CNNs for automatic glaucoma assessment using fundus images: an extensive validation-Reference-Cited by-同舟云学术

CNNs for automatic glaucoma assessment using fundus images: an extensive validation

Published:2019-03-20 Issue:1 Volume:18 Page:
ISSN:1475-925X
Container-title:BioMedical Engineering OnLine
language:en
Short-container-title:BioMed Eng OnLine

Author:

Diaz-Pinto Andres^ORCID,Morales Sandra,Naranjo Valery,Köhler Thomas,Mossi Jose M.,Navea Amparo

Funder

Conselleria d’Educació, Investigació, Cultura i Esport

Horizon 2020 Framework Programme for Research and Innovation

Ministerio de Economía, Industria y Competitividad, Gobierno de España

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology

Link

http://link.springer.com/content/pdf/10.1186/s12938-019-0649-y.pdf

Reference37 articles.

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3. Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G. Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal. 2010;14:471–81. https://doi.org/10.1016/j.media.2009.12.006 .

4. Sivaswamy J, Krishnadas SR, Joshi GD, Jain M, Ujjwal A, ST Drishti. Retinal image dataset for optic nerve head (ONH) segmentation. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). 2014, p. 53–6. https://doi.org/10.1109/ISBI.2014.6867807 .

5. Morales S, Naranjo V, Angulo J, Alcañiz M. Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans Med Imag. 2013;32:786–96. https://doi.org/10.1109/TMI.2013.2238244 .

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