Case Western Reserve Univ., Cleveland, OH.
IEEE Trans Med Imaging. 1994;13(4):619-26. doi: 10.1109/42.363106.
An algorithm is presented for the analysis and quantification of the vascular structures of the human retina. Information about retinal blood vessel morphology is used in grading the severity and progression of a number of diseases. These disease processes are typically followed over relatively long time courses, and subjective analysis of the sequential images dictates the appropriate therapy for these patients. In this research, retinal fluorescein angiograms are acquired digitally in a 1024x1024 16-b image format and are processed using an automated vessel tracking program to identify and quantitate stenotic and/or tortuous vessel segments. The algorithm relies on a matched filtering approach coupled with a priori knowledge about retinal vessel properties to automatically detect the vessel boundaries, track the midline of the vessel, and extract useful parameters of clinical interest. By modeling the vessel profile using Gaussian functions, improved estimates of vessel diameters are obtained over previous algorithms. An adaptive densitometric tracking technique based on local neighborhood information is also used to improve computational performance in regions where the vessel is relatively straight.
提出了一种用于分析和量化人视网膜血管结构的算法。关于视网膜血管形态的信息用于对许多疾病的严重程度和进展进行分级。这些疾病过程通常是在相对较长的时间内进行跟踪，并且对序列图像的主观分析决定了这些患者的适当治疗方法。在这项研究中，视网膜荧光素血管造影术以 1024x1024 16 位图像格式数字化获取，并使用自动血管跟踪程序进行处理，以识别和量化狭窄和/或扭曲的血管段。该算法依赖于匹配滤波方法，并结合有关视网膜血管特性的先验知识，自动检测血管边界，跟踪血管的中线，并提取具有临床意义的有用参数。通过使用高斯函数对血管轮廓进行建模，可以获得比以前的算法更好的血管直径估计值。还使用基于局部邻域信息的自适应密度跟踪技术来提高血管相对直的区域的计算性能。
