The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
FABC: Retinal Vessel Segmentation Using AdaBoost
Abstract
This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy ( 0.9597 versus 0.9473 for the nearest performer).
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Citations
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
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
Feature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541
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
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.001 · Cited by 567
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
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
- Lupascu, C. A., Tegolo, D., & Trucco, E.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2010-09
- Volume
- 14
- Issue
- 5
- Pages
- 1267-1274
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2123434300
- Cited by
- 352
- References
- 3
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- closed
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