The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features
Open accessAbstract
This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
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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
Economic costs of diabetes in the U.S. in 2007
American Diabetes Association (2008) · Diabetes Care · doi:10.2337/dc08-9017 · Cited by 1,323
Cited by
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
Forest point processes for the automatic extraction of networks in raster data
Schmidt, A., Lafarge, F., Brenner, C., Rottensteiner, F., & Heipke, C. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.01.012 · Cited by 21
Gihedan, A. (2021) · Scientific Journal for the Faculty of Science-Sirte University · doi:10.37375/sjfssu.v1i1.74
Metadata
- Authors
- Marín, D., Aquino, A., Gegundez-Arias, M. E., & Bravo, J. M.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2011-01
- Volume
- 30
- Issue
- 1
- Pages
- 146-158
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2051578148
- Cited by
- 929
- References
- 2
- FWCI
- 18.80
- Type
- journal article
- Language
- en
- Open access
- green
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- Marín, D., Aquino, A., Gegundez-Arias, M. E., & Bravo, J. M. (2011). A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2010.2064333
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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.
