Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, 46022 Valencia, Spain.
IEEE Trans Med Imaging. 2013 Apr;32(4):786-96. doi: 10.1109/TMI.2013.2238244. Epub 2013 Jan 9.
The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.
本文提出的算法允许从眼底图像中自动分割视盘。其目的是促进某些疾病的早期检测，并实现完全自动化，以避免专家干预。所提出的用于提取视盘轮廓的方法主要基于数学形态学和主成分分析（PCA）。它利用了不同的操作，如广义距离函数（GDF）、分水岭变换的变体、随机分水岭和测地线变换。分割方法的输入是通过 PCA 获得的。使用 PCA 的目的是获得能够更好地表示原始 RGB 图像的灰度图像。所实现的算法已经在五个公共数据库上进行了验证，取得了有前景的结果。所获得的平均值（Jaccard 和 Dice 系数分别为 0.8200 和 0.8932，准确性为 0.9947，真阳性和假阳性分数分别为 0.9275 和 0.0036）表明，该方法是自动分割视盘的一种稳健工具。此外，它还相当可靠，因为它在具有较大变异性的数据库上能够正常工作，并能提高其他最先进方法的结果。
