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Region-edge-based active contours driven by hybrid and local fuzzy region-based energy for image segmentation. (English) Zbl 1475.68423

Summary: This paper raises a region-edge-based active contour driven by the hybrid and local fuzzy region-based energy to segment images with high noise and intensity inhomogeneity. The energy functional consists of region energy and edge energy. The region energy is made up of hybrid fuzzy region term and local fuzzy region term. Its aim is to motivate initial contour to move toward the exact object boundary. What’s more, it is proved to be convex and ensures the segmentation results independent of initialization. The hybrid fuzzy region term can balance the importance of the object and background while the local fuzzy region term by incorporating spatial and local information can decrease the effect of intensity inhomogeneity in given images. The edge energy is used to regularize the pseudo level set function (LSF) and maintain the appearance of the smoothness during the curve evolution. Inspired by the fuzzy energy-based active contour (FEAC), a more direct and simpler method is developed to calculate the difference between the old and new energy functions to update the pseudo LSF during the curve evolution. Experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models. The code is available at: https://github.com/fangchj2002/HLFRA.

MSC:

68U10 Computing methodologies for image processing
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory

Software:

GitHub
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References:

[1] Cremers, D.; Rousson, M.; Deriche, R., A review of statistical approaches to level set segmentation: integrating color texture, motion and shape, Int. J. Comput. Vis., 72, 5, 195-215 (2007)
[2] Munoz, X.; Freixenet, J.; Cufi, X.; Marti, J., Strategies for image segmentation combining region and boundary information, Pattern Recognit., 24, 1, 375-392 (2003)
[3] Kim, S.; Nowozin, S.; Kohli, P.; Chang, D. Y., Higher-order correlation clustering for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 36, 1761-1774 (2015)
[4] Kass, M.; Witkin, A.; Terzopoulos, D., Snakes: active contour models, Int. J. Comput. Vis., 1, 321-331 (1988)
[5] Caselles, V.; Kimmel, R.; Sapiro, G., Geodesic active contours, Int. J. Comput. Vis., 22, 61-79 (1997) · Zbl 0894.68131
[6] Chan, T.; Vese, L., Active contours without edges, IEEE Trans. Image Proc., 10, 266-277 (2001) · Zbl 1039.68779
[7] Zhu, S. C.; Yuille, A., Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 18, 9, 884-900 (1996)
[8] Mumford, D.; Shah, J., Optimal approximation by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., 42, 577-685 (1989) · Zbl 0691.49036
[9] [9] C. Li, C. Y. Kao, J.C. Gore, Z. Ding, Implicit active contours driven by local binary fitting energy, IEEE Conf. Comput. Vis. Pattern Recognit. 7 (2007) Minneapolis.
[10] Vese, L.; Chan, T., A multiphase level set framework for image segmentation using the Mumford and Shah model, Int. J. Comput. Vis., 50, 271-293 (2002) · Zbl 1012.68782
[11] Li, C.; Kao, C. Y.; Gore, J. C.; Ding, Z., Minimization of region-scalable fitting energy for image segmentation, IEEE Trans. Image Proc., 17, 1940-1949 (2008) · Zbl 1371.94225
[12] Zhang, K.; Song, H.; Zhang, L., Active contours driven by local image fitting energy, Pattern Recognit., 43, 4, 1199-1206 (2010) · Zbl 1192.68624
[13] Wang, L.; Chang, Y.; Wang, H.; Wu, Z. Z.; Pu, J. T.; Yang, X. D., An active contour model based on local fitted images for image segmentation, Inf. Sci., 418-419, 61-73 (2017)
[14] Jia, Z. X.; Xia, Y.; Sun, Q. S.; Cao, G.; Chen, Q., Active contours driven by local likelihood image fitting energy for image segmentation, Inf. Sci., 301, 285-304 (2015)
