Computer-aided Diagnosis of Glaucoma Using Fundus Images

DOI

10.2991/meic-14.2014.205How to use a DOI?

Keywords

machine learnin; glaucoma diagnosis; computer-aided diagnosis; fundus images; ISNT rule

Abstract

Glaucoma is a chronic eye disease which cannot be cured, so that detecting the disease in time is important. Machine learning for glaucoma diagnosis has achieved great development in recent years. In this paper, we present an algorithm for glaucoma diagnosis from optic disc and optic cup boundary lines in fundus images based on doctors' knowledge. We do meticulous division, scaling transformation and principal component analysis on the optic disc and optic cup boundary lines to extract features. The extracted features correspond well with doctors' knowledge. Therefore, we can make an intuitive explanation for the diagnosis results to doctors, rather than just as a black-box prediction. On a real sample set, the proposed feature extraction and diagnosis algorithms achieve high prediction accuracy.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yongli Xu
AU  - Xin Jia
AU  - Man Hu
AU  - Lina Zhao
PY  - 2014/11
DA  - 2014/11
TI  - Computer-aided Diagnosis of Glaucoma Using Fundus Images
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 915
EP  - 920
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.205
DO  - 10.2991/meic-14.2014.205
ID  - Xu2014/11
ER  -