Department of Electronic, Computer Science and Automatic Engineering, "La Rábida" High School of Engineering, University of Huelva, 21819 Palos de la Frontera, Spain.
Comput Biol Med. 2014 Dec;55:61-73. doi: 10.1016/j.compbiomed.2014.10.007. Epub 2014 Oct 18.
This paper presents a methodology for establishing the macular grading grid in digital retinal images by means of fovea centre detection. To this effect, visual and anatomical feature-based criteria are combined with the aim of exploiting the benefits of both techniques. First, acceptable fovea centre estimation is obtained by using a priori known anatomical features with respect to the optic disc and the vascular tree. Second, a type of morphological processing is employed in an attempt to improve the obtained fovea centre estimation when the fovea is detectable in the image; otherwise, it is declared indistinguishable and the first result is retained. The methodology was tested on the MESSIDOR and DIARETDB1 databases making use of a distance criterion between the obtained and the real fovea centre. Fovea centres in the brackets between the categories Excellent and Fair (fovea centres primarily accepted as valid in the literature) made up for 98.24% and 94.38% of the cases in the MESSIDOR and DIARETDB1, respectively.
本文提出了一种通过检测黄斑中心来建立数字视网膜图像黄斑分级网格的方法。为此，将基于视觉和解剖特征的标准相结合，旨在利用两种技术的优势。首先，通过使用与视盘和血管树相关的先验解剖特征，获得可接受的黄斑中心估计值。其次，采用某种形态学处理方法，试图在图像中可检测到黄斑时改进获得的黄斑中心估计值；否则，将其声明为不可区分，并保留第一个结果。该方法在 MESSIDOR 和 DIARETDB1 数据库上进行了测试，使用了获得的黄斑中心与真实黄斑中心之间的距离标准。在 MESSIDOR 和 DIARETDB1 中，类别为优秀和良好（文献中主要被接受为有效的黄斑中心）的黄斑中心分别占 98.24%和 94.38%。
