Vermeer K A, Vos F M, Lemij H G, Vossepoel A M
Pattern Recognition Group, Delft university of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.
Comput Biol Med. 2004 Apr;34(3):209-19. doi: 10.1016/S0010-4825(03)00055-6.
Retinal blood vessels are important structures in ophthalmological images. Many detection methods are available, but the results are not always satisfactory. In this paper, we present a novel model based method for blood vessel detection in retinal images. It is based on a Laplace and thresholding segmentation step, followed by a classification step to improve performance. The last step assures incorporation of the inner part of large vessels with specular reflection. The method gives a sensitivity of 92% with a specificity of 91%. The method can be optimized for the specific properties of the blood vessels in the image and it allows for detection of vessels that appear to be split due to specular reflection.
视网膜血管是眼科图像中的重要结构。有许多检测方法可用，但结果并不总是令人满意。在本文中，我们提出了一种基于模型的新颖方法用于视网膜图像中的血管检测。它基于拉普拉斯和阈值分割步骤，随后是一个分类步骤以提高性能。最后一步确保纳入具有镜面反射的大血管内部。该方法的灵敏度为92%，特异性为91%。该方法可以针对图像中血管的特定属性进行优化，并且能够检测因镜面反射而看似分裂的血管。
