Jayadevappa D, Kumar S S, Murty D S. Medical image segmentation algorithms using deformable models: a review. IETE Technical Review, 2011, 28(3): 248-255
[2]
Caselles V, Kimmel R, Sapiro G. Geodesic active contours. International Journal of Computer Vision, 1997, 22(1): 61-79
[3]
Zhu G P, Zhang S Q, Zeng Q S, Wang C H. Boundary-based image segmentation using binary level set method. Optical Engineering, 2007, 46(5): 0505011-0505013
[4]
Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 1989, 42(5): 577-685
[5]
Lie J, Lysaker M, Tai X C. A binary level set model and some applications to Mumford-Shah image segmentation. IEEE Transactions on Image Processing, 2006, 15(5): 1171-1181
[6]
Li C M, Kao C Y, Gore J C, Ding Z H. Implicit active contours driven by local binary fitting energy. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007. 1-9
[7]
Zhang K H, Song H H, Zhang L. Active contours driven by local image fitting energy. Pattern Recognition, 2010, 43(4): 1199-1206
[8]
Zhang K H, Zhang L, Zhang S. A variational multiphase level set approach to simultaneous segmentation and bias correction. In: Proceedings of 17th IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010. 4105-4108
[9]
Mille J. Narrow band region-based active contours and surfaces for 2D and 3D segmentation. Computer Vision and Image Understanding, 2009, 113(9): 946-965
[10]
Shi Y G, Karl W C. A real-time algorithm for the approximation of level set-based curve evolution. IEEE Transactions on Image Processing, 2008, 17(5): 645-656
[11]
Zhang K H, Zhang L, Song H H, Zhou W G. Active contours with selective local or global segmentation: a new formulation and level set method. Image and Vision Computing, 2010, 28(4): 668-676
[12]
Li W B, Song S H, Qian X. Active contours with selective local or global segmentation property for multiobject image. Optical Engineering, 2011, 50(6): 067009
[13]
Liu J, Tai X C, Huang H Y, Huan Z D. A fast segmentation method based on constraint optimization and its applications: intensity inhomogeneity and texture segmentation. Pattern Recognition, 2011, 44(9): 2093-2108
[14]
Guo Z H, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 2010, 19(6): 1657-1663
[15]
Law Y N, Lee H K, Yip A M. Subspace learning for Mumford-Shah-model-based texture segmentation through texture patches. Applied Optics, 2011, 50(21): 3947-3957
[16]
Riklin-Raviv T, Kiryati N, Sochen N. Segmentation by level sets and symmetry. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006. 1015-1022
[17]
Wang H, Dong L, O'Daniel J, Mohan R, Garden A S, Ang K K, Kuban D A, Bonnen M, Chang J Y, Cheung R. Validation of an accelerated ''demons'' algorithm for deformable image registration in radiation therapy. Physics in Medicine and Biology, 2005, 50(12): 2887-2905
[18]
Cremers D, Rousson M, Deriche R. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision, 2007, 72(2): 195-215
[19]
Houhou N, Thiran J P, Bresson X. Fast texture segmentation based on semi-local region descriptor and active contour. Numerical Mathematics Theory Methods and Applications, 2009, 2(4): 445-468
[20]
Caselles V, Kimmel R, Sapiro G. Geodesic active contours. In: Proceedings of the 1995 International Conference on Computer Vision. Massachusetts, USA: IEEE, 1995. 694-699
[21]
Li C M, Xu C Y, Gui C F, Fox M D. Level set evolution without re-initialization: a new variational formulation. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 430-436
[22]
Li C M, Xu C Y, Gui C F, Fox M D. Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on Image Processing, 2010, 19(12): 3243-3254
[23]
Chan T F, Vese L A. Active contours without edges. IEEE Transactions on Image Processing, 2001, 10(2): 266-277
[24]
Vese L A, Chan T F. A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 2002, 50(3): 271-293
[25]
Goudail F, Réfrégier P. Target segmentation in active polarimetric images by use of statistical active contours. Applied Optics, 2002, 41(5): 874-883
[26]
Li C M, Kao C Y, Gore J C, Ding Z H. Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 2008, 17(10): 1940-1949
[27]
Wang X F, Huang D S, Xu H. An efficient local Chan-Vese model for image segmentation. Pattern Recognition, 2010, 43(3): 603-618
[28]
Li C M, Huang R, Ding Z H, Gatenby C, Metaxas D, Gore J. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. In: Proceedings of 11th International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg: IEEE, 2008. 1083-1091
[29]
Li C M, Huang R, Ding Z H, Gatenby J C, Metaxas D N, Gore J C. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on Image Processing, 2011, 20(7): 2007-2016
[30]
Lankton S, Tannenbaum A. Localizing region-based active contours. IEEE Transactions on Image Processing, 2008, 17(11): 2029-2039
[31]
Wang Y, Lin Z X, Cao J G, Li M Q. Automatic MRI brain tumor segmentation system based on localizing active contour models. In: Proceedings of the 2011 International Conference on Information Science, Automation and Material System. Zhengzhou, China: IEEE, 2011. 1342-1346
[32]
Burkert F, Butenuth M, Ulrich M. Real-time object detection with sub-pixel accuracy using the level set method. The Photogrammetric Record, 2011, 26(134): 154-170
[33]
Tran T T, Pham V T, Chiu Y J, Shyu K K. Active contour with selective local or global segmentation for intensity inhomogeneous image. In: Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology. Chengdu, China: IEEE, 2010. 306-310
