Abstract
Variational level set method has become a powerful tool in image segmentation due to its ability to handle complex topological changes and maintain continuity and smoothness in the process of evolution. However its evolution process can be unstable, which results in over flatted or over sharpened contours and segmentation failure. To improve the accuracy and stability of evolution, we propose a high-order level set variational segmentation method integrated with molecular beam epitaxy (MBE) equation regularization. This method uses the crystal growth in the MBE process to limit the evolution of the level set function. Thus can avoid the re-initialization in the evolution process and regulate the smoothness of the segmented curve and keep the segmentation results independent of the initial curve selection. It also works for noisy images with intensity inhomogeneity, which is a challenge in image segmentation. To solve the variational model, we derive the gradient flow and design a scalar auxiliary variable scheme, which can significantly improve the computational efficiency compared with the traditional semi-implicit and semi-explicit scheme. Numerical experiments show that the proposed method can generate smooth segmentation curves, preserve segmentation details and obtain robust segmentation results of small objects. Compared to existing level set methods, this model is state-of-the-art in both accuracy and efficiency.
References
Guo, X., Xue, Y., Wu, C.: Effective two-stage image segmentation: a new non-lipschitz decomposition approach with convergent algorithm. J. Math. Imaging Vis. 63, 356–379 (2021). https://doi.org/10.1007/s10851-020-01001-3
Lambert, Z., Le Guyader, C.: About the incorporation of topological prescriptions in cnns for medical image semantic segmentation. J. Math. Imaging Vis. (2024). https://doi.org/10.1007/s10851-024-01172-3
Li, D., Zhang, G., Wu, Z., Yi, L.: An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. IEEE Trans. Image Process. 19(10), 2781–2789 (2010). https://doi.org/10.1109/TIP.2010.2049528
Falcone, M., Paolucci, G., Tozza, S.: A high-order scheme for image segmentation via a modified level-set method. SIAM J. Imaging Sci. 13(1), 497–534 (2020). https://doi.org/10.1137/18M1231432
Bowden, A., Sirakov, N.M.: Active contour directed by the Poisson gradient vector field and edge tracking. J. Math. Imaging Vis. 63(6), 665–680 (2021). https://doi.org/10.1007/s10851-021-01017-3
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000). https://doi.org/10.1109/34.868688
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991). https://doi.org/10.1109/34.87344
Tai, X.-C., Deng, L.-J., Yin, K.: A multigrid algorithm for maxflow and min-cut problems with applications to multiphase image segmentation. J. Sci. Comput. 87(3), 101–22 (2021). https://doi.org/10.1007/s10915-021-01458-3
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001). https://doi.org/10.1109/83.902291
Yang, W., Huang, Z., Zhu, W.: Image segmentation using the Cahn-Hilliard equation. J. Sci. Comput. 79(2), 1057–1077 (2019). https://doi.org/10.1007/s10915-018-00899-7
Cardelino, J., Caselles, V., Bertalmío, M., Randall, G.: A contrario selection of optimal partitions for image segmentation. SIAM J. Imaging Sci. 6(3), 1274–1317 (2013). https://doi.org/10.1137/11086029X
Luo, S., Tai, X.-C., Glowinski, R.: Convex object(s) characterization and segmentation using level set function. J. Math. Imaging Vis. 64(1), 68–88 (2022). https://doi.org/10.1007/s10851-021-01056-w
Gao, W., Bertozzi, A.: Level set based multispectral segmentation with corners. SIAM J. Imaging Sci. 4(2), 597–617 (2011). https://doi.org/10.1137/100799538
Liu, C., Qiao, Z., Zhang, Q.: Two-phase segmentation for intensity inhomogeneous images by the Allen–Cahn local binary fitting model. SIAM J. Sci. Comput. 44(1), 177–196 (2022). https://doi.org/10.1137/21M1421830
Zhang, W., Wang, X., You, W., Chen, J., Dai, P., Zhang, P.: Resls: Region and edge synergetic level set framework for image segmentation. IEEE Trans. Image Process. 29, 57–71 (2020). https://doi.org/10.1109/TIP.2019.2928134
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988). https://doi.org/10.1016/0021-9991(88)90002-2
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997). https://doi.org/10.1023/A:1007979827043
Li, C., Kao, C.-Y., Gore, J.C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008). https://doi.org/10.1109/TIP.2008.2002304
Estellers, V., Zosso, D., Lai, R., Osher, S., Thiran, J.-P., Bresson, X.: Efficient algorithm for level set method preserving distance function. IEEE Trans. Image Process. 21(12), 4722–4734 (2012). https://doi.org/10.1109/TIP.2012.2202674
Osher, S., Fedkiw, R.: Level set methods and dynamic implicit surfaces 153, 273 (2003). https://doi.org/10.1007/b98879
Chopp, D.L.: Computing Minimal Surfaces Via Level Set Curvature Flow 106, 77–91 (1993). https://doi.org/10.1006/jcph.1993.1092
Peng, D., Merriman, B., Osher, S., Zhao, H., Kang, M.: A pde-based fast local level set method. J. Comput. Phys. 155(2), 410–438 (1999). https://doi.org/10.1006/jcph.1999.6345
