Abstract
An improved image segmentation model was established to achieve accurate detection of target contours under high noise, low resolution, and uneven illumination environments. The new model is based on the variational level set algorithm, which improves the C–V (Chan and Vese) model and GAC (Geodesic Active Contour) model, fuses the contour and area models to segment the image information, that is, the edge information and region information of the image are fused into the same "energy" functional. According to the geometric characteristics of the curve, GAC model can effectively avoid re parameterization and light insensitivity in the evolution process, and CV model can effectively distinguish the fuzzy boundary of the image by maximizing the gray difference between the target and the background, it has strong anti-noise performance. By solving the steady-state solution of the partial differential equation, the optimal solution of the energy model is solved. New method can improve the calculation accuracy, topological structure adaptability, anti-noise ability, and reduce the light sensitivity effectively. Experiment shows that the new model has good robustness, high real-time performance, and it can effectively improve detection accuracy.
Similar content being viewed by others
References
Zhou, S., Kan, P., Silbernagel, J., & Jin, J. (2020). Application of image segmentation in surface water extraction of freshwater lakes using radar data. ISPRS International Journal of Geo-Information, 9(7), 424.
Zhang, Y., Chen, P., Hong, H., Huang, Z., & Zhou, C. (2020). The research of image segmentation methods for interested area extraction in image matching guidance. Automatic Target Recognition and Navigation.
Sakaridis, C., Dai, D., & Van Gool, L. (2020). Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2020.3045882
Islam, M. M., & Kashem, M. A .(2021). Parametric active contour model-based tumor area segmentation from brain mri images using minimum initial points. Iran Journal of Computer Science, 4,125–132.
Kowdiki, M., & Khaparde, A. (2021). Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with dynamic time warping. Computer Science Review, 39, 100320.
Jiang, D., Huo, L., & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE, 13(5), 1–23.
Jiang, D., Zhang, P., & Lv, Z. (2016). Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal, 3(6), 1437–1447.
Yu, S., Lu, Y., & Molloy, D. (2019). A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations. IEEE Transactions on Image Processing, 28(3), 1513–1527.
Jiang, D., Li, W., & Lv, H. (2017). An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing, 220(12), 160–169.
Ren, H., Su, Z. B., Lv, C. H., & Zou, F. J. (2015). An improved algorithm for active contour extraction based on greedy snake. In IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS). https://doi.org/10.1109/ICIS.2015.
Celestine, A., & Peter, J. D. (2020). Investigations on adaptive connectivity and shape prior based fuzzy graph-cut colour image segmentation. Expert Systems. https://doi.org/10.1111/exsy.12554.
Feng, C., Yang, J., Lou, C., Li, W., & Zhao, D. (2020). A global inhomogeneous intensity clustering- (ginc-) based active contour model for image segmentation and bias correction. Computational and Mathematical Methods in Medicine., 2020(5), 1–18.
Wang, Y., Jiang, D., Huo, L., & Zhao, Y. (2021). A new traffic prediction algorithm to software defined networking. Mobile Networks and Applications. online available, 26, 716–725.
Jiang, D., Wang, Y., Lv, Z., Wang, W., & Wang, H. (2020). An energy-efficient networking ap-proach in cloud services for IIoT networks. IEEE Journal on Selected Areas in Commu-nications., 38(5), 928–941.
Huo, L., Jiang, D., Lv, Z., & Singh, S. (2020). An intelligent optimization‐based traffic information acquirement approach to software‐defined networking. Computational Intelligence, 36(1), 151–171.
Mohana, P. R., & Venkatesan, P. (2021). An efficient image segmentation and classification of lung lesions in pet and ct image fusion using dtwt incorporated svm. Microprocessors and Microsystems. https://doi.org/10.1016/j.micpro.2021.103958.
Mariano, R., Oscar, D., Washington, M., & Alonso, R. M. (2018). Spatial sampling for image segmentation. Computer Journal, (3), 313–324.
Jiang, D., Wang, Y., Lv, Z., Qi, S., & Singh, S. (2020). Big data analysis based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics, 16(2), 1310–1320.
Huo, L., Jiang, D., Qi, S., Song, H., & Miao, L. (2021). An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mobile Networks and Applications, 26(7), 575–585.
Avalos, G., Geredeli, P. G .(2020). Exponential stability of a non-dissipative, compressible flow–structure pde model. Journal of Evolution Equations, 20(1), 1–38. https://doi.org/10.1007/s00028-019-00513-9
Xia, M., Greenman, C. D., & Chou, T. (2020). Pde models of adder mechanisms in cellular proliferation. SIAM Journal on Applied Mathematics., 80(3), 1307–1335.
