The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
Abstract
Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.
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Medical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791488 · Cited by 834
Self-citationCNNs for automatic glaucoma assessment using fundus images: an extensive validation
Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J. M., & Navea, A. (2019) · BioMedical Engineering OnLine · doi:10.1186/s12938-019-0649-y · Cited by 435
Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image
Fu, H., Cheng, J., Xu, Y., Zhang, C., Wong, D. W. K., Liu, J., & Cao, X. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837012 · Cited by 394
Self-citationA Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Li, L., Xu, M., Liu, H., Li, Y., Wang, X., Jiang, L., Wang, Z., Fan, X., & Wang, N. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927226 · Cited by 303
Metadata
- Authors
- Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D. W. K., Tan, N.-M., Tao, D., Cheng, C.-Y., Aung, T., & Wong, T. Y.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-06
- Volume
- 32
- Issue
- 6
- Pages
- 1019-1032
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2081178133
- Cited by
- 593
- References
- 41
- FWCI
- 22.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D. W. K., Tan, N.-M., Tao, D., Cheng, C.-Y., Aung, T., & Wong, T. Y. (2013). Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2013.2247770
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