Please use this identifier to cite or link to this item:
|Title:||Focal biologically inspired feature for glaucoma type classification||Authors:||Cheng, J.
|Issue Date:||2011||Citation:||Cheng, J.,Tao, D.,Liu, J.,Wong, D.W.K.,Lee, B.H.,Baskaran, M.,Wong, T.Y.,Aung, T. (2011). Focal biologically inspired feature for glaucoma type classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6893 LNCS (PART 3) : 91-98. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-23626-6_12||Abstract:||Glaucoma is an optic nerve disease resulting in 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. Ophthalmologists examine the iridocorneal angle between iris and cornea to determine the glaucoma type. However, manual classification/grading of the iridocorneal angle images is subjective and time consuming. To save workload and facilitate large-scale clinical use, it is essential to determine glaucoma type automatically. In this paper, we propose to use focal biologically inspired feature for the classification. The iris surface is located to determine the focal region. The association between focal biologically inspired feature and angle grades is built. The experimental results show that the proposed method can correctly classify 85.2% images from open angle glaucoma and 84.3% images from angle closure glaucoma. The accuracy could be improved close to 90% with more images included in the training. The results show that the focal biologically inspired feature is effective for automatic glaucoma type classification. It can be used to reduce workload of ophthalmologists and diagnosis cost. © 2011 Springer-Verlag.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/108611||ISBN:||9783642236259||ISSN:||03029743||DOI:||10.1007/978-3-642-23626-6_12|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 19, 2019
checked on Sep 20, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.