Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEMBS.2011.6091111
Title: Focal edge association to glaucoma diagnosis
Authors: Cheng, J.
Liu, J.
Wong, D.W.K.
Tan, N.M.
Lee, B.H.
Cheung, C. 
Baskaran, M.
Wong, T.Y. 
Aung, T.
Issue Date: 2011
Source: Cheng, J.,Liu, J.,Wong, D.W.K.,Tan, N.M.,Lee, B.H.,Cheung, C.,Baskaran, M.,Wong, T.Y.,Aung, T. (2011). Focal edge association to glaucoma diagnosis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS : 4481-4484. ScholarBank@NUS Repository. https://doi.org/10.1109/IEMBS.2011.6091111
Abstract: Glaucoma is an optic nerve disease resulting in the loss of vision. There are two common types of glaucoma: open angle glaucoma and angle closure glaucoma. Glaucoma type classification is important in glaucoma diagnosis. Clinically, ophthalmologists examine the iridocorneal angle between iris and cornea to determine the glaucoma type as well as the degree of closure. However, manual grading of the iridocorneal angle images is subjective and often time consuming. In this paper, we propose focal edge for automated iridocorneal angle grading. The iris surface is located to determine focal region and focal edges. The association between focal edges and angle grades is built through machine learning. A modified grading system with three grades is adopted. The experimental results show that the proposed method can correctly classify 87.3% open angle and 88.4% closed angle. Moreover, it can correctly classify 75.0% grade 1 and 77.4% grade 0 for angle closure cases. © 2011 IEEE.
Source Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
URI: http://scholarbank.nus.edu.sg/handle/10635/53522
ISBN: 9781424441211
ISSN: 1557170X
DOI: 10.1109/IEMBS.2011.6091111
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