The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction
Abstract
This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Cited by
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.02.007 · Cited by 2,191
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2796074 · Cited by 482
Zhao, Y., Rada, L., Chen, K., Harding, S. P., & Zheng, Y. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2409024 · Cited by 391
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
Yan, Z., Yang, X., & Cheng, K.-T. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2872813 · Cited by 341
Metadata
- Authors
- Mendonca, A. M., & Campilho, A.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2006-09
- Volume
- 25
- Issue
- 9
- Pages
- 1200-1213
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2170092083
- Cited by
- 921
- References
- 1
- FWCI
- 13.35
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. https://app.cochat.ai/papers/10.1109/tmi.2006.879955
- The paper
- Mendonca, A. M., & Campilho, A. (2006). Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2006.879955
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
