Article Contents
Article Contents

# Nonlinear diffusion based image segmentation using two fast algorithms

• * Corresponding author: Lu Tan
• In this paper, a new variational model is proposed for image segmentation based on active contours, nonlinear diffusion and level sets. It includes a Chan-Vese model-based data fitting term and a regularized term that uses the potential functions (PF) of nonlinear diffusion. The former term can segment the image by region partition instead of having to rely on the edge information. The latter term is capable of automatically preserving image edges as well as smoothing noisy regions. To improve computational efficiency, the implementation of the proposed model does not directly solve the high order nonlinear partial differential equations and instead exploit the efficient alternating direction method of multipliers (ADMM), which allows the use of fast Fourier transform (FFT), analytical generalized soft thresholding equation, and projection formula. In particular, we creatively propose a new fast algorithm, normal vector projection method (NVPM), based on alternating optimization method and normal vector projection. Its stability can be the same as ADMM and it has faster convergence ability. Extensive numerical experiments on grey and colour images validate the effectiveness of the proposed model and the efficiency of the algorithms.

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

 Citation:

• Figure 1.  Effects of our model. The first row: initial curves. The second row: the results obtained by ADMM and NVPM. (a2) and (b2) from ADMM, (c2) and (d2) from NVPM

Figure 2.  Effects of GAC and PSAC modelsw. The first and the fourth column: initial curves. The second and the fifth column: final curves of GAC model. The third and the sixth column: final curves of PSAC model

Figure 3.  Plots of parametric errors and energy curves. The first row is obtained by ADMM. The second row is obtained by NVPM

Figure 4.  Effects of our model, GAC model and PSAC model. The first column: initial curves. The second column: the results of our model obtained by ADMM (top) and NVPM (bottom). The third column: the results of GAC model. The last column: the results of PSAC model

Figure 5.  Non-threshold solutions of our methods. The first column: final results of $\phi$. The second column: zoomed small sub-regions (red rectangles in (c1) and (d1)) for detail comparison

Figure 6.  Effects of our model, GAC model and PSAC model on colour images. (a1), (b1) and (c1): initial curves. (a2), (b2) and (c2): results of our model via ADMM (a2), NVPM (b2) and NVPM* (c2). (a3), (b3) and (c3): GAC model results. (a4), (b4) and (c4): results of PSAC model

Figure 7.  Plots of parametric errors and energy curves. The first row is obtained by our model using ADMM. The second row is obtained by our model using NVPM*

Table 1.  Potential functions for the regularization term

 $\varphi(|\nabla\phi|)$ source (ⅰ) \$ |\nabla\phi|^p, 0

Table 2.  Comparisons of iterations and time using different methods

 Image Methods Iterations Time (sec) Fig. 1 (a2)PF (ⅰ) ADMM 3 0.062 NVPM 3 0.047 NVPM* 3 0.039 Fig. 1 (b2)PF (ⅲ) ADMM 7 0.162 NVPM 6 0.132 NVPM* 6 0.122 Fig. 1 (c2)PF (ⅴ) ADMM 5 0.155 NVPM 5 0.145 NVPM* 5 0.141 Fig. 1 (d2)PF (ⅶ) ADMM 3 0.094 NVPM 3 0.083 NVPM* 3 0.076

Table 3.  Comparisons of iterations and time using different methods

 Image Methods Iterations Time (sec) Fig. 4 (a2) PF (ⅱ) ADMM 6 0.184 NVPM 6 0.178 NVPM* 6 0.175 Fig. 4 (b2)PF (ⅳ) ADMM 16 0.215 NVPM 9 0.118 NVPM* 8 0.109

Table 4.  Comparisons of iterations and time using different methods

 Image Methods Iterations Time (sec) Fig. 6 (a2)PF (ⅵ) ADMM 6 0.336 NVPM 5 0.273 NVPM* 5 0.264 Fig. 6 (a2)PF (ⅷ) ADMM 7 0.389 NVPM 7 0.318 NVPM* 7 0.296 Fig. 6 (b2) PF (ⅸ) ADMM 5 0.175 NVPM 5 0.168 NVPM* 5 0.162 Fig. 6 (b2)PF (ⅹ) ADMM 5 0.183 NVPM 5 0.172 NVPM* 5 0.163 Fig. 6 (c2)PF (ⅵ) ADMM 11 2.389 NVPM 11 2.052 NVPM* 11 2.043 Fig. 6 (c2)PF (ⅸ) ADMM 10 1.998 NVPM 10 1.805 NVPM* 9 1.626
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