@inproceedings{fffb50b6a5de46d5bd558cb3af131605,
title = "An automatic diagnosis system of nuclear cataract using slit-lamp images",
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.",
author = "Huiqi Li and Lim, {Joo Hwee} and Jiang Liu and Wong, {Damon Wing Kee} and Tan, {Ngan Meng} and Shijian Lu and Zhuo Zhang and Wong, {Tien Yin}",
year = "2009",
doi = "10.1109/IEMBS.2009.5334735",
language = "English",
isbn = "9781424432967",
series = "Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009",
publisher = "IEEE Computer Society",
pages = "3693--3696",
booktitle = "Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
note = "31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; Conference date: 02-09-2009 Through 06-09-2009",
}