A Computer-Aided Diagnosis System of Nuclear Cataract

被引：70

作者：

Li, Huiqi ^{[1
]}

Lim, Joo Hwee ^{[1
]}

Liu, Jiang ^{[1
]}

Mitchell, Paul ^{[2
]}

Tan, Ava Grace ^{[2
]}

Wang, Jie Jin ^{[3
]}

Wong, Tien Yin ^{[3
,4
]}

机构：

[1] Inst Infocomm Res, Agcy Sci Technol & Res, Singapore 138632, Singapore

[2] Univ Sydney, Ctr Vis Res, Dept Ophthalmol, Sydney, NSW 2001, Australia

[3] Univ Melbourne, Ctr Eye Res Australia, Melbourne, Vic 3002, Australia

[4] Natl Univ Singapore, Singapore Eye Res Inst, Singapore 119077, Singapore

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2010年 / 57卷 / 07期

基金：

英国医学研究理事会;

关键词：

Automatic grading; computer-aided diagnosis; nuclear cataract; slit lamp image; SINGAPORE MALAY EYE; CLASSIFICATION-SYSTEM; POPULATION; PREVALENCE; DISEASES;

D O I：

10.1109/TBME.2010.2041454

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated 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. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.

引用

页码：1690 / 1698

页数：9

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