Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore.
IEEE Trans Biomed Eng. 2010 Jul;57(7):1690-8. doi: 10.1109/TBME.2010.2041454. Epub 2010 Feb 17.
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.
白内障是全球致盲的主要原因，而核性白内障是最常见的白内障类型。本文研究了一种用于自动诊断核性白内障的算法。使用裂隙灯镜头图像，根据混浊的严重程度对核性白内障进行分级。使用改进的主动形状模型检测镜头图像中的解剖结构。根据临床分级方案，在解剖学标记的基础上提取局部特征。采用支持向量机回归进行等级预测。这是第一次可以在裂隙灯图像中自动检测到核区。该系统使用>5000 张临床图像和临床真实数据进行验证。结构检测的成功率为 95%，在 5.0 级评分上的平均分级差异为 0.36。自动诊断系统可以提高分级的客观性，并有可能在临床和人群研究中使用，以减轻眼科医生的工作量。
