A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function


  • Xiaotian Wang
  • Zhang Liu
  • Chencheng Huang
  • Qi Wang
  • Jiaxi Wang




image segmentation, active contour model, level set function, adaptive double potential well function.


In this paper, we present a modified local region-based active contour model employing an adaptive double potential well function for image segmentation. Initially, to circumvent the issue of the potential well function's excessive evolutionary pace within the zero potential well, which could lead to rapid level set evolution and inadvertent targeting of segmentation areas, we introduce an adaptive double potential well function. This function dynamically modulates coefficient by increasing the diffusion rate during the initial phase, decreasing it in the later stages, and mitigating it in the vicinity of the zero potential well. Subsequently, we incorporate a length term and a penalty term, both predicated on the adaptive double well function, into the energy functional derived from the local region-based Chan-Vese (LRCV) model. This integration serves to augment the edge smoothness of the curve and the precision of the segmentation process. Experimental outcomes demonstrate that our proposed model significantly augments segmentation accuracy when benchmarked against certain related models.


Ames W F. Numerical methods for partial differential equations[M]. Academic press, 2014:15-19.

Chan T, Vese L. Active contours without edges[J]. IEEE Trans Image Process, 2001, 10(2):266-277.

Cheng Y Z, Zhong L H, He. Segmentation of wood cross section pipe holes based on improved K-Means clustering and Level set [J]. Forest Engineering, 2022,38 (01): 42-51.

Deng L N. Improvement of CV model and its application in image segmentation [D]. Xi'an University of Science and Technology, 2019.

Gao M Y. Research on image segmentation based on CV Level set [D]. Southwest University, 2020.

Li C M, Kao C Y, Gore J C Minimization of region-scalable fitting energy for image segmentation[J]. IEEE transactions on image processing, 2008, 17(10):1940-1949.

Li C M, Xu C Y, Gui C F. Level set evolution without re-initialization: a new variational formulation[C]. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’05), 2005, 1:430-436.

Li C M, Xu C Y, Gui C F. Distance regularized level set evolution and its application to image segmentation[J]. IEEE transactions on image processing, 2010, 19(12):3243-3254.

Litjens G, Toth R, Van de Ven W. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge[J]. Medical Image Analysis, 2014, 18(2):359-373.

Liu S G, Peng Y L. A local region-based Chan-Vese model for image segmentation[J]. Pattern Recognition, 2012, 45(7):2769-2779.

Ma Rui. Research on image segmentation method based on partial differential equation [D]. Xinjiang Normal University, 2021.

Mao L, Zhao L Q, Yu M A. Parathyroid segmentation based on hybrid level set model based on image local entropy[J]. Acta Optica Sinica, 2019, 39(12):256-264.

Mumford D B, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems[J]. Communications on pure and applied mathematics, 1989, 42(5):577-685.

Osher S, Sethian J A. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations[J]. Journal of computational physics, 1988, 79(1):12-49.

Pan JY. Iterative Residual Optimization Network for Limited-angle Tomographic Reconstruction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2024, 33:910-925.

Shen Y J, Wang X P, Zhou Y Z. Local level set mask optimization based on semi implicit discretization[J]. Acta Optica Sinica, 2021, 41(09):96-103.

Sun C, Xu Y L, Bi D Y. Distance normalized level set algorithm using V-well function[J]. Computer application and software, 2013, 30(4):271-274.

Wang D K, Hou Y Q, Peng J Y. Partial differential equation method for image processing[M]. Beijing:Science Press, 2008:103-107.

Wu WW. Dual-domain residual-based optimization network for sparse-view CT reconstruction[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2021, 40(11):3002-3014.

Xu Z. Image segmentation method based on local region and Level set regularization [J]. Journal of Tonghua Normal University, 2023,44 (02): 28-32.

Yang X, Hofmann R, Dapp R, Kamp T, Rolo T, Xiao X, Moosmann J, Kashef J, and Stotzka R. TV-based conjugate gradient method and discrete L-curve for few-view CT reconstruction of X-ray in vivo data [J]. Optics express, 2015, 23(5): 5368-5387.

Zhang H Y, Gao S B, Zhou J B. A novel active contour model method[J]. Microelectronics and computer, 2015, 32(09):161-163.

Zhang Y, Yang K, Zhu Y. NOWNUNM: Nonlocal Weighted Nuclear Norm Minimization for Sparse-Sampling CT Reconstruction[J]. IEEE Access, 2018, 6:73370-73379.

Zheng Y R. An image segmentation method based on hesitant wisdom set and Level set [J]. Journal of Chongqing Technology and Business University (Natural Science Edition), 2022,39 (05): 17-23.



2024-06-11 — Updated on 2024-06-28

How to Cite

Wang, X., Liu, Z., Huang, C., Wang, Q., & Wang, J. (2024). A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function. Image Analysis and Stereology, 43(2), 151–158. https://doi.org/10.5566/ias.3021



Original Research Paper