The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
An approach to localize the retinal blood vessels using bit planes and centerline detection
Open accessAbstract
The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, 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
Image analysis and mathematical morphology
(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,151
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
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
Investigative Ophthalmology and Visual Science
Frey, C. (2010) · Cited by 3,350
The design and use of steerable filters
Freeman, W. T., & Adelson, E. H. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.93808 · Cited by 2,942
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
Hoover, A. D., Kouznetsova, V., & Goldbaum, M. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.845178 · Cited by 2,582
Detection of blood vessels in retinal images using two-dimensional matched filters
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., & Goldbaum, M. (1989) · IEEE Transactions on Medical Imaging · doi:10.1109/42.34715 · Cited by 1,668
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
Soares, J. V. B., Leandro, J. J. G., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879967 · Cited by 1,497
Mendonca, A. M., & Campilho, A. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879955 · Cited by 921
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
Zana, F., & Klein, J.-C. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.931095 · Cited by 862
Cited by
DUNet: A deformable network for retinal vessel segmentation
Jin, Q., Meng, Z., Pham, T. D., Chen, Q., Wei, L., & Su, R. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.025 · Cited by 867
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images
Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., & Wang, T. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2457891 · Cited by 571
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation
Yan, Z., Yang, X., & Cheng, K.-T. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2828137 · Cited by 428
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores
Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J. P. W., Duits, R., & ter Haar Romeny, B. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2587062 · Cited by 417
Hierarchical retinal blood vessel segmentation based on feature and ensemble learning
Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., & Yang, G. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.07.059 · Cited by 390
Metadata
- Authors
- Fraz, M. M., Barman, S. A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A. R., & Owen, C. G.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 108
- Issue
- 2
- Pages
- 600-616
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2132519114
- Cited by
- 340
- References
- 66
- FWCI
- 9.65
- Type
- journal article
- Language
- en
- Open access
- green
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: An approach to localize the retinal blood vessels using bit planes and centerline detection. https://app.cochat.ai/papers/10.1016/j.cmpb.2011.08.009
- The paper
- Fraz, M. M., Barman, S. A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A. R., & Owen, C. G. (2012). An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2011.08.009
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.
