An intensity consistent approach to the cross sectional analysis of deformation tensor derived maps of brain shape

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2488, Issue , 2002, Pages 492-499

  Studholme, C ^a   Cardenas, V ^a   Maudsley, A ^a   Weiner, M ^a  

^a SAN FRANCISCO VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; DEFORMATION; MEDICAL COMPUTING; MEDICAL IMAGING; TENSORS; TISSUE;

CROSS SECTIONAL ANALYSIS; DEFORMATION TENSORS; GAUSSIAN FILTERING; MEASURE OF UNCERTAINTY; MULTI-SUBJECT STUDIES; SPATIAL FILTERINGS; STATISTICAL FRAMEWORK; TISSUE SEGMENTATION;

INFORMATION FILTERING;

EID: 84958539963     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45786-0_61     Document Type: Conference Paper

References (14)

1. J. Ashburner, C. Hutton, R. Frackowiak, I. Johnsrude, C. Price, and K.J. Friston. Identifying global anatomical differences: Deformation-based morphometry. Neuroimage, 6:348-357, 1998.
2. G.E. Christensen and H.J. Johnson. Consistent image registration. Transactions on Medical Imaging, pages 568-582, 2001.
3. M. K. Chung, K. J. Worsley, T. Paus, C. Cherif, D. L. Collins, J. N. Giedd, J. L. Rapoport, and A. C. Evans. A unified statistical approach to deformation-based morphometry. Neuroimage, 14:596-606, 2001.
4. J.G. Csernansky, L. Wang, S. Joshi, J.P. Miller, M. Gado, D. Kido, D. McKeel, J.C. Morris, and MI. Miller. Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Neurology, 55(11):1636-1643, 2000.
5. C. Davatzikos, M. Vaillant, S.M. Resnick, J.L. Prince, S. Letovsky, and R.N. Bryan. A computerised approach for morphological analysis of the corpus callosum. Journal of Computer Assisted Tomography, 20(1):88-97, 1996.
6. C. Gaser, H.P. Voltz, S. Kiebel, S. Riehemann, and H. Sauer. Detecting structural changes in whole brain based on nonlinear deformations- application to schizophrenia research. Neuroimage, 10:107-113, 1999.
7. J.S. Lee. Digital image smoothing and the sigma filter. Computer Vision, Graphics and Image Processing, 24(2):255-269, 1983.
8. T. E. Nichols and A. P. Holmes. Nonparametric permutation tests for functional neuroimagin: A primer with examples. Human Brain Mapping, 15:1-25, 2001.
9. D.J. Pettey and J.C. Gee. Using a linear diagnostic function and non-rigid registration to search for morphological differences between poluations: An example involving the male and female corpus callosum. In Proceedings SPIE Medical Imaging, volume 4322, page In Press, 2001.
10. C. Studholme, V. Cardenas, N. Schuff, H. Rosen, B. Miller, and M. Weiner. Detecting spatially consistent structural differences in alzheimer’s and fronto temporal dementia using deformation morphometry. In Proceedings of 4th International Conference on Medical Image Computing and Computer Assisted Interventions, 2001, pages 41-48, 2001.
11. C. Studholme, V. Cardenas, and M. Weiner. Building whole brain maps of atrophy from multi-subject longitudinal studies using free form deformations. In Proceedings of ISMRM 2001, Glasgow, Scotland, 2001.
12. C. Studholme, V. Cardenas, and M. Weiner. Multi scale image and multi scale deformation of brain anatomy for building average brain atlases. In Proceedings SPIE Medical Imaging, volume 4322-4360, pages 557-568, 2001.
13. J.P. Thirion and G. Calmon. Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences. IEEE Transactions on Medical Imaging, 18(5):429-441, 1999.
14. P. Thompson, R. Woods, M. Mega, and A. Toga. Mathematical/Computational challenges in creating deformable and probabilistic atlases of the human brain. Human Brain Mapping, 9:81-92, 2000.