Double regularization control based on level set evolution for tablet packaging image segmentation

Abstract

This paper proposes a novel double regularization control (DRC) method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.

Keywords

Tablet packaging image / Level set evolution / Image segmentation / Curvatures / Double regularization control (DRC)

References

[1] Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phy, 1988, 79(1): 12-49.

[2] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis, 1988, 1(4): 321-331.

[3] Xu C, Prince JL. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process, 1998, 7(3): 359-369.

[4] Caselles V, Catté F, Coll T, et al. A geometric model for active contours in image processing. Numeri Math, 1993, 66(1): 1-31.

[5] Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell, 1995, 17(2): 158-175.

[6] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. Int J Comput Vis, 1997, 22(1): 61-79.

[7] Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: a new variational formulation. IEEE Comput Soc Conf Comput Vis Pattern Recognit, 2005, 1: 430-436.

[8] Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process, 2001, 10(2): 266-277.

[9] Vemuri B, Chen Y (2003) Joint image registration and segmentation. In: Geometric level set methods in imaging, vision, and graphics. Springer, Berlin, 251–269

[10] Wang X, Min H, Zou L, et al. A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement. Pattern Recognit, 2015, 48(1): 189-204.

[11] Michailovich O, Rathi Y, Tannenbaum A. Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans Image Process, 2007, 16(11): 2787-2801.

[12] Zhou W, Fei M, Li K, et al. Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering. Trans Inst Meas Control, 2014, 36(4): 465-477.

[13] Zhou W, Fei M, Zhou H, et al. A sparse representation based fast detection method for surface defect detection of bottle caps. Neurocomputing, 2014, 123(10): 406-414.

[14] Zhou H, Yuan Y, Lin F, et al. Level set image segmentation with Bayesian analysis. Neurocomputing, 2008, 71(10): 1994-2000.

[15] Zhou H, Schaefer G, Celebi ME, et al. Gradient vector flow with mean shift for skin lesion segmentation. Comput Med Imaging Graph, 2011, 35(2): 121-127.

[16] Zhou H, Li X, Schaefer G, et al. Mean shift based gradient vector flow for image segmentation. Comput Vis Image Underst, 2013, 117(9): 1004-1016.

[17] Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math, 1989, 42(5): 577-685.

[18] Vese LA, Chan TF. A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vis, 2002, 50(3): 271-293.

[19] Li C, Kao CY, Gore JC, et al. Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process, 2008, 17(10): 1940-1949.

[20] Li C, Xu C, Gui C, et al. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process, 2010, 19(12): 3243-3254.

[21] Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging, 2007, 26(3): 405-421.