Reference
- Zhong SZ, Li H, Lin ZK. Digitized virtual human: background and meaning. China Basic Sci. 2002;6:12–16.
- Zhong SZ. Scientific significance and prospective application of digitized virtual human. Acad J First Med Coll PLA. 2003;23:193–195.
- Shan J, Wang G, Wang D. Research of medical image fusion based on data set of digital visible human. Chin J Med Imaging Technol. 2009;25(1):141–144.
- Li H, Gu H, Han Y, et al. Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. Int J Remote Sens. 2010;31:1453–1470.
- Jayaram K, Udupa Punam K. Saha Fuzzy connectedness and image segmentation. Proc IEEE. 2003;91:1649–1669.
- Dhanachandra N, Manglem K, Chanu YJ. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci. 2015;54:764–771.
- Zheng Y, Jeon B, Xu D, et al. Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Int Fuzzy Syst. 2015;28:961–973.
- Hore S, Chakraborty S, Chatterjee S, et al. An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. IJECE. 2016;6:2773–2780.
- Wang T, Ji Z, Sun Q, et al. Image segmentation based on weighting boundary information via graph cut. J Vis Commun Image Represent. 2015;33:10–19.
- Zhang YD, Chen S, Wang SH, et al. Magnetic resonance brain image classification based on weighted‐type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol. 2015;25:317–327.
- Jian M, Jung C. Interactive image segmentation using adaptive constraint propagation. IEEE Trans Image Process. 2016;25:1301–1311.
- Tang M, Gorelick L, Veksler O, et al. GrabCut in one cut. Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney (Australia): IEEE Computer Society; 2013. p. 1769–1776.
- Boykov YY, Jolly MP. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. IEEE International Conference on Computer Vision. Los Alamitos (CA): IEEE Computer Society; 2001. p. 105.
- Rother C, Kolmogorov V, Blake A. Interactive foreground extraction using iterated graph cuts. ACM Trans Graph. 2004;23:309–314.
- Levin A, Lischinski D, Weiss Y. A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell. 2008;30:228–242.
- Wang J, Cohen MF. An iterative optimization approach for unified image segmentation and matting. Tenth IEEE International Conference on Computer Vision. Washington, DC: IEEE Computer Society. 2005. p. 936–943.
- Wang J-S, Gan Q, Wei Y, et al. Cellular neural networks with opposite‐sign templates for image thinning. Int J Circ Theor Appl. 1999;27:229–240.
- Saeed K, Rybnik M, Tabędzki M. A criterion for image thinning: implementation and applications. Image Process Commun. 2001;7:77–83.
- Au OKC, Tai CL, Chu HK. Skeleton extraction by mesh contraction. ACM Trans Graph. 2008;27:44.