An automatic diagnosis system of nuclear cataract using slit-lamp images.
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.
Li
Huiqi
H
Institute for Infocomm Research, A*STAR, Connexis, Singapore. huiqili@i2r.a-star.edu.sg
Lim
Joo Hwee
JH
Liu
Jiang
J
Wong
Damon Wing Kee
DW
Tan
Ngan Meng
NM
Lu
Shijian
S
Zhang
Zhuo
Z
Wong
Tien Yin
TY
D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D002386:Cataract / Q000175:diagnosis* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted; D003952:Diagnostic Imaging / Q000379:methods; D003941:Diagnostic Techniques, Ophthalmological; D001330:Electronic Data Processing; D006801:Humans; D007908:Lens, Crystalline / Q000473:pathology; D012044:Regression Analysis; D015203:Reproducibility of Results; D012984:Software