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Cited by 346

The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.

The dual-bootstrap iterative closest point algorithm with application to retinal image registration

Stewart, C.V., Chia-Ling Tsai, Roysam, B.·2003·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2003.819276
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Abstract

Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% image overlap.

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Metadata

Authors
Stewart, C. V., Tsai, C.-L., & Roysam, B.
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2003-11
Volume
22
Issue
11
Pages
1379-1394
ISSN
0278-0062 (ISSN-L)
DOI
10.1109/tmi.2003.819276
OpenAlex
W2141085506
Cited by
346
References
91
FWCI
9.57
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The dual-bootstrap iterative closest point algorithm with application to retinal image registration. https://app.cochat.ai/papers/10.1109/tmi.2003.819276
The paper
Stewart, C. V., Tsai, C.-L., & Roysam, B. (2003). The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2003.819276

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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.