In view of the problem that the local active contour model is difficult to achieve image segmentation accurately and quickly, an improved image segmentation method based on Local Image Fitting (LIF) is proposed. Firstly, the local median is used as the fitting center of the curve to enhance the robustness of the model to noise. Secondly, a minimized Laplacian of gaussian energy (Log) term is introduced, and the Log operator is used to smooth the image and enhance the edges of the image. Finally, the minimized Log energy term is combined with the LIF, which together drives the curve to the boundary. Experimental results show that the Precision rate, Recall rate and Dice Similarity Coefficient of this model are closest to 1. Compared with other main region-based models, the image segmentation accuracy of this method is significantly higher than that of other algorithms, which improves the anti-noise performance and image segmentation speed.
Cite this paper
Chen, W. , Liu, C. and Pan, B. (2021). An Improved Active Contour Model Based on Local Information. Open Access Library Journal, 8, e7187. doi: http://dx.doi.org/10.4236/oalib.1107187.
Chen, D. and Cohen, L.D. (2017) Anisotropic Edge-Based Balloon Eikonal Active Contours. International Conference on Geometric Science of Information, Paris, 7-9 November 2017, 782-790. https://doi.org/10.1007/978-3-319-68445-1_90
Badoual, A., Unser, M. and Depeursinge, A. (2019) Texture-Driven Parametric Snakes for Semi-Automatic Image Segmentation. Computer Vision and Image Understanding, 188, Article ID: 102793. https://doi.org/10.1016/j.cviu.2019.102793
Fang, L.L., Zhao, W.T., Li, X.Y. and Wang, X.H. (2017) A Convex Active Contour Model Driven by Local Entropy Energy with Applications to Infrared Ship Target Segmentation. Optics and Laser Technology, 96, 166-175.
https://doi.org/10.1016/j.optlastec.2017.05.008
Wang, H., Huang, T., Xu, Z. and Wang, Y. (2016) A Two-Stage Image Segmentation via Global and Local Region Active Contours. Neurocomputing, 205, 130-140.
https://doi.org/10.1016/j.neucom.2016.03.050
Zhang, W.H., Wang, X., Zhang, P.B. and Chen, J.F. (2017) Global Optimal Hybrid Geometric Active Contour for Automated Lung Segmentation on CT Images. Computers in Biology and Medicine, 91, 168-180.
https://doi.org/10.1016/j.compbiomed.2017.10.005
Zhang, K., Zhang, L., Lam, K. and Zhang, D. (2017) A Level Set Approach to Image Segmentation with Intensity in Homogeneity. IEEE Transactions on Cybernetics, 46, 546-557. https://doi.org/10.1109/TCYB.2015.2409119
Liu, C., Xie, C.H., Yang, J., Xiao, Y.Y. and Bao, J.L. (2018) A Method for Coastal Oil Tank Detection in Polarimetric SAR Images Based on Recognition of T-Shaped Harbor. Journal of Systems Engineering and Electronics, 29, 499-509.
https://doi.org/10.21629/JSEE.2018.03.07
Guan, S.-Y., Wang, T.-M., Meng, C. and Wang, J.-C. (2018) A Review of Point Feature Based Medical Image Registration. Chinese Journal of Mechanical Engineering, 31, 21-36. https://doi.org/10.1186/s10033-018-0275-9
Li, C.M., Kao, C.Y., Gore, J.C., et al. (2007) Implicit Active Contours Driven by Local Binary Fitting Energy. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 17-22 June 2007, 1-7.
https://doi.org/10.1109/CVPR.2007.383014
Zhang, K.H., Song, H.H. and Zhang, L. (2010) Active Contours Driven by Local Image Fitting Energy. Pattern Recognition, 43, 1199-1206.
https://doi.org/10.1016/j.patcog.2009.10.010
Ding, K.Y., Xiao, L.F. and Weng, G.R. (2018) Active Contours Driven by Local Pre-Fitting Energy for Fast Image Segmentation. Pattern Recognition Letters, 104, 29-36. https://doi.org/10.1016/j.patrec.2018.01.019
Han, B. and Wu, Y.Q. (2019) Active Contours Driven by Global and Local Weighted Signed Pressure Force for Image Segmentation. Pattern Recognition, 88, 715-728.
https://doi.org/10.1016/j.patcog.2018.12.028
Gao, M., Chen, H., Zheng, S., et al. (2019) Feature Fusion and Non-Negative Matrix Factorization Based Active Contours for Texture Segmentation. Signal Processing, 159, 104-118. https://doi.org/10.1016/j.sigpro.2019.01.021
Ding, K., Xiao, L. and Weng, G. (2017) Active Contours Driven by Region-Scalable Fitting and Optimized Laplacian of Gaussian Energy for Image Segmentation. Signal Processing, 134, 224-233. https://doi.org/10.1016/j.sigpro.2016.12.021
Taha, A.A. and Hanbury, A. (2015) Metrics for Evaluating 3D Medical Image Segmentation: Analysis, Selection, and Tool. BMC Medical Imaging, 15, Article No. 29.
https://doi.org/10.1186/s12880-015-0068-x