Objective

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The active contour model is a widely used method in image segmentation. The basic idea of the traditional parametric active contour model is to define an initial curve composed of control points

and make the curve as close to the target as possible

by defining the energy function and finding the minimum value to obtain the edge contour of the target object

because of its sensitivity to initialization

small capture range and can not converge to the concave area and other shortcomings

so that the initial contour must be set close to the edge of the target object in order to get a better segmentation results

or can not effectively extract the target edge contour or extract the results are not accurate. Some scholars have made improvements on this basis and proposed various active contour models based on vector fields

such as classical gradient vector flow (GVF)

vector field convolution (VFC)

adaptive diffusion flow (ADF) model and other models

these vector-based active contour models use the new external force field instead of the original Gaussian force field. Although the shortcomings of the traditional parametric active contour model are solved

the initial contour is insensitive

and the capturing range is extended

for some simple images with concave regions

the boundary can be extracted accurately

but there is still a problem of premature convergence for image of containing complex concave. For the vector contour model based on the vector field

the vector field often appears at the equilibrium point when extracting the concave object

resulting in premature convergence

it can not be better convergence to the depression area

especially deep and narrow concave and complex depression area. An active contour model integrating concave point detection and affine transformation is proposed to solve the above problems.

Method

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Because VFC model is not sensitive to initialization

can enter a part of the concave area

and the calculation is simple

so this paper chooses the VFC model based on the fusion concave point detection and affine transformation method. First

the VFC active contour model based on vector field is used to simulate the curve. The coordinates of the points on the contour curve are obtained

and the normal direction of each point is obtained. And then use the method of concave point detection to judge the concavity and convexity of each point

concave points are extracted separately

the concave point that does not converge to the target boundary is detected by the gradient method; Second

the affine transformation of these concave points is extended to the normal direction

and the maximum distance can be obtained without approaching and not crossing the target boundary

a new contour curve is formed by replacing the original points with the transformed points

and the transformed points cross the area of equilibrium point

have the force to continue converging to the concave area; Finally

in order to ensure the accuracy of the extraction boundary

the new contour curve will be transformed once again

and finally converge to the target boundary

the complete object is extracted.

Result

2

The GVF model

VFC model

ADF model and the proposed model were tested and compared respectively in the data sets of the composite images with concave regions

single/multi-target real images and noise images. By dividing the synthetic image with the concave area

it shows that the proposed model is superior to the concave area segmentation

and can accurately enter the concave area and the calculation is simple

on the basis of this

the average JS value of the model segmentation is 95.51%. Compared with the current advanced active contour model based on vector field:GVF model

VFC model and ADF model

the similarity of model segmentation results is improved 15.08%

12.09% and 10.70% respectively

the overall effect is superior to the mention of these advanced models

which not only solves the problem of edge contour extraction of target objects in concave area

but also improves the accuracy of segmentation. Then

segmentation of single/multi-target images and noise images

the experimental results show that the proposed model can improve the robustness of the segmentation performance on the basis of maintaining the good segmentation effect of the original model.

Conclusion

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The model proposed by the fusion concave points detection and affine transformation effectively avoids the problem of the equilibrium point which is often found in the concave area of the active contour model based on the vector field. It not only achieves the effective segmentation of the deep concave and the complex concave image

but also improves the segmentation accuracy so that the extracted edge contour is closer to the real boundary of the target object. In addition

although this paper is a fusion of concave point detection and affine transform based on the VFC model

but the fusion of concave point detection and affine transform in active contour model is introduced to solve the equilibrium problem can be applied to any vector field based on active contour model

and has wide universality. This paper mainly solves the problem that the active contour model based on vector field appears the equilibrium point in the process of evolution of concave area

the problem of premature convergence leads to the in correctly edge contour

and there is not too much consideration of multi-objective image segmentation

background and noise effects

although the proposed model to maintain the excellent segmentation characteristics of original model to solve these problems

but also need a lot of experiments to test

verify. So this problem can be used as the next step to improve the direction of the model

continue in-depth study.