Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99333
Title: A survey on computer aided diagnosis for ocular diseases
Authors: Wong, Damon Wing Kee
Zhang, Zhuo
Srivastava, Ruchir
Liu, Huiying
Chen, Xiangyu
Duan, Lixin
Kwoh, Chee Keong
Wong, Tien Yin
Liu, Jiang
Issue Date: 2014
Source: Zhang, Z., Srivastava, R., Liu, H., Chen, X., Duan, L., Wong, D. W. K., et al. (2014). A survey on computer aided diagnosis for ocular diseases. BMC Medical Informatics and Decision Making, 14(80).
Series/Report no.: BMC medical informatics and decision making
Abstract: Background: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. Method: We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. Result: We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. Conclusion: While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.
URI: https://hdl.handle.net/10356/99333
http://hdl.handle.net/10220/38561
ISSN: 1472-6947
DOI: http://dx.doi.org/10.1186/1472-6947-14-80
Rights: © 2014 Zhang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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