Gregson P H, Shen Z, Scott R C, Kozousek V
Department of Electrical Engineering, Technical University of Nova Scotia, Halifax, Canada.
Comput Biomed Res. 1995 Aug;28(4):291-304. doi: 10.1006/cbmr.1995.1020.
The degree of venous beading in ocular fundus images has been shown to be a more powerful predictor of conversion to proliferative diabetic retinopathy than any other type of retinal abnormality. Further, the degree of venous beading has been shown to be well correlated with disease progression. An algorithm for automated grading of venous beading in digitized ocular fundus images is described. Thresholding is used to extract a rough silhouette of the vein. Morphological closing is used to fill any holes in the silhouette arising from either the central light reflex or noise. The silhouette is then "thinned" to find vein centerlines. Each centerline is partitioned into fixed-length segments of 32 pixels. Vein diameters are measured as a function of distance along each segment with the aid of the local centerline orientations. The resulting diameter data are then interpolated and resampled to generate diameter data at constant sampling intervals. A fast Fourier transform is performed on the resulting data to determine the magnitude spectrum of vein segment diameter. A venous beading index is calculated from the distribution of vein diameter frequency components. Performance of the new algorithm is compared to the currently accepted clinical practice of manual grading in a pilot clinical study of 51 subjects. The algorithm is seen to perform well.
眼底图像中静脉串珠的程度已被证明是比任何其他类型的视网膜异常更有力的增殖性糖尿病视网膜病变转化预测指标。此外，静脉串珠的程度已被证明与疾病进展密切相关。本文描述了一种用于数字化眼底图像中静脉串珠自动分级的算法。通过阈值处理来提取静脉的大致轮廓。形态学闭运算用于填充轮廓中由中央光反射或噪声产生的任何空洞。然后将轮廓“细化”以找到静脉中心线。每条中心线被划分为32像素的固定长度段。借助局部中心线方向，沿着每个段测量静脉直径作为距离的函数。然后对所得直径数据进行插值和重采样，以生成恒定采样间隔下的直径数据。对所得数据进行快速傅里叶变换，以确定静脉段直径的幅度谱。根据静脉直径频率成分的分布计算静脉串珠指数。在一项对51名受试者的初步临床研究中，将新算法的性能与目前公认的手动分级临床实践进行了比较。该算法表现良好。
