统计几何特征在微动脉瘤检测中的应用。
Statistical Geometrical Features for Microaneurysm Detection.
机构信息
Department of Information Technology, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, 431606, India.
Department of Electronics & Telecommunication, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, 431606, India.
出版信息
J Digit Imaging. 2018 Apr;31(2):224-234. doi: 10.1007/s10278-017-0008-0.
Automated microaneurysm (MA) detection is still an open challenge due to its small size and similarity with blood vessels. In this paper, we present a novel method which is simple, efficient, and real-time for segmenting and detecting MA in color fundus images (CFI). To do this, a novel set of features based on statistics of geometrical properties of connected regions, that can easily discriminate lesion and non-lesion pixels are used. For large-scale evaluation proposed method is validated on DIARETDB1, ROC, STARE, and MESSIDOR dataset. It proves robust with respect to different image characteristics and camera settings. The best performance was achieved on per-image evaluation on DIARETDB1 dataset with sensitivity of 88.09 at 92.65% specificity which is quite encouraging for clinical use.
自动微动脉瘤（MA）检测仍然是一个开放的挑战，因为它的体积小，与血管相似。在本文中，我们提出了一种新的方法，用于分割和检测彩色眼底图像（CFI）中的 MA，该方法简单、高效、实时。为此，使用了一组基于连通区域几何特性统计的新特征，这些特征可以很容易地区分病变和非病变像素。为了进行大规模评估，我们在 DIARETDB1、ROC、STARE 和 MESSIDOR 数据集上验证了所提出的方法。它在不同的图像特征和相机设置下表现出很强的稳健性。在 DIARETDB1 数据集上的逐图像评估中取得了最佳性能，灵敏度为 88.09%，特异性为 92.65%，这对于临床应用来说是非常有希望的。
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