Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.769975
Title: Image based grading of nuclear cataract by SVM regression
Authors: Huiqi, L.
Joo, H.L.
Jiang, L.
Tien, Y.W. 
Tan, A.
Jie, J.W.
Mitchell, P.
Keywords: Active shape model
Nuclear cataract
Slit-lamp image
SVM regression
Issue Date: 2008
Citation: Huiqi, L., Joo, H.L., Jiang, L., Tien, Y.W., Tan, A., Jie, J.W., Mitchell, P. (2008). Image based grading of nuclear cataract by SVM regression. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 6915 : -. ScholarBank@NUS Repository. https://doi.org/10.1117/12.769975
Abstract: Cataract is one of the leading causes of blindness worldwide. A computer-aided approach to assess nuclear cataract automatically and objectively is proposed in this paper. An enhanced Active Shape Model (ASM) is investigated to extract robust lens contour from slit-lamp images. The mean intensity in the lens area, the color information on the central posterior subcapsular reflex, and the profile on the visual axis are selected as the features for grading. A Support Vector Machine (SVM) scheme is proposed to grade nuclear cataract automatically. The proposed approach has been tested using the lens images from Singapore National Eye Centre. The mean error between the automatic grading and grader's decimal grading is 0.38. Statistical analysis shows that 97.8% of the automatic grades are within one grade difference to human grader's integer grades. Experimental results indicate that the proposed automatic grading approach is promising in facilitating nuclear cataract diagnosis.
Source Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
URI: http://scholarbank.nus.edu.sg/handle/10635/108613
ISBN: 9780819470997
ISSN: 16057422
DOI: 10.1117/12.769975
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

6
checked on Sep 22, 2020

WEB OF SCIENCETM
Citations

4
checked on Sep 22, 2020

Page view(s)

86
checked on Sep 19, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.