Chow Yew Chung, Gao Xinting, Li Huiqi, Lim Joo Hwee, Sun Ying, Wong Tien Yin
National University of Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5044-7. doi: 10.1109/IEMBS.2011.6091249.
Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the performance from three aspects, namely ROI detection, lens mask generation and opacity detection. In the detection method, image clipping and texture analysis are applied to overcome the over-detection problem for clear lens images and global thresholding is exploited to solve the under-detection problem for severe cataract images. The proposed method is tested on 725 retro-illumination lens images randomly selected from a database of a community study. Experiments show improved performance compared with the state-of-the-art method.
白内障仍然是全球失明的主要原因。通过人工分级进行白内障诊断具有主观性且耗时。目前有几种自动分级方法，但每种方法都存在一些缺点。本文提出了一种基于纹理和强度分析的自动检测新方法，以解决现有方法的问题，并从感兴趣区域（ROI）检测、晶状体掩码生成和混浊度检测三个方面提高性能。在检测方法中，应用图像裁剪和纹理分析来克服清晰晶状体图像的过度检测问题，并利用全局阈值处理来解决严重白内障图像的检测不足问题。该方法在从一项社区研究数据库中随机选取的725张后照式晶状体图像上进行了测试。实验表明，与现有最先进方法相比，该方法性能有所提高。
