The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina
Abstract
This paper describes a robust hierarchical algorithm for fully-automatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computer-aided instrumentation. Central to the algorithm is a 12-parameter interimage transformation derived by modeling the retina as a rigid quadratic surface with unknown parameters. The parameters are estimated by matching vascular landmarks by recursively tracing the blood vessel structure. The parameter estimation technique, which could be generalized to other applications, is a hierarchy of models and methods, making the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions. Experiments involving 3,000 image pairs from 16 different healthy eyes were performed. Final registration errors less than a pixel are routinely achieved. The speed, accuracy, and ability to handle small overlaps compare favorably with retinal image registration techniques published in the literature.
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Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
Robust Regression and Outlier Detection
Piepel, G. F. (1989) · Technometrics · doi:10.1080/00401706.1989.10488524 · Cited by 6,144
Robust Regression and Outlier Detection
Rousseeuw, P. J., & Leroy, A. M. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/0471725382 · Cited by 5,609
Wiley Series in Probability and Statistics
(2008) · Wiley series in probability and statistics · doi:10.1002/series1345 · Cited by 4,217
Medical image analysis: progress over two decades and the challenges ahead
Duncan, J. S., & Ayache, N. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824822 · Cited by 4,140
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,057
Robust Statistics: The Approach Based on Influence Functions
Ruppert, D. (1987) · Technometrics · doi:10.2307/1269782 · Cited by 3,880
Least Median of Squares Regression
Rousseeuw, P. J. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477105 · Cited by 3,835
Object modelling by registration of multiple range images
Chen, Y., & Medioni, G. (1992) · Image and Vision Computing · doi:10.1016/0262-8856(92)90066-c · Cited by 3,154
Cited by
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587
“used the branching and crossover points of the blood vessel structure as feature points [276].”
MethodRetinal Imaging and Image Analysis
Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2084567 · Cited by 1,439
The dual-bootstrap iterative closest point algorithm with application to retinal image registration
Stewart, C. V., Tsai, C.-L., & Roysam, B. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819276 · Cited by 346
Self-citationRetinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures
Sofka, M., & Stewart, C. V. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.884190 · Cited by 334
Self-citationA Deep Step Pattern Representation for Multimodal Retinal Image Registration
Lee, J., Liu, P., Cheng, J., & Fu, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00518 · Cited by 56
Metadata
- Authors
- Can, A., Stewart, C. V., Roysam, B., & Tanenbaum, H. L.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-03
- Volume
- 24
- Issue
- 3
- Pages
- 347-364
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2057771956
- Cited by
- 272
- References
- 44
- FWCI
- 9.96
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
