The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Optic Nerve Head Segmentation
Open accessAbstract
Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 microns/pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.
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Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
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
Dynamic 3D models with local and global deformations: deformable superquadrics
Terzopoulos, D., & Metaxas, D. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85659 · Cited by 781
Sinthanayothin, C., Boyce, J. F., Cook, H. L., & Williamson, T. H. (1999) · British Journal of Ophthalmology · doi:10.1136/bjo.83.8.902 · Cited by 778
Blake, A., & Isard, M. (1998) · Springer eBooks · doi:10.1007/978-1-4471-1555-7 · Cited by 760
Lalonde, M., Beaulieu, M., & Gagnon, L. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.963823 · Cited by 400
Screening for Diabetic Retinopathy
Singer, D. E., Nathan, D. M., Fogel, H. A., & Schachat, A. P. (1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-116-8-660 · Cited by 292
Cited by
Orlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
RIM-ONE: An open retinal image database for optic nerve evaluation
Fumero, F., Alayon, S., Sanchez, J. L., Sigut, J., & Gonzalez-Hernandez, M. (2011) · 2011 24th International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms.2011.5999143 · Cited by 538
Metadata
- Authors
- Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., & Kennedy, L.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2004-02
- Volume
- 23
- Issue
- 2
- Pages
- 256-264
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2170203892
- Cited by
- 389
- References
- 16
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- green
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