Nasehi Tehrani Joubin, Yan Hong, Zhu Meidong, Jin Craig, McEwan Alistair L
School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1434-7. doi: 10.1109/EMBC.2012.6346209.
Manual measurements of small changes in retinal vascular diameter are slow and may be subject to considerable observer-related biases. Among the conventional automatic methods the sliding linear regression filter (SLRF) demonstrates the least scattered and most repeatable coefficients. For optimal performance it relies on the choice of the correct filter scale for different vessel sizes. A small scale extracts fine details at the expense noise sensitivity, while large scales have poor edge localization. Here we use auto scale phase congruency to select the filter scales with fuzzy weighting to reduce noise, and L1 regularization for edge smoothing. Our method uses a one dimensional analysis normal to the vessel and so is faster than the 2D phase congruency. In 65 vessels randomly selected from 20 images the proposed method showed better repeatability and over three times less scattering than conventional SLRF.
手动测量视网膜血管直径的微小变化速度缓慢,且可能存在相当大的与观察者相关的偏差。在传统的自动方法中,滑动线性回归滤波器(SLRF)表现出的系数离散度最小且最具可重复性。为实现最佳性能,它依赖于针对不同血管大小选择正确的滤波器尺度。小尺度能提取精细细节,但以噪声敏感性为代价,而大尺度的边缘定位较差。在此,我们使用自动尺度相位一致性来选择具有模糊加权的滤波器尺度以降低噪声,并使用L1正则化进行边缘平滑。我们的方法使用垂直于血管的一维分析,因此比二维相位一致性更快。在从20幅图像中随机选取的65条血管上,所提出的方法显示出更好的可重复性,且离散度比传统SLRF少三倍以上。