[15] Miao, J. Q.; Huang, T. Z.; Zhou, X. B.; Wang, Y. G.; Liu, J., Image segmentation based on an active contour model of partial image restoration with local cosine fitting energy, Inf. Sci., 418-419, 61-73 (2017)
[16] Ding, K. Y.; Xiao, L. F.; Weng, G. R., Active contours driven by local pre-fitting energy for fast image segmentation, Pattern Recognit. Lett., 104, 29-36 (2018)
[17] Wang, X.; Huang, D.; Xu, H., An efficient local Chan-Vese model for image segmentation, Pattern Recognit., 43, 3, 603-618 (2010) · Zbl 1185.68817
[18] He, C. J.; Wang, Y.; Chen, Q., Active contours driven by weighted region-scalable fitting energy based on local entropy, Signal Process., 92, 587-600 (2012)
[19] Brox, T.; Cremers, D., On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional, Int. J. Comput. Vis., 84, 184-193 (2009) · Zbl 1477.68335
[20] Wang, H.; Liu, M., Active contours driven by local Gaussian distribution fitting energy based on local entropy, Int. J. Pattern Recognit. Artif. Intell., 27, 6, 1073-1089 (2013)
[21] Ali, H.; Badshah, N.; Chen, K.; Khan, G., A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity, Pattern Recognit., 51, 27-42 (2016) · Zbl 1394.68396
[22] Li, C.; Xu, C.; Gui, C.; Fox, M. D., Level set evolution without re-initialization: a new variational formulation, IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 1, 430-436 (2005)
[23] 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) · Zbl 1371.94226
[24] Krinidis, S.; Chatzis, V., Fuzzy energy-based active contour, IEEE Trans. Image Process., 18, 12, 2747-2755 (2009) · Zbl 1371.94200
[25] Shyu, K.; Pham, V.; Tran, T.; Lee, P., Global and local fuzzy energy-based active contours for image segmentation, Nonlinear Dynam., 67, 2, 1559-1578 (2012) · Zbl 1256.94014
[26] Mondal, A.; Ghosh, S.; Ghosh, A., Robust global and local fuzzy energy based active contour for image segmentation, Appl. Soft Comput., 47, 191-215 (2016)
[27] Mondal, A.; Ghosh, S.; Ghosh, A., Partially camouflaged object tracking using modified probabilistic neural network and fuzzy energy based active contour, Int. J. Comput. Vis., 122, 116-148 (2017)
[28] Sun, W. Y.; Dong, E. Q.; Qiao, H. J., A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation, SIViP, 12, 1, 91-98 (2018)
[29] Tran, T. T.; Pham, V. T.; Shyu, K. K., Image segmentation using fuzzy energy-based active contour with shape prior, J. Vis. Commun. Image R., 25, 1732-1745 (2014)
[30] Zhang, K.; Zhang, L.; Song, H.; Zhou, W., Active contours with selective local or global segmentation: a new formulation and level set method, Image Vision Comput., 28, 4, 668-676 (2010)
[31] Shan, X.; Kim, D.; Kobayashi, E.; Li, B. N., Regularized level set models using fuzzy clustering for medical image segmentation, Filomat, 32, 5, 1507-1512 (2018)
[32] Hanbay, K.; Talu, M. F., A novel active contour model for medical images via the Hessian matrix and eigenvalues, Comput. Math. Appl., 75, 3081-3104 (2018) · Zbl 1409.92131
[33] Fang, J.; Liu, H.; Zhang, L.; Liu, J.; Liu, H., Fuzzy region-based active contours driven by weighting global and local fitting energy, IEEE Access, 184518-184536 (2019)
[34] Fang, J.; Liu, H.; Zhang, L.; Liu, J.; Liu, H., Active contour driven by weighted hybrid signed pressure force for image segmentation, IEEE Access, 97492-97504 (2019)
[35] Zhang, H.; Fritts, J. E.; Goldman, S. A., An entropy-based objective evaluation method for image segmentation, Electron. Imag., 38-49 (2003)
[36] Liu, T.; Sun, J.; Zheng, N. N.; Tang, X.; Shum, H. Y., Learning to detect a salient object, IEEE CVPR (2007)
[37] Salah, M. B.; Mitiche, A.; Ayed, I. B., Efficient level set segmentation with a kernel induced data term, IEEE Trans. Image Proc., 19, 220-232 (2010) · Zbl 1371.68284
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