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.
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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
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
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
Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2535311 · Cited by 503
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
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
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
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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.
