Abstract
In this paper, we propose to fuse geometric and appearance-based features at the feature-level for automatic glaucoma diagnosis. The cup-to-disc ratio and neuro-retinal rim width variation are extracted as the geometric features based on a coarseto- fine localization method. For the appearancebased feature extraction, the principal components analysis is adopted. Finally, these features are combined at the feature-level based on the random projection and the total error rate minimization classifier. Experimental results on an in-house data set shows that the feature-level fusion can enhance the classification performance comparing with that before fusion.
| Original language | English |
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| Title of host publication | 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017 |
| Publisher | European Association of Geoscientists and Engineers, EAGE |
| Pages | 76-85 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781941968437 |
| Publication status | Published - 2017 |
| Event | 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017 - Lodz, Poland Duration: 2017 Sept 18 → 2017 Sept 20 |
Publication series
| Name | 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017 |
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Other
| Other | 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017 |
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| Country/Territory | Poland |
| City | Lodz |
| Period | 17/9/18 → 17/9/20 |
Bibliographical note
Funding Information:This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2016-31-0650).
Publisher Copyright:
© 2017 SDIWC.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence