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A fractional-order image segmentation model with application to low-contrast and piecewise smooth images. (English) Zbl 1538.94003

Summary: In this paper, we propose a two-stage image segmentation model based on structure tensor and fractional-order regularization. In the first stage, we use the fractional-order regularization to approximate the Hausdorff measure of the Mumford-Shah (MS) model. The existence and uniqueness of the solution is proved and the alternating direction implicit (ADI) scheme is used to find the solution of the modified MS model. In the second stage, a thresholding is used to induce the segmentation of the target. The superior performances of the proposed model are demonstrated by some comparative experimental results with several state-of-art methods.

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68U10 Computing methodologies for image processing
65K05 Numerical mathematical programming methods
65D18 Numerical aspects of computer graphics, image analysis, and computational geometry
65M06 Finite difference methods for initial value and initial-boundary value problems involving PDEs
Full Text: DOI

References:

[1] Xie, W.; Liu, D.; Yang, M., SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation. Atmos. Meas. Tech., 4, 1953-1961 (2020)
[2] Rushing, J. A.; Ranganath, H.; Hinke, T. H., Image segmentation using association rule features. IEEE Trans. Image Process., 5, 558-567 (2002)
[3] Li, X.; Yu, L.; Chen, H., Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans. Neural Netw. Learn. Syst., 2, 523-534 (2020)
[4] Eelbode, T.; Bertels, J.; Berman, M., Optimization for medical image segmentation: theory and practice when evaluating with dice score or Jaccard index. IEEE Trans. Med. Imaging, 11, 3679-3690 (2020)
[5] Cholakkal, H.; Sun, G.; Khan, F. S., Object counting and instance segmentation with image-level supervision, 12397-12405
[6] Jiao, X.; Chen, Y.; Dong, R., An unsupervised image segmentation method combining graph clustering and high-level feature representation. Neurocomputing, 83-92 (2020)
[7] Badshah, N.; Chen, K.; Ali, H., Coefficient of variation based image selective segmentation model using active contours. East Asian J. Appl. Math., 02, 150-169 (2012) · Zbl 1284.68623
[8] Bramble, J.; Pasciak, J.; Vassilev, A., Analysis of the inexact Uzawa algorithm for saddle point problems. SIAM J. Numer. Anal., 3, 1072-1092 (1997) · Zbl 0873.65031
[9] Caselles, V.; Kimmel, R.; Sapiro, G., Geodesic active contours. Int. J. Comput. Vis., 61-79 (1997) · Zbl 0894.68131
[10] 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., 195-215 (2007)
[11] Gupta, D.; Anand, R., A hybrid edge-based segmentation approach for ultrasound medical images. Biomed. Signal Process. Control, 116-126 (2017)
[12] Chen, D.; Spencer, J.; Mirebeau, J. M., A generalized asymmetric dual-front model for active contours and image segmentation. IEEE Trans. Image Process., 5056-5071 (2021)
[13] Zhang, J.; Chen, K.; Gould, D. A., A fast algorithm for automatic segmentation and extraction of a single object by active surfaces. Int. J. Comput. Math., 6, 1251-1274 (2015) · Zbl 1310.94020
[14] Wu, C.; Tai, X. C., Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models. SIAM J. Imaging Sci., 3, 300-339 (2010) · Zbl 1206.90245
[15] Zhang, D.; Tai, X.; Lui, L. M., Topology- and convexity-preserving image segmentation based on image registration. Appl. Math. Model., 218-239 (2021) · Zbl 1481.68047
[16] Mumford, D.; Shah, J., Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math., 577-685 (1989) · Zbl 0691.49036
[17] Jauhiainen, J.; Seppänen, A.; Valkonen, T., Mumford-Shah regularization in electrical impedance tomography with complete electrode model. Inverse Probl., 6 (2022) · Zbl 1489.35307
[18] Klann, E.; Ramlau, R.; Ring, W., A Mumford-Shah level-set approach for the inversion and segmentation of SPECT/CT data. Inverse Probl. Imaging, 1, 137 (2011) · Zbl 1213.94015
[19] Ben-Ari, R.; Sochen, N., Stereo matching with Mumford-Shah regularization and occlusion handling. IEEE Trans. Pattern Anal. Mach. Intell., 11, 2071-2084 (2010)
[20] Ambrosio, L.; Tortorelli, V. M., Approximation of functional depending on jumps by elliptic functional via \(t\)-convergence. Commun. Pure Appl. Math., 8, 999-1036 (1990) · Zbl 0722.49020
[21] Chan, T.; Vese, L., Active contours without edges. IEEE Trans. Image Process., 266-277 (2001) · Zbl 1039.68779
