The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores
Open accessAbstract
This paper presents a robust and fully automatic filter-based approach for retinal vessel segmentation. We propose new filters based on 3D rotating frames in so-called orientation scores, which are functions on the Lie-group domain of positions and orientations [Formula: see text]. By means of a wavelet-type transform, a 2D image is lifted to a 3D orientation score, where elongated structures are disentangled into their corresponding orientation planes. In the lifted domain [Formula: see text], vessels are enhanced by means of multi-scale second-order Gaussian derivatives perpendicular to the line structures. More precisely, we use a left-invariant rotating derivative (LID) frame, and a locally adaptive derivative (LAD) frame. The LAD is adaptive to the local line structures and is found by eigensystem analysis of the left-invariant Hessian matrix (computed with the LID). After multi-scale filtering via the LID or LAD in the orientation score domain, the results are projected back to the 2D image plane giving us the enhanced vessels. Then a binary segmentation is obtained through thresholding. The proposed methods are validated on six retinal image datasets with different image types, on which competitive segmentation performances are achieved. In particular, the proposed algorithm of applying the LAD filter on orientation scores (LAD-OS) outperforms most of the state-of-the-art methods. The LAD-OS is capable of dealing with typically difficult cases like crossings, central arterial reflex, closely parallel and tiny vessels. The high computational speed of the proposed methods allows processing of large datasets in a screening setting.
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
Multiscale vessel enhancement filtering
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0056195 · Cited by 3,707
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
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
Retinal 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
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2205687 · Cited by 1,031
Marín, D., Aquino, A., Gegundez-Arias, M. E., & Bravo, J. M. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2064333 · Cited by 929
Mendonca, A. M., & Campilho, A. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879955 · Cited by 921
Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification
Ricci, E., & Perfetti, R. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.898551 · Cited by 818
Comparative study of retinal vessel segmentation methods on a new publicly available database
Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., & Abramoff, M. D. (2004) · SPIE Proceedings · doi:10.1117/12.535349 · Cited by 790
Cited by
Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536
“Colonoscopy Kvasir-SEG [59] Gastrointestinal polyp colonoscopy LiVScar [60] Infarct segmentation in the left ventricle CMR LUNA16 [61] Lungs, heart and trachea CT M&Ms [62] Left and right ventricle and left ventricular myocardium CMR MALBCV-Abdomen [63] Abdominal multi-organ segmentation CT Montgomery County CXR Set […”
InfluentialA 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
Retinal vessel segmentation based on Fully Convolutional Neural Networks
Oliveira, A., Pereira, S., & Silva, C. A. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.034 · Cited by 299
SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation
Wu, H., Wang, W., Zhong, J., Lei, B., Wen, Z., & Qin, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102025 · Cited by 259
Metadata
- Authors
- Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J. P. W., Duits, R., & ter Haar Romeny, B. M.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-12
- Volume
- 35
- Issue
- 12
- Pages
- 2631-2644
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2488605601
- Cited by
- 417
- References
- 35
- FWCI
- 24.70
- 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: Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores. https://app.cochat.ai/papers/10.1109/tmi.2016.2587062
- The paper
- Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J. P. W., Duits, R., & ter Haar Romeny, B. M. (2016). Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2016.2587062
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.
