Lee Noah, Laine Andrew F, Smith R Theodore
Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, CO 80305, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:4965-8. doi: 10.1109/IEMBS.2007.4353455.
Fundus auto-fluorescence (FAF) images with hypo-fluorescence indicate geographic atrophy (GA) of the retinal pigment epithelium (RPE) in age-related macular degeneration (AMD). Manual quantification of GA is time consuming and prone to inter- and intra-observer variability. Automatic quantification is important for determining disease progression and facilitating clinical diagnosis of AMD. In this paper we describe a hybrid segmentation method for GA quantification by identifying hypo-fluorescent GA regions from other interfering retinal vessel structures. First, we employ background illumination correction exploiting a non-linear adaptive smoothing operator. Then, we use the level set framework to perform segmentation of hypo-fluorescent areas. Finally, we present an energy function combining morphological scale-space analysis with a geometric model-based approach to perform segmentation refinement of false positive hypo- fluorescent areas due to interfering retinal structures. The clinically apparent areas of hypo-fluorescence were drawn by an expert grader and compared on a pixel by pixel basis to our segmentation results. The mean sensitivity and specificity of the ROC analysis were 0.89 and 0.98%.
眼底自发荧光（FAF）图像中的低荧光表明年龄相关性黄斑变性（AMD）中视网膜色素上皮（RPE）的地图样萎缩（GA）。手动量化GA耗时且容易出现观察者间和观察者内的差异。自动量化对于确定疾病进展和促进AMD的临床诊断很重要。在本文中，我们描述了一种通过从其他干扰性视网膜血管结构中识别低荧光GA区域来进行GA量化的混合分割方法。首先，我们利用非线性自适应平滑算子进行背景照明校正。然后，我们使用水平集框架对低荧光区域进行分割。最后，我们提出了一种能量函数，将形态学尺度空间分析与基于几何模型的方法相结合，以对由于干扰性视网膜结构导致的假阳性低荧光区域进行分割细化。由专业分级人员绘制临床上明显的低荧光区域，并逐像素地与我们的分割结果进行比较。ROC分析的平均敏感性和特异性分别为0.89和0.98%。