[22] Spencer, J.; Chen, K., A convex and selective variational model for image segmentation. Commun. Math. Sci., 6, 1453-1472 (2015) · Zbl 1327.62387
[23] Zhang, J.; Chen, K.; Yu, B., A 3D multi-grid algorithm for the Chan-Vese model of variational image segmentation. Int. J. Comput. Math., 2, 160-189 (2012) · Zbl 1242.94006
[24] Vese, L.; Chan, T., A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis., 271-293 (2002) · Zbl 1012.68782
[25] Cai, X.; Chan, R.; Zeng, T., A two-stage image segmentation method using a convex variant of the Mumford-Shah model and thresholding. SIAM J. Imaging Sci., 1, 368-390 (2013) · Zbl 1283.52011
[26] Chan, R.; Yang, H.; Zeng, T., A two-stage image segmentation method for blurry images with Poisson or multiplicative gamma noise. SIAM J. Imaging Sci., 1, 98-127 (2014) · Zbl 1297.65066
[27] Yu, H.; Jiao, L.; Liu, F., CRIM-FCHO: SAR image two-stage segmentation with multifeature ensemble. IEEE Trans. Geosci. Remote Sens., 4, 2400-2423 (2015)
[28] Song, J.; Jiao, W.; Lankowicz, K., A two-stage adaptive thresholding segmentation for noisy low-contrast images. Ecol. Inform. (2022)
[29] Chen, X.; Zhou, W., Smoothing nonlinear conjugate gradient method for image restoration using nonsmooth nonconvex minimization. SIAM J. Imaging Sci., 4, 765-790 (2010) · Zbl 1200.65031
[30] Hintermüller, M.; Wu, T., Nonconvex \(T V^q\)-models in image restoration: analysis and a trust-region regularization-based superlinearly convergent solver. SIAM J. Imaging Sci., 3, 1385-1415 (2013) · Zbl 1281.65033
[31] Yao, Q.; Kwok, J. T.; Zhong, W., Fast low-rank matrix learning with nonconvex regularization, 539-548
[32] Wu, T.; Shao, J.; Gu, X., Two-stage image segmentation based on nonconvex \(l_2 - l_p\) approximation and thresholding. Appl. Math. Comput. (2021)
[33] Pang, Z. F.; Zhang, H. L.; Luo, S., Image denoising based on the adaptive weighted TVp regularization. Signal Process. (2020)
[34] Wu, T.; Gu, X.; Wang, Y., Adaptive total variation based image segmentation with semi-proximal alternating minimization. Signal Process. (2021)
[35] Demengel, F.; Demengel, G., Functional Spaces for the Theory of Elliptic Partial Differential Equations, 219-224 (2011), Springer
[36] Zhang, J.; Chen, K., Variational image registration by a total fractional-order variation model. J. Comput. Phys., 442-461 (2015) · Zbl 1349.94050
[37] Han, H.; Wang, Z., An alternating direction implicit scheme of a fractional-order diffusion tensor image registration model. Appl. Math. Comput., 105-118 (2019) · Zbl 1429.65184
[38] Zhang, J.; Chen, K., A total fractional-order variation model for image restoration with nonhomogeneous boundary conditions and its numerical solution. SIAM J. Imaging Sci., 4, 2487-2518 (2015) · Zbl 1327.62388
[39] Han, H., A tensor voting based fractional-order image denoising model and its numerical algorithm. Appl. Numer. Math., 133-144 (2019) · Zbl 1477.94011
[40] Yang, J.; Guo, Z.; Zhang, D., An anisotropic diffusion system with nonlinear time-delay structure tensor for image enhancement and segmentation. Comput. Math. Appl., 29-44 (2022) · Zbl 1538.94017
[41] Estellers, V.; Soatto, S.; Bresson, X., Adaptive regularization with the structure tensor. IEEE Trans. Image Process., 6, 1777-1790 (2015) · Zbl 1408.94166
[42] Li, D.; Zhang, C.; Ran, M., A linear finite difference scheme for generalized time fractional Burgers equation. Appl. Math. Model., 11-12, 6069-6081 (2016) · Zbl 1465.65075
[43] Zhang, Y.; Sun, Z., Error analysis of a compact ADI scheme for the 2D fractional subdiffusion equation. J. Sci. Comput., 1, 104-128 (2014) · Zbl 1304.65208
[44] Wang, X. F.; Huang, D.; Xu, H., An efficient local Chan-Vese model for image segmentation. Pattern Recognit., 3, 603-618 (2010) · Zbl 1185.68817
[45] Goldstein, T.; Bresson, X.; Osher, S., Geometric applications of the split Bregman method: segmentation and surface reconstruction. J. Sci. Comput., 272-293 (2010) · Zbl 1203.65044
[46] Wang, L.; Li, C.; Sun, Q. S.; Xia, D. S.; Kao, C. Y., Active contours driven by local and global intensity fitting energy with application to brain MR images segmentation. Comput. Med. Imaging Graph., 520-531 (2009)
[47] Pang, Z.; Guan, Z.; Li, Y.; Chen, K.; Ge, H., Image segmentation based on the hybrid bias field correction. Appl. Math. Comput. (2023) · Zbl 07702346
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