Lowell James, Hunter Andrew, Steel David, Basu Ansu, Ryder Robert, Fletcher Eric, Kennedy Lee
Department of Computer Science, University of Durham, Durham DH1 3LE, UK.
IEEE Trans Med Imaging. 2004 Feb;23(2):256-64. doi: 10.1109/TMI.2003.823261.
Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 microns/pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.
可靠且高效的视盘定位与分割是自动视网膜筛查中的重要任务。通用边缘检测算法由于边界模糊、图像对比度不一致或边缘特征缺失，常常无法对视盘进行分割。本文提出了一种用于低分辨率图像（约20微米/像素）中视神经头边界定位与分割的算法。视盘定位通过专门的模板匹配实现，分割则借助可变形轮廓模型。后者使用全局椭圆模型和具有可变边缘强度相关刚度的局部可变形模型。该算法针对从糖尿病筛查项目中随机选取的100幅图像数据库进行评估。有10幅图像被归类为不可用；其他图像质量参差不齐。定位算法在除一幅可用图像外的所有图像上均成功；轮廓估计算法经眼科医生定性评估，在83%的病例中表现为优至良，甚至在模糊图像上也表现良好。
