Abstract
Image segmentation plays an important role in many medical imaging systems, yet in complex circumstances it remains an open problem. One of the main difficulties is the intensity inhomogeneity in an image. In order to tackle this problem, we first introduce a region-based level set segmentation framework to unify the traditional global and local methods. We then propose two novel parameter priors, i.e., the local order regularization and interactive regularization, and then utilize them as the constraints of the objective energy function. The objective energy function is finally minimized via a level set evolution process to achieve image segmentation. Extensive experiments show that the proposed approach has gained significant improvements in both accuracy and efficiency over the state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Caselles V, Kimmel R, Sapiro G. Geodesic active contours. Int J Comput Vis, 1997, 22: 61–79
Yezzi A, Kichenassamy S, Kumar A, et al. A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imaging, 1997, 16: 199–209
Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: A new variational formulation. In: Proceedings of IEEE Conf on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2005. 430–436
Chan T F, Vese L A. Active contours without edges. IEEE Trans Image Process, 2001, 10: 266–277
Tsai A, Anthony Y J, Willsky A S. Curve evolution implementation of the mumfordcshah functional for image segmentation, de-noising, interpolation, and magnification. IEEE Trans Image Process, 2001, 10: 1169–1186
Vese L A, Chan T F. A multi-phase level set framework for image segmentation using the mumford and shah model. Int J Comput Vis, 2002, 50: 271–293
An J, Rousson M, Xu C. γ-convergence approximation to piecewise smooth medical image segmentation. In: Proceedings of Int Conf on Medical Image Computing and Computer Assisted Intervention (Part II). Berlin: Springer, 2007. 495–502
Li C, Kao C Y, Gore J C, et al. Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process, 2008, 17: 1940–1949
Wang L, Macione J, Sun Q, et al. Level set segmentation based on local gaussian distribution fitting. In: Proceedings of Asian Conf on Computer Vision. Berlin: Springer, 2009. 293–302
Liu H, Chen Y, Chen W. Neighborhood aided implicit active contours. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2006. 841–848
Lie J, Lysaker M, Tai X. A binary level set model and some applications to mumford-shah image segmentation. IEEE Trans Image Process, 2006, 15: 1171–1181
Ronfard R. Region-based strategies for active contour models. Int J Comput Vis, 1994, 13: 229–251
Paragios N, Deriche R. Geodesic active regions and level set methods for supervised texture segmentation. Int J Comput Vis, 2002, 46: 223–247
Shen C, Yu H, Liu X, et al. Medical image segmentation based on level set with new local fitting energy. In: Proceedings of IEEE Conf on Medical Image Analysis and Clinical Applications. Los Alamitos: IEEE Computer Society Press, 2010. 34–37
Chiu Y J, Pham V T, Tran T T, et al. Evaluation of active contour on medical inhomogeneous image segmentation. In: Proceedings of IEEE Conf on Computer Science and Information Technology. Los Alamitos: IEEE Computer Society Press, 2010. 311–314
Zhang K, Song H, Zhang L. Active contours driven by local image fitting energy. Pattern Recognit, 2010, 43: 1199–1206
Mao H, Liu H, Shi P. Neighbor-constrained active contours without edges. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops. Los Alamitos: IEEE Computer Society Press, 2008. 1–7
Chen Y, Zhang J, Macione J. An improved level set method for brain MR images segmentation and bias correction. Comput Med Imaging Graph, 2009, 33: 510–519
Zhai Y, Wu B, Zhang D, et al. A new variational model for segmenting objects of interest from color images. Math Probl Eng, 2010, 2010: 1–16
Boykov Y Y, Jolly M P. Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proceedings of IEEE Int Conf on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2001. 105–112
Grady L. Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2006, 28: 1768–1783
Xiang S, Nie F, Zhang C, et al. Interactive natural image segmentation via spline regression. IEEE Trans Image Process, 2009, 18: 1623–1632
Xiang S, Nie F, Zhang C. Semi-supervised classification via local spline regression. IEEE Trans Pattern Anal Mach Intell, 2010, 32: 2039–2053
Mao H, Liu H, Shi P. A convex neighbor-constrained active contour model for image segmentation. In: Proceedings of Int Conf on Image Processing. Los Alamitos: IEEE Computer Society Press, 2010. 793–796
Nikolova M, Esedoglu S, Chan T F. Algorithms for finding global minimizers of image segmentation and de-noising models. SIAM J Appl Math, 2006, 66: 1632–1648
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, L., Yu, Z. & Pan, C. A unified level set framework utilizing parameter priors for medical image segmentation. Sci. China Inf. Sci. 56, 1–14 (2013). https://doi.org/10.1007/s11432-012-4683-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-012-4683-7