Splat feature classification: Detection of the presence of large retinal hemorrhages
Splat feature classification: Detection of the presence of large retinal hemorrhages
复制标题
Splat特征分类：检测是否存在大的视网膜出血
DOI：
10.1109/isbi.2011.5872498
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
M. Abràmoff
中科院分区：
文献类型：
--
作者：
L. Tang;M. Niemeijer;M. Abràmoff
可靠地检测大量视网膜出血对于开发可转化为实践的自动筛查系统非常重要。在本研究中，我们提出了一种基于splat特征分类的新型大视网膜出血检测方法。眼底照片被分割成覆盖整个图像的多个块。每个图块包含具有相似颜色和接近空间位置的像素。在每个图块中提取一组不同的特征。通过学习血管形成的碎片的特性，训练了一个分类器，使其能够区分血液碎片和非血液碎片。一旦血斑，即脉管系统和出血，与背景分离，连接的脉管系统被移除，并且剩余的对象被认为是出血候选者。与人类专家相比，我们的方法在由 1200 张图像组成的测试集上表现令人满意。
Reliable detection of large retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. In this study, we propose a novel large retinal hemorrhages detection method based on splat feature classification. Fundus photographs are partitioned into a number of splats covering the entire image. Each splat contains pixels with similar color and close spatial location. A set of distinct features is extracted within each splat. By learning properties of splats formed from blood vessels, a classifier was trained so that it can distinguish blood splats from non-blood splats. Once the blood splats, i.e. vasculature and hemorrhages, are separated from the background, the connected vasculature was removed and the remaining objects considered hemorrhage candidates. Our approach had a satisfactory performance on a test set composed of 1200 images compared to a human expert.