Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85756
Title: Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques
Authors: Koh, Joel E.W.
Ng, Eddie Y.K.
Bhandary, Sulatha V.
Hagiwara, Yuki
Laude, Augustinus
Acharya, U. Rajendra
Keywords: Age-related Macular Degeneration
Bag-of-visual-words
Issue Date: 2018
Source: Koh, J. E. W., Ng, E. Y. K., Bhandary, S. V., Hagiwara, Y., Laude, A., & Acharya, U. R. (2018). Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques. Computers in Biology and Medicine, 92, 204-209.
Series/Report no.: Computers in Biology and Medicine
Abstract: Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.
URI: https://hdl.handle.net/10356/85756
http://hdl.handle.net/10220/45365
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2017.11.019
Rights: © 2017 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Computers in Biology and Medicine, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.compbiomed.2017.11.019].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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