Adaptive Shape Prior Constrained Level Sets for Bladder MR Image Segmentation

IEEE Journal of Biomedical and Health Informatics

Volumn 18, Issue 5, 2014, Pages 1707-1716

  Qin, Xianjing ^a   Li, Xuelong ^a   Liu, Yang ^b   Lu, Hongbing ^b   Yan, Pingkun ^a  

^a XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^b FOURTH MILITARY MEDICAL UNIVERSITY   (China)

Author keywords

Adaptive shape prior (ASP); coupled level sets; directional gradient; segmentation

Indexed keywords

MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING;

BLADDER WALL; INTENSITY DISTRIBUTION; LEVEL SET; LEVEL-SET MODEL; MINIMUM THICKNESS; PRIOR KNOWLEDGE; REGION INFORMATION; SHAPE PRIORS;

IMAGE SEGMENTATION;

ARTIFACT; BLADDER; BLADDER WALL; CLINICAL ARTICLE; CONTROLLED STUDY; HUMAN; MODEL; THICKNESS; ALGORITHM; ANATOMY AND HISTOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; THREE DIMENSIONAL IMAGING;

ALGORITHMS; HUMANS; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; URINARY BLADDER;

EID: 84917679292     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2013.2288935     Document Type: Article

References (25)

1. American Cancer Society, Cancer Facts & Figures 2012. Atlanta, GA, USA: Amer. Cancer Soc., 2012
2. D. Chen, B. Li,W. Huang, and Z. Liang, "A multi-scan MRI-based virtual cystoscopy," Proc. SPIE, vol. 3978, pp. 146-152, 2000.
3. S. Jaume, M. Ferrant, B. Macq, L. Hoyte, J. Fielding, A. Schreyer, R. Kikinis, and S. Warfield, "Tumor detection in the bladder wall with a measurement of abnormal thickness in CT scans," IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 383-390, Mar. 2003.
4. H. C. van Assen, M. G. Danilouchkine, M. S. Dirksen, J. H. Reiber, and B. P. Lelieveldt, "A 3-d active shape model driven by fuzzy inference: Application to cardiac CT and MR," IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 5, pp. 595-605, Sep. 2008.
5. P. Yan, S. Xu, B. Turkbey, and J. Kruecker, "Adaptively learning local shape statistics for prostate segmentation in ultrasound," IEEE Trans. Biomed. Eng., vol. 58, no. 3, pp. 633-641, Mar. 2011.
6. Y. Cao, Y. Yuan, X. Li, B. Turkbey, P. Choyke, and P. Yan, "Segmenting images by combining selected atlases on manifold," in Proc. Med. Image Comput. Comput.-Assist. Interven., 2011, pp. 272-279.
7. S. Zhang, Y. Zhan, M. Dewan, J. Huang, D. N. Metaxas, and X. S. Zhou, "Towards robust and effective shape modeling: Sparse shape composition," Med. Image Anal., vol. 16, no. 1, pp. 265-277, 2012.
8. S. Zhang, Y. Zhan, and D. N. Metaxas, "Deformable segmentation via sparse representation and dictionary learning," Med. Image Anal., vol. 16, pp. 1385-1396, 2012.
9. G. Chen, L. Gu, L. Qian, and J. Xu, "An improved level set for liver segmentation and perfusion analysis in MRIs," IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 1, pp. 94-103, Jan. 2009.
10. C. Li, R. Huang, Z. Ding, J. Gatenby, D. Metaxas, and J. Gore, "A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI," IEEE Trans. Image Process., vol. 20, no. 7, pp. 2007-2016, Jul. 2011.
11. C. Duan, Z. Liang, S. Bao, H. Zhu, S. Wang, G. Zhang, J. Chen, and H. Lu, "A coupled level set framework for bladder wall segmentation with application to MR Cystography," IEEE Trans. Med. Imag., vol. 29, no. 3, pp. 903-915, Mar. 2010.
12. J. Chi, M. Brady, N. Moore, and J. Schnabel, "Segmentation of the bladder wall using coupled level set methods," in Proc. IEEE Int. Symp. Biomed. Imag.: Nano Macro, 2011, pp. 1653-1656.
13. Z. Ma, R. Jorge, T. Mascarenhas, and J. Tavares, "Novel approach to segment the inner and outer boundaries of the bladder wall in T2-weighted magnetic resonance images," Ann. Biomed. Eng., vol. 39, pp. 1-11, 2011.
14. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," Int. J. Comput. Vis., vol. 22, no. 1, pp. 61-79, 1997.
15. C. Li, C. Xu, C. Gui, and M. Fox, "Level set evolution without reinitialization: A new variational formulation," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005, vol. 1, pp. 430-436.
16. T. Chan and L. Vese, "Active contours without edges," IEEE Trans. Image Process., vol. 10, no. 2, pp. 266-277, Feb. 2001.
17. X. Qin, Y. Liu, H. Lu, X. Li, and P. Yan, "Coupled directional level set for MR image segmentation," in Proc. IEEE 11th Int. Conf. Mach. Learn. Appl., 2012, vol. 1, pp. 185-190.
18. G. Zhu, S. Zhang, Q. Zeng, and C. Wang, "Directional geodesic active contour for image segmentation," J. Electron. Imag., vol. 16, no. 3, pp. 030501-030501, 2007.
19. C. Li, C. Xu, C. Gui, andM. Fox, "Distance regularized level set evolution and its application to image segmentation," IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243-3254, Dec. 2010.
20. S. Osher and J. Sethian, "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations," J. Computat. Phys., vol. 79, no. 1, pp. 12-49, 1988.
21. B. Hamm, G. Krestin, M. Laniado, V. Nicolas, and M. Taupitz, MR Imaging of the Abdomen and Pelvis. New York, NY, USA: Thieme Medical Pub, 2009.
22. S. Paramasivam, A. Proietto, and M. Puvaneswary, "Pelvic anatomy and MRI," Best Pract. Res. Clin. Obstetr. Gynaecol., vol. 20, no. 1, pp. 3-22, 2006.
23. J. W. Chi, M. Brady, N. R. Moore, and J. A. Schnabel, "Fusion of perpendicular anisotropic MRI sequences," in Proc. IEEE Int. Symp. Biomed. Imag.: Nano Macro, 2011, pp. 1455-1458.
24. M.Kuklisova-Murgasova, G. Quaghebeur, J.V.Hajnal, and J.A. Schnabel, "Unified framework for super-resolution reconstruction of 3D fetal brain MR images from 2D slices with intensity correction and outlier rejection," in Proc. MICCAI Workshop Image Anal. Human Brain Development, 2011.
25. C. Xu, D. L. Pham, and J. L. Prince, "Image segmentation using deformable models," in Handbook of Medical Imaging, M. Sonka and J. M. Fitzpatrick Eds., ch. 3, SPIE 2000, vol. 2, pp. 129-174.