3D segmentation of rodent brains using deformable models and variational methods

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

Volumn , Issue , 2009, Pages 94-100

  Zhang, Shaoting ^a   Zhou, Jinghao ^a   Wang, Xiaoxu ^a   Chang, Sukmoon ^a   Metaxas, Dimitris N ^a   Pappas, George ^b   Delis, Foteini ^b   Volkow, Nora D ^b   Wang, Gene Jack ^b   Thanos, Panayotis K ^b   Kambhamettu, Chandra ^c  

^a RUTGERS UNIVERSITY   (United States)

^b BROOKHAVEN NATIONAL LABORATORY   (United States)

^c University of Delaware   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; BRAIN MAPPING; COMPUTER VISION; MAGNETIC RESONANCE IMAGING; TOPOLOGY; VARIATIONAL TECHNIQUES;

ACTIVE SHAPE MODEL; ANALYSIS OF VARIOUS; AUTOMATIC SEGMENTATIONS; DEFORMABLE MODELS; PRIORI KNOWLEDGE; SEGMENTATION METHODS; TOPOLOGICAL CHANGES; VARIATIONAL METHODS;

IMAGE SEGMENTATION;

EID: 70449572264     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2009.5204051     Document Type: Conference Paper

References (20)

1. A. Pitiot, H. Delingette, and P. Thompson, Medical Image Systems: Technology and Applications, ch. Automated Image Segmentation: Issues and Applications. Academic Press, 2004.
2. V. Boronikolas, M. Michaelides, J. Zhou, G. Wang, S. Blackband, S. Grant, D. Metaxas, N. Volkow, and P. Thanos, "Validation of the active shape model for automatic brain region segmentation," IEEE Nuclear Science Symposium and Medical Imaging Conference, 2006.
3. R. McCarley, C. Wiblea, M. Frumina, Y. Hirayasua, J. Levitta, I. Fischera, and M. Shenton, "Mri anatomy of schizophrenia," Biological Psychiatry, vol.45, no.9, pp. 1099-1119, 1999.
4. O. Carmichael, H. Aizenstein, S. Davis, J. Becker, P. Thompson, C. Meltzer, and Y. Liu, Atlas-based hippocampus segmentation in alzheimer's disease and mild cognitive impairment, NeuroImage, vol.27, no.4, pp. 979-990, 2005. (Pubitemid 41317265)
5. F. Delis, Y. Ma, H. Benvenste, D. Grandy, G. Wang, N. Volkow, and P. Thanos, "Effects of chronic alcohol intake and dopamine d2 receptor gene expression on brain anatomy: an in-vivo mri volumetric study of the mouse brain," in Society for Neuroscience, 2008.
6. I. Rabkin, F. Delis, Y Ma, S. Grant, G. Wang, N.D.Volkow, J. Shih, and P. Thanos, "Monoamine oxidase a gene knockout mouse brain anatomyian in-vitro mri volumetric study," in Society for Neuroscience, 2008.
7. A. Pitiot, H. Delingette, P. Thompson, and A. N., "Expert knowledge-guided segmentation system for brain mri," NeuroImage, vol.23, pp. s85-s96, 2004.
8. M. Leventon, E. Grimson, and O. Faugeras, "Statistical shape influence in geodesic active contours," IEEE Proc. of Computer Vision and Pattern Recognition, pp. 4-11, 2000.
9. V. Barra and J. Boire, "Automatic segmentation of subcortical brain structures in mr images using information fusion," IEEE transctions on medical imaging, vol.20, no.7, pp. 549-558, 2001.
10. W. Niessen, K. Vincken, J. Weickert, B. Romeny, and M. Viergever, "Multiscale segmentation of threedimensional mr brain images," International Journal of Computer Vision, vol.31, no.2, pp. 185-202, 1999.
11. D. Shen, E. Herskovies, and C. Davatzikos, An adaptivefocus statistical shape model for segmentation and shape modeling of 3d brain structures, IEEE Transactions on Medical Imaging, vol.20, no.4, pp. 257-270, 2001. (Pubitemid 32536135)
12. J. Zhou, S. Chang, Q. Liu, G. Pappas, V. Boronikolas, M. Michaelides, N. Volkow, P. Thanos, and D. Metaxas, "A novel learning based segmentation method for rodent brain structures using mri," IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 61-64, 2008.
13. T. Cootes, C. Taylor, D. Cooper, and J. Graham, "Active shape models-their training and application," Computer Vision and Image Understanding, vol.61, no.1, pp. 38-59, 1995.
14. X. Huang, D. Metaxas, and T. Chen, "Metamorphs: Deformable shape and texture models," in IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR, 2004.
15. X. Huang and D. Metaxas, "Metamorphs: Deformable shape and appearance models," TPAMI, vol.30, no.8, pp. 1444-1459, 2008.
16. J. Zhou, S. Chang, S. Zhang, G. Pappas, M. Michaelides, F. Delis, N. Volkow, P. Thanos, and D. Metaxas, "Segmentation of rodent brains from mri based on a novel statistical structure prediction method," in IEEE International Symposium on Bomedical Imaging (ISBI), 2009.
17. G. Turk and J. F. O'Brien, "Shape transformation using variational implicit functions," in The Proceedings of ACM SIGGRAPH 99, pp. 335-342, 1999.
18. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York, 2001.
19. S. Zhou, J. Zhou, B. Georgescu, and D. Comaniciu, "A boosting regression approach to medical anatomy detection," CVPR, pp. 1-8, 2007.
20. F. De La Torre and M. Black, "A framework for robust subspace learning," International Journal of Computer Vision, vol.54, no.1-3, pp. 117-142, 2003.