Objective

2

Image segmentation is based on the grayscale information of the image

and the homogenous regions with different properties of the image are divided using different methods without overlapping each other. When image segmentation is performed to obtain the region of interest

the image will inevitably have a certain degree of noise due to various factors

the noise will cause the image edge to be weakened

the false edge will be generated during segmentation

the segmentation curve will fall into the local minimum

and the evolution will stop. The situation

in turn

affects the accuracy of the edge recognition of the target to be segmented during image segmentation

and the segmentation result encounters difficulty in achieving the desired effect. The level set method based on active set is widely used in image segmentation

but it is still affected by noise. Thus

a new method is proposed to solve the noise problem.

Method

2

Since given that the anisotropic diffusion model is applied to image segmentation

including denoising and maintaining the edge of the target to be segmented

a new model is proposed to fuse the anisotropic diffusion divergence field information into the DRLSE model to improve the efficiency and accuracy of the existing segmentation algorithm of the noise image based on the distance regularization level set (DRLSE) evolution model.The model can overcome the problems of the distance regularized level set model

such as slow convergence speed

easy to fall into the false boundary and leak from the weak edge. The improved model can accelerate the initial contour evolution to the edge of the target to be segmented when segmenting the noise image. The main improvement is to change the constant coefficient α of the control area term in the DRLSE evolution model to the variable weight coefficient α(

$$I$$

) on the basis of the anisotropic diffusion divergence field information. Given that the improved variable weight coefficient is based on the anisotropic diffusion information

the improved nonedge force is considerably smoothed down

the vector force at the edge is strengthened

and the edge interfered by noise is strengthened. The improved model can accelerate the initial contour evolution to the edge of the target to be segmented when segmenting the noise image.

Result

2

Image segmentation experiments are performed on several images selected from the Weizmann database and artificially modified images. Segmentation time

D coefficient

and J coefficient are compared (the closer the D and J coefficients to the 1 segmentation result and true value image similarity

the higher the ratio

and the greater the similarity between the segmentation efficiency and result). The noise-free image is segmented. If the image background is simple and the edge is clear

then the D-factor and J-factor of the model segmentation result are 0.996 4 and 0.992 4

respectively. The D-factor and J-factor of the DRLSE model segmentation result are 0.995 6 and 0.990 6

respectively. The model segmentation time is 3.809 s. Compared with the 3.809 s of the DRLSE model

the D-factor and J-factor of the model segmentation result are 0.994 6 and 0.993 1

respectively

and the D-factor and J-factor of the DRLSE model are 0.991 1 and 0.981 8

respectively. The model segmentation time of 4.294 s is shorter than the DRLSE model's 4.966 s. For the edge part

the fuzzy background gray image is uneven. The D and J coefficients of the model segmentation result are 0.997 1 and 0.989 3

respectively. The D and J coefficients of the DRLSE model segmentation result are 0.970 4 and 0.931 5

respectively. The model segmentation time of 3.718 s is shorter than the DRLSE model's 13.241 s. The noise image is segmented. If the background of the image is simple

then the edge is only an artificially added noise. The D and J coefficients of the model segmentation result are 0.997 6 and 0.995 0

respectively

and the D and J coefficients of the DRLSE model segmentation result are 0.997 2 and 0.994 2

respectively. The time of 4.182 s is shorter than the DRLSE model's 11.274 s. For the image with noise and uneven edge gray

the D and J coefficients of the model segmentation are 0.990 3 and 0.980 2

respectively. The D and J coefficients of the DRLSE model segmentation result are 0.980 5 and 0.960 3

respectively. The model segmentation time of 4.294 s is shorter than the DRLSE model's 4.966 s. For the image where the noise is to be segmented

a concave region is observed

and the edge blurred background gray is uneven. The D and J coefficients of the model segmentation result are 0.985 7 and 0.970 9

respectively. The D and J coefficients of the DRLSE model are 0.885 3 and 0.747 3

respectively. The model segmentation time of 4.235 s is shorter than the DRLSE model's 39.400 s. Results of the segmentation experiments show that the improved model is considerably better in terms of segmentation accuracy and runtime than the original model for noisy and noise-free images.

Conclusion

2

In this paper

we introduce the DRLSE evolution model based on the anisotropic diffusion divergence field model. The improved model can accelerate the curve evolution to the target boundary given gray unevenness in the background. The image can overcome the noise and edge weakening caused by the noise figure in evolution and several shortcomings of the DRLSE evolution model during evolution. Experimental results show the effectiveness

efficiency

and robustness of the fusion anisotropic diffusion information DRLSE model in image segmentation

especially noise image segmentation.