Accurate and efficient medical image segmentation is a critical yet challenging task due to issues like intensity inhomogeneity, poor contrast, noise, and blur. In this paper, we introduce a novel framework that addresses these challenges by leveraging adaptive level set evolution, enhanced with a unique edge indication function. Unlike prior edge-based algorithms, which frequently fail with noisy images and have large computing costs, our method incorporates an improved edge indicator term into the level set architecture, considerably improving performance on degraded images. The efficiency of proposed model depends on the optimization and implementation of proximal alternating direction technique of multipliers ([Formula: see text]). Our findings were validated using qualitative and quantitative methods such as dice coefficient assessment, sensitivity, accuracy, and mean absolute distance (MAD). Experimental findings show that the model successfully detects boundaries of objects within noisy and blurred visual data. The algorithm showed exceptional precision through its average dice coefficient of 0.96 which matched the ground truth data measurement standards. The system runs efficiently for only 0.90 seconds on average as a performance result. The framework achieved standout performance metrics that included 0.9552 accuracy together with 0.8854 sensitivity and 0.0796 MAD. The framework demonstrates robust capabilities in medical image evaluation which makes it an optimistic instrument for advancing the field.

College of Humanities and Sciences Ajman University Ajman United Arab Emirates

Department of Mathematics Education Faculty Laghman University Mehtarlam City 2701 Afghanistan

General Education Centre Quanzhou University of Information Engineering Quanzhou 362000 Fujian China

IT4Innovations VSB Technical University of Ostrava Ostrava Poruba Czech Republic

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Zhou, S., Wang, J., Zhang, S., Liang, Y. & Gong, Y. Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing186, 107–118 (2016).

Norouzi, A. et al. Medical image segmentation methods, algorithms, and applications. IETE Tech. Rev.31(3), 199–213 (2014).

Szeliski, R. Computer Vision: Algorithms and Applications 1st edn (Springer-Verlag, 2010).

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).

Wang, B., Gao, X., Tao, D. & Li, X. A unified tensor level set for image segmentation. IEEE Trans. Syst. Man Cybern. Part B40(3), 857–867 (2010). PubMed

Wang, Z., Ma, B. & Zhu, Y. Review of level set in image segmentation. Arch. Comput. Methods Eng.28, 2429–2446 (2021).

Yang, X., Gao, X., Tao, D. & Li, X. Improving level set method for fast auroral oval segmentation. IEEE Trans. Image Process.23(7), 2854–2865 (2014). PubMed

Chan, T. & Vese, L. Active contours without edges. IEEE Trans. Image Process.10(2), 266–277 (2001). PubMed

Tsai, A., Yezzi, A. & Willsky, A. S. Curve evolution implementation of the Mumford–Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process.10(8), 1169–1186 (2001). PubMed

Shi, Y. & Karl, W. Real-time tracking using level sets. in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) Vol. 2, 34–41 (2005).

Osher , S. & Paragios, N. Geometric Level Set Methods in Imaging, Vision, and Graphics (Springer Science & Business Media, 2003).

Agarwal, R. P. & Wong, P. J. Advanced Topics in Difference Equations Vol. 404 (Springer Science & Business Media, 2013).

Cai, Q. et al. AVLSM: Adaptive variational level set model for image segmentation in the presence of severe intensity inhomogeneity and high noise. IEEE Trans. Image Process.31, 43–57 (2021). PubMed

Jiang, L. & Chen, S. Parametric structural shape & topology optimization with a variational distance-regularized level set method. Comput. Methods Appl. Mech. Eng.321, 316–336 (2017).

Lv, H., Zhang, Y. & Wang, R. Active contour model based on local absolute difference energy and fractional-order penalty term. Appl. Math. Model.107, 207–232 (2022).

Lutful, M. et al. Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements. Sci. Rep.12(1), 15949 (2022). PubMed PMC

Umirzakova, S., Mardieva, S., Muksimova, S., Ahmad, S. & Whangbo, T. Enhancing the super-resolution of medical images: Introducing the deep residual feature distillation channel attention network for optimized performance and efficiency’’. Bioengineering10(11), 1332 (2023). PubMed PMC

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. 1 430–436 (IEEE, 2005).

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). PubMed PMC

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). PubMed

Zheng, L., Wang, S. & Tian, Q. Coupled binary embedding for large-scale image retrieval. IEEE Trans. Image Process.23(8), 3368–3380 (2014). PubMed

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).

Han, Y., Zhang, S., Geng, Z., Wei, Q. & Zhi, O. Level set based shape prior and deep learning for image segmentation. IET Image Process.14, 183–191 (2020).

Xu, C. et al. Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process.7(3), 359–369 (1998). PubMed

Sussman, M., Smereka, P. & Osher, S. A level set approach for computing solutions to incompressible two-phase flow. J. Comput. Phys.114(1), 146–159 (1994).

Enright, D., Fedkiw, R., Ferziger, J. & Mitchell, I. A hybrid particle level set method for improved interface capturing. J. Comput. Phys.183(1), 83–116 (2002).

Osher, S., Paragios, N., Weickert, J. & Kühne, G. Fast Methods for Implicit Active Contour Models (Springer, 2003).

Yushkevich, P. A. et al. User-guided 3d active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage31(3), 1116–1128 (2006). PubMed

Sureau, F., Latreche, M., Savanier, M. & Comtat, C. Convergent admm plug and play pet image reconstruction. arXiv preprint arXiv:2310.04299 (2023).

Pang, Z.-F., Fan, L.-L. & Zhu, H.-H. A novel dual-based admm to the chan-vese model. Multimedia Tools and Applications 1–18 (2023).

He, B., Liao, L.-Z., Han, D. & Yang, H. A new inexact alternating directions method for monotone variational inequalities. Math. Programm.92, 103–118 (2002).

Parikh, N. et al.Proximal Algorithms, Foundations and Trends R in Optimization (2014).

Yang, Y., Jia, Q.-S., Xu, Z., Guan, X. & Spanos, C. J. Proximal admm for nonconvex and nonsmooth optimization. Automatica146, 110551 (2022).

He, B., Ma, F. & Yuan, X. Optimal linearized alternating direction method of multipliers for convex programming. http://www.optimization-online.org (2017).

Sawatzky, A., Xu, Q., Schirra, C. O. & Anastasio, M. A. Proximal admm for multi-channel image reconstruction in spectral x-ray ct. IEEE Trans. Med. Imaging33(8), 1657–1668 (2014). PubMed

Wali, S. et al. An efficient method for Eulerâs elastica based image deconvolution. IEEE Access7, 61226–61239 (2019).

Chang, H., Lou, Y., Ng, M. K. & Zeng, T. Phase retrieval from incomplete magnitude information via total variation regularization. SIAM J. Sci. Comput.38(6), A3672–A3695 (2016).

Taha, A. A. & Hanbury, A. Metrics for evaluating 3d medical image segmentation: Analysis, selection, and tool. BMC Med. Imaging15(1), 1–28 (2015). PubMed PMC

Xie, X. & Mirmehdi, M. Rags: Region-aided geometric snake. IEEE Trans. Image Process.13, 640–652 (2004). PubMed

Ronfard, R. Region-based strategies for active contour models. Int. J. Comput. Vis.13, 229–251 (1994).

Ni, Y. et al. DA-Tran: Multiphase liver tumor segmentation with a domain-adaptive transformer network. Pattern Recognit.149, 110233 (2024).

Olaf, R., Philipp, F. & Thomas, B. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 234-241 (2015).

Li, C., Gore, J. C. & Christos, D. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging32(7), 913–923 (2014). PubMed PMC