24 March 2016 Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood
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Abstract
Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.
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Chisako Muramatsu, Kyoko Ishida, Akira Sawada, Yuji Hatanaka, Tetsuya Yamamoto, Hiroshi Fujita, "Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852L (24 March 2016); doi: 10.1117/12.2216662; https://doi.org/10.1117/12.2216662
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