NDLI: An automatic diagnosis system of nuclear cataract using slit-lamp images

Content Provider	IEEE Xplore Digital Library
Author	Huiqi Li Joo Hwee Lim Jiang Liu Wong, D.W.K. Ngan Meng Tan Shijian Lu Zhuo Zhang Tien Yin Wong
Copyright Year	2009
Description	Author affiliation: Institute for Infocomm Research, A¿STAR (Joo Hwee Lim; Jiang Liu; Wong, D.W.K.; Ngan Meng Tan; Shijian Lu; Zhuo Zhang) \|\| Institute for Infocomm Research, A¿STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632 (Huiqi Li) \|\| Singapore Eye Research Institute and National University of Singapore (Tien Yin Wong)
Abstract	An automatic diagnosis system of nuclear cataract is presented in this paper. Nuclear cataract is graded according to the severity of opacity using slit-lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model (ASM). Based on the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine (SVM) regression is employed to train a grading model for grade prediction. The system is tested using clinical images and clinical ground truth. More than five thousands slit-lamp images were tested. The success rate of feature extraction is 95% and the mean grading difference is 0.36. The automatic diagnosis system can help to improve the grading objectivity and save the workload of ophthalmologists.
Starting Page	3693
Ending Page	3696
File Size	784983
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5334735
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lenses Feature extraction Data mining Anatomical structure Flowcharts Support vector machines USA Councils Linear regression Neural networks Clinical diagnosis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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