Learning-based deformation estimation for fast non-rigid registration

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Volumn , Issue , 2008, Pages

  Kim, Min Jeong ^a   Kim, Myoung Hee ^a   Shen, Dinggang ^b  

^a Ewha Womans University   (South Korea)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; EDUCATION; ESTIMATION; FEATURE EXTRACTION; IMAGE ENHANCEMENT; IMAGE PROCESSING; MATHEMATICAL MODELS; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL METHODS; TESTING; WEAVING;

BRAIN IMAGING; DEFORMATION COEFFICIENTS; DEFORMATION FIELDS; ESTIMATION METHODS; IMAGE INFORMATIONS; NON-RIGID REGISTRATION; RE-SAMPLING; REGISTRATION ALGORITHMS; REGISTRATION METHODS; ROBUST PERFORMANCE; SAMPLE DEFORMATION; STATISTICAL DEFORMATION MODEL; STATISTICAL MODELLING; TEMPLATE IMAGES; TEST IMAGES;

DEFORMATION;

EID: 51849089599     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2008.4563006     Document Type: Conference Paper

References (15)

1. D. Shen and C. Davatzikos. HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging, 21(11):1421-1439, 2002.
2. T. Hartkens, D.L.G. Hill, A.D. Castellano-Smith, et al. Using points and surfaces to improve voxel-based non-rigid registration, Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lecture Notes in Computer Science 2489:565-572, 2002.
3. H. Chui, L. Win, R. Schultz, J.S. Duncan, A. Rangarajan. A unified non-rigid feature registration method for brain mapping. Medical Image Analysis, 7:113-130, 2003.
4. D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, and D.J. Hawkes. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transactions on Medical Imaging 18(8):712-721, 1999.
5. Y. Wang and L.H. Staib. Elastic Model based Non-rigid Registration Incorporating Statistical Shape Information. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lecture Notes in Computer Science 2489:1162-1173, 1998.
6. D. Rueckert, A. Frangi, and J. Schnabel. Automatic Construction of 3-D Statistical Deformation Models of the Brain Using Nonrigid Registration. IEEE Transactions on Medical Imaging, 22(8):1014-1025, 2003.
7. Z. Xue and D. Shen. Statistically-Constrained Deformable Registration of MR Brain Images. Fourth IEEE International Symposium on Biomedical Imaging (ISBI):25-28, 2007.
8. P. Golland, W.E. L. Grimson, M. E. Shenton, and R. Kikinis. Small sample size learning for shape analysis of anatomical structures. Medical Image Computing Computer-Assisted Intervention (MICCAI):72-82, 2000.
9. Y. Fan, D. Shen, C. Davatzikos. Detecting Cognitive States from fMRI images of Subjects by Machine Learning and Multivariate Classification. IEEE Workshop on Mathematical Methods in Biomedical Image (MMBIA), 2006.
10. Z. Tu, X. Zhou, D. Comaniciu and L. Bogoni. A Learning Based Approach for 3D Segmentation and Colon Detagging. European Conference on Computer Vision (ECCV):436-448, 2006.
11. S. Zhou and D. Comaniciu. Shape Regression Machine. Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Sciences 4584:13-25, 2007.
12. M. Breijl, M. Sonka. Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples. IEEE Transactions on Medical Imaging, 19(10):973-985, 2000.
13. G. Wu, F. Qi, D. Shen. Learning-Based Deformable Registration of MR Brain Images. IEEE Transactions on Medical Imaging, 25(9):1145-1157, 2006.
14. R. Collobert, S. Bengio. SVMTorch: Support Vector Machine for Large-scale Regression Problems. Journal of Machine Learning Research, 1:143-160, 2001.
15. T. Cootes and C. Taylor. Statistical Models of Appearance for Computer Vision. Technical report, University of Manchester: 1-125, 2004.