The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification
Abstract
In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development, we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine. The effectiveness of both methods is demonstrated through receiver operating characteristic analysis on two publicly available databases of color fundus images.
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Cites
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Detection of blood vessels in retinal images using two-dimensional matched filters
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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
Cited by
Segmenting Retinal Blood Vessels WithDeep Neural Networks
Liskowski, P., & Krawiec, K. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2546227 · Cited by 890
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
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
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
Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation
Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., & Seung, H. S. (2010) · Neural Computation · doi:10.1162/neco.2009.10-08-881 · Cited by 376
Metadata
- Authors
- Ricci, E., & Perfetti, R.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2007-10
- Volume
- 26
- Issue
- 10
- Pages
- 1357-1365
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2116628223
- Cited by
- 818
- References
- 30
- FWCI
- 7.52
- Type
- journal article
- Language
- en
- Open access
- closed
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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).
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