A computer-aided diagnosis system of nuclear cataract.
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.
Li
Huiqi
H
Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore. huiqili@i2r.a-star.edu.sg
Lim
Joo Hwee
JH
Liu
Jiang
J
Mitchell
Paul
P
Tan
Ava Grace
AG
Wang
Jie Jin
JJ
Wong
Tien Yin
TY
D000465:Algorithms; D001185:Artificial Intelligence; D002386:Cataract / Q000175:diagnosis* / Q000473:pathology; D018450:Disease Progression; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results