The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
Open accessAbstract
This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.
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Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
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
Segmenting Retinal Blood Vessels WithDeep Neural Networks
Liskowski, P., & Krawiec, K. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2546227 · Cited by 890
DUNet: A deformable network for retinal vessel segmentation
Jin, Q., Meng, Z., Pham, T. D., Chen, Q., Wei, L., & Su, R. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.025 · Cited by 867
Recurrent residual U-Net for medical image segmentation
Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019) · Journal of Medical Imaging · doi:10.1117/1.jmi.6.1.014006 · Cited by 850
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
An ensemble of fine-tuned convolutional neural networks for medical image classification
Kumar, A., Kim, J., Lyndon, D., Fulham, M., & Feng, D. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2635663 · Cited by 515
Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2535311 · Cited by 503
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
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
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101748 · Cited by 401
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
Yan, Z., Yang, X., & Cheng, K.-T. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2872813 · Cited by 341
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review
Tsiknakis, N., Theodoropoulos, D., Manikis, G., Ktistakis, E., Boutsora, O., Berto, A., Scarpa, F., Scarpa, A., Fotiadis, D. I., & Marias, K. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104599 · Cited by 328
SegGPT: Towards Segmenting Everything In Context
Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., & Huang, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00110 · Cited by 310
Retinal vessel segmentation based on Fully Convolutional Neural Networks
Oliveira, A., Pereira, S., & Silva, C. A. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.034 · Cited by 299
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 286
Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation
Li, X., Jiang, Y., Li, M., & Yin, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2993842 · Cited by 265
Metadata
- Authors
- Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2012-09
- Volume
- 59
- Issue
- 9
- Pages
- 2538-2548
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2033723371
- Cited by
- 1,031
- References
- 3
- FWCI
- 14.91
- Type
- journal article
- Language
- en
- Open access
- green
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