive gradient vector flow external force

IntroductionGradient vector flow (GVF) has been proven as an effective external force for active contours. However, its smoothness constraint does not take the image structure into account, such that the GVF diffusion is isotropic and cannot preserve weak edges well.MethodsIn this article, an image structure adaptive gradient vector flow (ISAGVF) external force is proposed for active contours. In the proposed ISAGVF model, the smoothness constraint is first reformulated in matrix form, and then the image structure tensor is incorporated. As the structure tensor characterizes the image structure well, the proposed ISAGVF model can be adaptive to image structure, and the ISAGVF snake performs well on weak edge preservation and deep concavity convergence while possessing some other desirable properties of the GVF snake, such as enlarged capture range and insensitivity to initialization.ResultsExperiments on synthetic and real images manifest these properties of the ISAGVF snake.

Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force.

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.

Active contours with neighborhood-extending and noise-smoothing gradient vector flow external force

We propose a novel external force for active contours, which we call neighborhood-extending and noise-smoothing gradient vector flow (NNGVF). The proposed NNGVF snake expresses the gradient vector flow (GVF) as a convolution with a neighborhood-extending Laplacian operator augmented by a noise-smoothing mask. We find that the NNGVF snake provides better segmentation than the GVF snake in terms of noise resistance, weak edge preservation, and an enlarged capture range. The NNGVF snake accomplishes this with a reduced computational cost while maintaining other desirable properties of the GVF snake, such as initialization insensitivity and good convergences at concavities. We demonstrate the advantages of NNGVF on synthetic and real images.

Image Segmentation Using Active Contours With Normally Biased GVF External Force

Gradient vector flow (GVF) is an effective external force for active contours, but its isotropic nature handicaps its performance. The recently proposed NGVF model is anisotropic since it only keeps the diffusion along the normal direction of the isophotes; however, it is sensitive to noise and could erase weak boundaries. In this letter, the normally biased GVF (NBGVF) external force is proposed for snake models, which keeps the diffusion along the tangential direction of the isophotes and biases that along the normal direction. The biasing weight approaches zero at boundaries and is 1 in homogeneous regions. Consequently, the NBGVF snake can preserve weak edges and smooth out noise while maintaining other desirable properties of GVF and NGVF snakes such as enlarged capture range, insensitivity to initialization and convergence to u-shape concavity. These properties are evaluated on synthetic and real images.

Geodesic active contour, inertia and initial speed

A new force field for geodesic active contours, called inertia, is proposed in this paper. Based on analyzing the evolution process of geodesic active contours and constructing extension velocities in level set methods, it is found that geodesic active contours can inherit their evolution velocities from the previous time step and have the tendency to keep their original evolution state through integrating inertia into them. As an useful complementarity to the force field family for geodesic active contours, inertia force is constructed on the base of gradient vector flow external force field. Experimental results reveal that, compared with gradient vector flow geodesic active contours, geodesic active contours integrated with inertia and gradient vector flow can enter into long indentions and special-shape concavities. Furthermore, they are more tolerant toward initial positions under the action of inertia and initial speeds.

Harmonic gradient vector flow external force for snake model

Gradient vector flow (GVF), an external force for the snake model, is reformulated by replacing the smoothness constraint with a harmonic one, which is related to the divergence and the curl of the vector field. The proposed formulation provides some new insights into the nature of the GVF.