Classification-driven pathological neuroimage retrieval using statistical asymmetry measures

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

Volumn 2208, Issue , 2001, Pages 655-665

  Liu, Y ^a   Dellaert, F ^a   Rothfus, W E ^b   Moore, A ^a   Schneider, J ^a   Kanade, T ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RETRIEVAL; MEDICAL COMPUTING; MEDICAL IMAGING; SEMANTICS;

BILATERAL ASYMMETRIES; DISCRIMINATIVE FEATURES; INDEXING FEATURES; MEMORY-BASED LEARNING; PRE-DEFINED SEMANTICS; QUANTITATIVE EVALUATION; SEMANTIC-BASED IMAGE RETRIEVALS; STATISTICAL DISTRIBUTION;

IMAGE CLASSIFICATION;

EID: 84958170084     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45468-3_79     Document Type: Conference Paper

References (17)

1. C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, 1995. ISBN:0198538499.
2. C. Davatzikos, M. Vaillant, S. Resnick, J.L. Prince, S. Letovsky, and R.N. Bryan. A computerizeda pproach for morphological analysis of the corpus callosum. Comp. Ass. Tomography., 20:88–97, Jan./Feb. 1996.
3. L. Devroye, L. Györfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer-Verlag, London, Paris, Tokyo, 1997.
4. R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. John Wiley & Sons, New York, 2001.
5. J.S. Duncan and N. Ayache. Medical image analysis: Progress over two decades andthe challenges ahead. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):85-106, 2000.
6. D. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic, and W. Equitz. Efficient andeffectiv e querying by image content. Journal of Intelligent Information Systems, 1994.
7. R. Guillemaud, M. Sakuma, P. Marais, J. Feldmar, R. Crow, L. DeLisi, A. Zisserman, and M. Brady. Cerebral symmetry analysis from mri scans. Submitted to Psychiatry Research Neuroimaging, 1998.
8. D. L. G. Hill, P.G. Batchelor, M. Holden, and D. J. Hawkes. Medical image registration. Physics in Medicine and Biology, 46:1–45, 2001.
9. Y. Liu, R.T. Collins, and W.E. Rothfus. Robust Midsagittal Plane Extraction from Normal andPat hological 3D Neuroradiology Images. IEEE Transactions on Medical Imaging, 20(3), March 2001.
10. Y. Liu, T. Zhao, E.S. Wachman, D.L. Farkas, and T. Kanade. Learning discriminant features in multispectrual biological images. In Mathematical Methods in Biomedical Image Analysis. submitted, 2001.
11. F. Maes, A. Collignon, D. Vandermeulun, G. Marchal, and P. Suetens. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2):187-198, 1997.
12. A. W. Moore, D. J. Hill, and M. P. Johnson. An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators. In S. Hanson, S. Judd, and T. Petsche, editors, Computational Learning Theory and Natural Learning Systems, Volume 3. MIT Press, 1994.
13. A. Pentland, R.W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. IJCV, 18(3):233–254, June 1996.
14. J. Schneider and A.W. Moore. A locally weighted learning tutorial using vizier 1.0. tech. report CMU-RI-TR-00-18, Robotics Institute, Carnegie Mellon, 1997.
15. P.M. Thompson, C. Schwartz, and A.W. Toga. High-resolution random mesh algorithms for creating a probabilistic 3dsu rface atlas of the human brain. Neuroimage, 3(1):19–34, Feb 1996.
16. J. West, M. Fitzpatrick, and et al. Comparison andev aluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography, 21:554–566, July/August 1997.
17. W. Zhang, S. Sclaroff, S. Dickinson, J. Feldman, and S. Dunn. Shape-basedi ndexing in a medical image database. Workshop on Biomedical Image Analysis, pages 221–230, June 1998.