Sussman, M., Fatemi, E.: An efficient, interface-preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM J. Sci. Comput. 20(4), 1165–1191 (1999). https://doi.org/10.1137/S1064827596298245
Estellers, V., Zosso, D., Lai, R., Osher, S., Thiran, J.-P., Bresson, X.: Efficient algorithm for level set method preserving distance function. IEEE Trans. Image Process. 21(12), 4722–4734 (2012). https://doi.org/10.1109/TIP.2012.2202674
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 430–4361 (2005). https://doi.org/10.1109/CVPR.2005.213
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010). https://doi.org/10.1109/TIP.2010.2069690
**e, X.: Active contouring based on gradient vector interaction and constrained level set diffusion. IEEE Trans. Image Process. 19(1), 154–164 (2010). https://doi.org/10.1109/TIP.2009.2032891
Zhang, K., Zhang, L., Song, H., Zhang, D.: Reinitialization-free level set evolution via reaction diffusion. IEEE Trans. Image Process. 22(1), 258–271 (2013). https://doi.org/10.1109/TIP.2012.2214046
Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011). https://doi.org/10.1109/TIP.2011.2146190
Li, H., Guo, W., Liu, J., Cui, L., **e, D.: Image segmentation with adaptive spatial priors from joint registration. SIAM J. Imaging Sci. 15(3), 1314–1344 (2022). https://doi.org/10.1137/21M1444874
Moldovan, D., Golubovic, L.: Interfacial coarsening dynamics in epitaxial growth with slope selection. Phys. Rev. E 61, 6190–6214 (2000). https://doi.org/10.1103/PhysRevE.61.6190
Shen, J., Xu, J., Yang, J.: A new class of efficient and robust energy stable schemes for gradient flows. SIAM Rev. 61(3), 474–506 (2019). https://doi.org/10.1137/17M1150153
Cheng, Q., Shen, J., Yang, X.: Highly efficient and accurate numerical schemes for the epitaxial thin film growth models by using the SAV approach. J. Sci. Comput. 78(3), 1467–1487 (2019). https://doi.org/10.1007/s10915-018-0832-5
Yao, W., Shen, J., Guo, Z., Sun, J., Wu, B.: A total fractional-order variation model for image super-resolution and its SAV algorithm. J. Sci. Comput. 82(3), 81–18 (2020). https://doi.org/10.1007/s10915-020-01185-1
Li, B., Liu, J.-G.: Thin film epitaxy with or without slope selection. Eur. J. Appl. Math. 14(6), 713–743 (2003). https://doi.org/10.1017/S095679250300528X
Chambolle, A.: Mathematical problems in image processing. ICTP Lecture Notes, vol. II, p. 94. Abdus Salam International Centre for Theoretical Physics, Trieste (2000). Inverse problems in image processing and image segmentation: some mathematical and numerical aspects, Available electronically at http://www.ictp.trieste.it/~pub_off/lectures/vol2.html
Apoung Kamga, J.-B., Després, B.: CFL condition and boundary conditions for DGM approximation of convection–diffusion. SIAM J. Numer. Anal. 44(6), 2245–2269 (2006). https://doi.org/10.1137/050633159
Zhao, H.: A fast swee** method for eikonal equations. Math. Comp. 74(250), 603–627 (2005). https://doi.org/10.1090/S0025-5718-04-01678-3
Wali, S., Li, C., Imran, M., Shakoor, A., Basit, A.: Level-set evolution for medical image segmentation with alternating direction method of multipliers. Signal Process. 211, 109105 (2023). https://doi.org/10.1016/j.sigpro.2023.109105
Chen, Y., Tagare, H.D., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Briggs, R.W., Geiser, E.A.: Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vision 50, 315–328 (2002). https://doi.org/10.1023/A:1020878408985
Ortiz, M., Repetto, E.A., Si, H.: A continuum model of kinetic roughening and coarsening in thin films. J. Mech. Phys. Solids 47(4), 697–730 (1999). https://doi.org/10.1016/S0022-5096(98)00102-1
Zhang, L.: Dirac delta function of matrix argument. Internat. J. Theoret. Phys. 60(7), 2445–2472 (2021). https://doi.org/10.1007/s10773-020-04598-8
Li, B., Ma, S., Schratz, K.: A semi-implicit exponential low-regularity integrator for the Navier–Stokes equations. SIAM J. Numer. Anal. 60(4), 2273–2292 (2022). https://doi.org/10.1137/21M1437007
Ethier, M., Bourgault, Y.: Semi-implicit time-discretization schemes for the bidomain model. SIAM J. Numer. Anal. 46(5), 2443–2468 (2008). https://doi.org/10.1137/070680503
Heideman, M.T., Johnson, D.H., Burrus, C.S.: Gauss and the history of the fast Fourier transform. Arch. Hist. Exact Sci. 34(3), 265–277 (1985). https://doi.org/10.1007/BF00348431
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (U21B2075), National Key R &D Program of China (2023YFC2205900, 2023YFC2205903), the National Natural Science Foundation of China (12171123, 12271130, 12371419), Natural Sciences Foundation of Heilongjiang Province (ZD2022A001), Self-Planned Task of State Key Laboratory of Robotics and System (HIT) (SKLRS201801A05), the Fundamental Research Funds for the Central Universities (2022FRFK060020, 2022FRFK060014).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Song, F., Sun, J., Shi, S. et al. Re-initialization-Free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation. J Math Imaging Vis (2024). https://doi.org/10.1007/s10851-024-01205-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10851-024-01205-x
Keywords
- Image segmentation
- Variational level set method
- Molecular beam epitaxy equation
- Re-initialization-free
- Scalar auxiliary variable