Discovering modes of an image population through mixture modeling

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

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 381-389

  Sabuncu, Mert R ^a   Balci, Serdar K ^a   Golland, Polina ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TRANSFORMATIONS; MEDICAL COMPUTING; MEDICAL IMAGING; MIXTURES; NEURODEGENERATIVE DISEASES;

FAST AND EFFICIENT ALGORITHMS; HEALTHY CONTROLS; NON-LINEAR TRANSFORMATIONS; PATIENTS WITH DEMENTIA; STRUCTURAL DIFFERENCES; SUB-POPULATIONS; TEMPLATE IMAGES; TRADITIONAL APPROACHES;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; FEMALE; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84883846429     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_46     Document Type: Conference Paper

References (22)

1. Allassonniere, A., Amit, Y., Trouve, A.: Towards a coherent statistical framework for dense deformable template estimation. J.R. Statist. Soc. B 69, 3-29 (2007)
2. Ashburner, J., Neelin, P., Collins, D.L., Evans, A., Friston, K.: Incorporating prior knowledge into image registration. Neuro Image 6(4), 344-352 (1997)
3. Blezek, D., Miller, J.: Atlas stratification. Medical Image Analysis 11(5), 443-457 (2007)
4. Collins, D.L., et al.: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. comput. assist. tomogr. 18(2), 192-205 (1994)
5. De Craene, M., et al.: Multi-subject registration for unbiased statistical atlas construction. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 655-662. Springer, Heidelberg (2004)
6. Davatzikos, C., et al.: A computerized approach for morphological analysis of the corpus callosum. Journal of Computer Assisted Tomography 20(1), 88-97 (1996)
7. DeCarli, C., et al.: Longitudinal changes in lateral ventricular volume in datients with dementia of the alzheimer type. Neurology 42 (1992)
8. Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cerebral Cortex 14, 11-22 (2004)
9. Guimond, A., Meunier, F., Thirion, J.P.: Average brain models: A convergence study. Technical Report 3731, INRIA (1999)
10. Heckemann, R.A., et al.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115-126 (2006)
11. Ibanez, L., et al: The ITK Software Guide (2005)
12. Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphism atlas construction for computational anatomy. Neuro Image 23, 151-160 (2004)
13. Marcus, D.S., et al.: Open Access Series of Imaging Studies (OASIS): Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience 19, 1498-1507 (2007)
14. Park, H., et al.: Least biased target selection in probabilistic atlas construction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 419-426. Springer, Heidelberg (2005)
15. Woods, R.P., et al.: Automated Image Registration: II. Intersubject Validation of Linear and Nonlinear Models. Comp. Assisted Tomography 22(1), 153-165 (1998)
16. Rueckert, D., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transaction of Medical Imaging 18, 712-721 (1999)
17. Scahill, R.I., et al.: A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch. Neurol. 60, 989-994 (2003)
18. Studholme, C., Cardenas, V.: A template free approach to volumetric spatial normalization of brain anatomy. Pattern Rec. Letters 25, 1191-1202 (2004)
19. Tsai, A., et al.: An EM algorithm for shape classification based on level sets. Medical Image Analysis 9, 491-502 (2005)
20. Twining, C.J., et al.: A unified information-theoretic approach to groupwise non-rigid registration and model building. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 1-14. Springer, Heidelberg (2005)
21. Woods, R.P., et al.: Creation and use of a Talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Human Brain Mapping 8(2-3), 73-79 (1999)
22. Zöllei, L., et al.: Efficient population registration of 3d data. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 291-301. Springer, Heidelberg (2005)