Kolářová, E., & Brančík, L. (2019). Noise influenced transmission line model via partial stochastic differential equations. International Conference on Telecommunications and Signal Processing (TSP). https://doi.org/10.1109/TSP.2019.8769101.
Pels, A., Gyselinck, J., Sabariego, R. V., & Schops, S. (2017). Solving nonlinear circuits with pulsed excitation by multirate partial differential equations. IEEE Transactions on Magnetics., 54(3), 1–4.
Reska, D., & Kretowski, M. (2021). Gpu-accelerated image segmentation based on level sets and multiple texture features. Multimedia Tools and Applications, 80(1), 1–23.
Ozturk, N., & Ozturk, S. (2021). A new effective hybrid segmentation method based on C–V and LGDF. Signal Image and Video Processing. https://doi.org/10.1007/s11760-021-01862-0
Jiang, D., Huo, L., & Song, H. (2020). Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Transactions on Network Science and Engineering., 7(1), 80–90.
Qi, S., Jiang, D., & Huo, L. (2021). A prediction approach to end-to-end traffic in space information networks. Mobile Networks and Applications, 26, 726–735.
Jiang, D., Wang, W., Shi, L., & Song, H. (2020). A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Transactions on Network Science and Engineering, 7(1), 507–519.
Jiang, D., Huo, L., Lv, Z., Song, H., & Qin, W. (2018). A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Transactions on Intel-ligent Transportation Systems., 19(10), 3305–3319.
Wang, F., Jiang, D., & Qi, S. (2019). An adaptive routing algorithm for integrated information networks. China Communications., 7(1), 196–207.
Liu, G., Dong, Y., Deng, M., & Liu, Y. (2020). Magnetostatic active contour model with classification method of sparse representation. Journal of Electrical and Computer Engineering., 2020(9), 1–10.
Zhang, H., Wang, G., Li, Y., & Wang, H. (2020). Faster r-cnn, fourth-order partial differential equation and global-local active contour model (fpde-glacm) for plaque segmentation in iv-oct image. Signal, Image and Video Processing., 14(3), 509–517.
Ali, H., Sher, A., Saeed, M., & Rada, L. (2020). Active contour image segmentation model with de-hazing constraints. IET Image Processing., 14(5), 921–928.
Wang, X., Zhao, X., Zhu, Y., & Su, X. (2020). Nsst and vector-valued c–v model based image segmentation algorithm. IET Image Processing, 14(8), 1614–1620.
Qiu, X., Yuan, J., & Li, L. (2020). An improved multi-level set C-V model for grading of korean pine seeds. Journal of Physics Conference Series, 1518, 012033.
Roberts, M., Chen, K., & Irion, K. L. (2019). A convex geodesic selective model for image segmentation. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-018-0857-2.
Yu, S., & Yiquan, W. (2020). A morphological approach to piecewise constant active contour model incorporated with the geodesic edge term. Machine Vision and Applications, 31(4), 1–25.
Reckinger, S., & Hughes, B. (2020). Strategies for implementing in-class, active, programming assessments: a multi-level model. In SIGCSE '20 The 51st ACM Technical Symposium on Computer Science Education, ACM.
Sarotte, C., Marzat, J., Piet-Lahanier, H., Ordonneau, G., & Galeotta, M. (2020). Model-based active fault-tolerant control for a cryogenic combustion test bench. Acta Astronautica, 177, 457–477.
Kai, L. I., Jianhua, Z., Shuqing, H., Fantao, K., & Jianzhai, W. U. (2019). Target extraction of cotton disease leaf images based on improved C-V model. Journal of China Agricultural University.
Lakra, M., & Kumar, S. (2020). A cnn-based computational algorithm for nonlinear image diffusion problem. Multimedia Tools and Applications, 79, 23887–23908.
Acknowledgements
This work is partly supported by the Key Laboratory of Intelligent Industrial Control Technology of the Jiangsu Province Research Project (JSKLIIC201705), Science and Technology Project of Ministry of Housing and Urban Rural Development(2014-K5-027), Xuzhou Science and Technology Plan Project (KC19003), the National Natural Science Foundation of China(62001148), the Fundamental Research Funds for the Provincial Universities of Zhejiang(GK199900299012-004), and the General Scientific Research Project of Zhejiang Provincial Education Department(Y201942025).
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
About this article
Cite this article
Li, D., Bei, L., Bao, J. et al. Image contour detection based on improved level set in complex environment. Wireless Netw 27, 4389–4402 (2021). https://doi.org/10.1007/s11276-021-02664-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-021-02664-5