Instituto de Informática, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, CEP. 91509-900, Porto Alegre, RS, Brazil.
Comput Methods Programs Biomed. 2011 Dec;104(3):397-409. doi: 10.1016/j.cmpb.2010.07.006. Epub 2010 Sep 16.
In this work, we present a new fovea center detection method for color eye fundus images. This method is based on known anatomical constraints on the relative locations of retina structures, and mathematical morphology. The detection of this anatomical feature is a prerequisite for the computer aided diagnosis of several retinal diseases, such as Diabetic Macular Edema. The proposed method is adaptive to local illumination changes, and it is robust to local disturbances introduced by pathologies in digital color eye fundus images (e.g. exudates). Our experimental results using the DRIVE image database indicate that our method is able to detect the fovea center in 37 out of 37 images (i.e. with a success rate of 100%). Using the DIARETDB1 database, our method was able to detect the fovea center in 92.13% of all tested cases (i.e. in 82 out of 89 images). These results indicate that our approach potentially can achieve a better performance than comparable methods proposed in the literature.
在这项工作中，我们提出了一种新的彩色眼底图像中央凹检测方法。该方法基于视网膜结构相对位置的已知解剖约束和数学形态学。该解剖特征的检测是计算机辅助诊断几种视网膜疾病（如糖尿病性黄斑水肿）的前提。所提出的方法适应于局部光照变化，并且对数字彩色眼底图像中的局部病变（例如渗出物）引起的局部干扰具有鲁棒性。我们使用 DRIVE 图像数据库的实验结果表明，我们的方法能够在 37 张图像中的 37 张（即成功率为 100％）中检测到中央凹中心。使用 DIARETDB1 数据库，我们的方法能够在所有测试病例（即 89 张图像中的 82 张）中检测到中央凹中心的 92.13％。这些结果表明，与文献中提出的可比方法相比，我们的方法可能具有更好的性能。
