Flexible architectures for retinal blood vessel segmentation in high-resolution fundus images-Reference-Cited by-同舟云学术

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Flexible architectures for retinal blood vessel segmentation in high-resolution fundus images

Published:2016-12-20 Issue:1 Volume:15 Page:31-42
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Bendaoudi Hamza^ORCID,Cheriet Farida,Manraj Ashley,Ben Tahar Houssem,Langlois J. M. Pierre^ORCID

Funder

Natural Sciences and Engineering Research Council NSERC

CMC Microsystems

Regroupement Stratégique en Microsystèmes du Québec (ReSMIQ)

Publisher

Springer Science and Business Media LLC

Subject

Information Systems

Link

http://link.springer.com/article/10.1007/s11554-016-0661-4/fulltext.html

Reference29 articles.

1. Cheung, N., Mitchell, P., Wong, T.Y.: Diabetic retinopathy. Lancet 376, 124–136 (2010)

2. Abramoff, M.D., Niemeijer, M., Russell, S.R.: Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev. Med. Devices 7, 287–296 (2010)

3. Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M.S., Abramoff, M.D.: Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Med. Imaging 24, 584–592 (2005)

4. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., et al.: Blood vessel segmentation methodologies in retinal images—a survey. Comput. Methods Progr. Biomed. 108, 407–433 (2012)

5. Yin, Y., Adel, M., Guillaume, M., Bourennane, S.: A probabilistic based method for tracking vessels in retinal images. In: 2010 IEEE International Conference on Image Processing, pp. 4081–4084 (2010)

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3. Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks;Symmetry;2020-06-01

4. M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications;2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2019-06

5. Embedded deep learning in ophthalmology: making ophthalmic imaging smarter;Therapeutic Advances in Ophthalmology;2019-01

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