The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
An Active Contour Model for Segmenting and Measuring Retinal Vessels
Open accessAbstract
This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a "Ribbon of Twins" active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.
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Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
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
Snakes, shapes, and gradient vector flow
Xu, C., & Prince, J. L. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661186 · Cited by 4,001
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
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
Jiang, X., & Mojon, D. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1159954 · Cited by 679
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., & Kennedy, L. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.823261 · Cited by 389
Self-citationThe detection and quantification of retinopathy using digital angiograms
Zhou, L., Rzeszotarski, M. S., Singerman, L. J., & Chokreff, J. M. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363106 · Cited by 292
Measurement of Retinal Vessel Widths From Fundus Images Based on 2-D Modeling
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., & Kennedy, R. L. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.830524 · Cited by 255
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Cited by
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
Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2535311 · Cited by 503
Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., & Angelopoulou, E. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0455 · Cited by 472
CS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging
Mou, L., Zhao, Y., Fu, H., Liu, Y., Cheng, J., Zheng, Y., Su, P., Yang, J., Chen, L., Frangi, A. F., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101874 · Cited by 437
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
Metadata
- Authors
- Al-Diri, B., Hunter, A., & Steel, D.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2009-09
- Volume
- 28
- Issue
- 9
- Pages
- 1488-1497
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2171459511
- Cited by
- 431
- References
- 17
- FWCI
- 13.62
- Type
- journal article
- Language
- en
- Open access
- green
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- Al-Diri, B., Hunter, A., & Steel, D. (2009). An Active Contour Model for Segmenting and Measuring Retinal Vessels. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2009.2017941
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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