A statistical parts-based appearance model of inter-subject variability

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

Volumn 4190 LNCS - I, Issue , 2006, Pages 232-240

  Toews, Matthew ^a   Collins, D Louis ^b   Arbel, Tal ^a  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; IMAGE ANALYSIS; MATHEMATICAL MODELS; PATTERN MATCHING; STATISTICAL METHODS;

ANATOMICAL DIFFERENCES; BRAIN IMAGES; IMAGE REGISTRATION; SCALE INVARIANT FEATURES;

CHARACTER SETS;

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

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; OBSERVER VARIATION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 33750238911     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11866565_29     Document Type: Conference Paper

References (12)

1. G. Carneiro and A.D. Jepson, Multi-scale phase-based local features, CVPR, vol. 1, 2003, pp. 736-743.
2. D. L. Collins, N.J Kabani, and A.C. Evans, Automatic volume estimation of gross cerebral structures, 4th International Conference of Functional Mapping of the Human Brain (A. Evans, ed.), 1998.
3. T.F. Cootes, G.J. Edwards, and C.J. Taylor, Active appearance models, Pattern Analysis and Machine Intelligence 23 (2001), no. 6, 681-684.
4. R. Fergus, P. Perona, and A. Zisserman, Object class recognition by unsupervised scale-invariant learning, CVPR, 2003, pp. 264-271.
5. Wu Guorong, Feihu Qi, and Dinggang Shen, Learning best features for deformable registration of mr brains, MICCAI, 2005.
6. C. Harris and M. Stephens, A combined corner and edge detector, Proceedings of the 4th Alvey Vision Conference, 1988, pp. 147-151.
7. T. Kadir and M. Brady, Saliency, scale and image description, IJCV 45 (2001), no. 2, 83-105.
8. David G. Lowe, Distinctive image features from scale-invariant keypoints, IJCV 60 (2004), no. 2, 91-110.
9. K. Mikolajczyk and C. Schmid, Scale and affine invariant interest point detectors, IJCV 60 (2004), no. 1, 63-86.
10. Daniel Rueckert, Alejandro F. Frangi, and Julia A. Schnabel, Automatic construction of 3-d statistical deformation models of the brain using nonrigid registration, TMI 22 (2003), no. 8, 1014-1025.
11. J. Talairach and P. Tournoux, Co-planar stereotactic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging, Georg Thieme Verlag, Stuttgart, 1988.
12. M. Toews and T. Arbel, Detection over viewpoint via the object class invariant, ICPR, 2006.