Segmentation of MR image using local and global region based geodesic model

BioMedical Engineering Online

Volumn 14, Issue 1, 2015, Pages

  Li, Xiuming ^a,b   Jiang, Dongsheng ^a,b   Shi, Yonghong ^a,b   Li, Wensheng ^a,b  

^a FUDAN UNIVERSITY   (China)

^b Fudan University   (China)

Author keywords

Global image information; Intensity inhomogeneity; Level set method; Local image information

Indexed keywords

GEODESY; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; NUMERICAL METHODS;

COMPUTATIONALLY EFFICIENT; GLOBAL IMAGE INFORMATION; GLOBAL INFORMATIONS; IMAGE INFORMATION; INTENSITY INFORMATION; INTENSITY INHOMOGENEITY; LEVEL SET METHOD; SIGNED PRESSURE FORCES;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; DIAGNOSTIC IMAGING; GEODESIC ACTIVE CONTOUR MODEL; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SIGNED PRESSURE FORCE; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; BRAIN; HAND; HUMAN; IMAGING PHANTOM; MAGNETIC RESONANCE ANGIOGRAPHY; PROCEDURES; THEORETICAL MODEL; VASCULARIZATION;

ALGORITHMS; BRAIN; HAND; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE ANGIOGRAPHY; MAGNETIC RESONANCE IMAGING; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; PHANTOMS, IMAGING;

EID: 84929310304     PISSN: None     EISSN: 1475925X     Source Type: Journal    
DOI: 10.1186/1475-925X-14-8     Document Type: Article

References (21)

1. Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. Int J Comput Vision 1988,1(4):321-31. 10.1007/BF00133570
2. Lankton S, Tannenbaum A: Localizing region-based active contours. IEEE Trans Image Process 2008,17(11):2029-39.
3. Caselles V, Kimmel R, Sapiro G: Geodesic active contours. Int J Comput Vision 1997,22(1):61-79. 10.1023/A:1007979827043
4. Goldenberg R, Kimmel R, Rivlin E, Rudzsky M: Fast geodesic active contours. IEEE Trans Image Process 2001,10(10):1467-75. 10.1109/83.951533
5. Zhang K, Zhang L, Song H, Zhou W: Active contours with selective local or global segmentation: a new formulation and level set method. Image Vision Comput 2010,28(4):668-76. 10.1016/j.imavis.2009.10.009
6. Song H: Active contours driven by regularised gradient flux flows for image segmentation. Electron Lett 2014,50(14):992-4. 10.1049/el.2014.1710
7. Chan T, Vese L: Active contours without edges. IEEE Trans Image Process 2001,10(2):266-77. 10.1109/83.902291
8. Li C, Kao C, Gore JC, Ding Z: Implicit active contours driven by local binary fitting energy. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2007, 1-7.
9. Li C, Kao C, Gore JC, Ding Z: Minimization of region-scalable fitting energy for image segmentation. I IEEE Trans Image Process 2008,17(10):1940-9.
10. Zhang K, Liu Q, Song H, Li X: A Variational Approach to Simultaneous Image Segmentation and Bias Correction. 2014.
11. He L, Zheng S, Wang L: Integrating local distribution information with level set for boundary extraction. J Vis Commun Image Represent 2010,21(4):343-54. 10.1016/j.jvcir.2010.02.009
12. Wang L, Li C, Sun Q, Kao C: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imag Grap 2009,33(7):520-31. 10.1016/j.compmedimag.2009.04.010
13. Aubert G, Kornprobst P: Mathematical problems in image processing: partial differential equations and the calculus of variations. Springer; 2006.
14. Li C, Huang R, Ding Z, Gatenby J, Metaxas DN, Gore JC: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 2011,20(7):2007-16.
15. Xu C, Yezzi A Jr, Prince JL: On the relationship between parametric and geometric active contours. Proceedings of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, vol 1 2000, 483-489.
16. Yu Y, Zhang C, Wei Y, Li X: Active contour method combining local fitting energy and global fitting energy dynamically. Proceedings of the International Conference on Medical Biometrics, vol LNCS 6165163-72.
17. Vese L, Chan T: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vision 2002,50(3):271-93. 10.1023/A:1020874308076
18. Chung G, Dinov I, Toga A, LA V: MRI tissue segmentation using a variational multilayer approach, Department of Mathematics, UCLA, CAM Report,08-54. 2008.
19. Internet Brain Segmentation Repository. http://www.nitrc.org/projects/ibsr/
20. Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 2001,13(5):856-76. 10.1006/nimg.2000.0730
21. Valverde S, Oliver A, Cabezas M, Roura E, Lladó X: Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations. J Magn Reson Imaging 2015, 41:93-101. 10.1002/jmri.24517