Li Huiqi, Chutatape Opas
Department of Computer Science, School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543.
IEEE Trans Biomed Eng. 2004 Feb;51(2):246-54. doi: 10.1109/TBME.2003.820400.
Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed. The success rates of disk localization, disk boundary detection, and fovea localization are 99%, 94%, and 100%, respectively. The sensitivity and specificity of exudate detection are 100% and 71%, correspondingly. The success of the proposed algorithms can be attributed to the utilization of the model-based methods. The detection and analysis could be applied to automatic mass screening and diagnosis of the retinal diseases.
彩色视网膜摄影是检测各种眼部疾病证据的重要工具。本文开发了提取彩色视网膜图像主要特征的新方法。采用主成分分析来定位视盘；在视盘形状检测中提出了一种改进的主动形状模型；建立了眼底坐标系以更好地描述视网膜图像中的特征；提出了一种通过结合区域生长和边缘检测来检测渗出物的方法。视盘定位、视盘边界检测和黄斑定位的成功率分别为99%、94%和100%。渗出物检测的敏感性和特异性分别为100%和71%。所提算法的成功可归因于基于模型方法的运用。该检测与分析可应用于视网膜疾病的自动大规模筛查与诊断。
