Intensity inhomogeneity correction of SD-OCT data using macular flatspace.
Intensity inhomogeneity correction of SD-OCT data using macular flatspace.
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DOI：
10.1016/j.media.2017.09.008
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发表时间：
2018-01
影响因子：
10.9
通讯作者：
Prince JL
中科院分区：
文献类型：
--
作者：
Lang A;Carass A;Jedynak BM;Solomon SD;Calabresi PA;Prince JL
使用光学相干断层扫描（OCT）获取的视网膜图像经常遭受强度不均匀性问题，该问题降低了图像的质量和用于测量结构变化的自动算法的性能。这种强度变化有许多原因，包括离轴采集、信号衰减、多帧平均和渐晕，使得难以以基本方式校正数据。本文提出了一种通过减少各层内光强变化来校正非均匀性的方法。特别是，N3算法，这是流行的神经图像分析，适用于OCT数据。N3通过锐化强度直方图来工作，这减少了不同类别中强度的变化。为了将其应用于此，首先将数据转换为称为黄斑平坦空间（MFS）的标准化空间。MFS允许通过去除视网膜的自然曲率来更容易地归一化每层内的强度。然后使用修改的平滑模型在MFS数据上运行N3，这提高了原始算法的效率。我们表明，我们的方法更准确地纠正合成OCT数据的增益字段相比，运行N3非平整数据。它还降低了每层内强度的总体变化性，而不牺牲层之间的对比度，并提高了OCT图像之间的配准性能。
Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images.
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影响因子：
4.4
作者：
Girard, Michael J. A.;Strouthidis, Nicholas G.;Mari, Jean Martial
通讯作者：
Mari, Jean Martial
影响因子：
3.4
作者：
Carass, Aaron;Lang, Andrew;Prince, Jerry L.
通讯作者：
Prince, Jerry L.
影响因子：
3.8
作者：
Kaba, D.;Wang, Y.;Li, Y.
通讯作者：
Li, Y.
影响因子：
4.4
作者：
Ishikawa, H;Stein, DM;Schuman, JS
通讯作者：
Schuman, JS
DOI：
10.1109/isbi.2016.7493243
发表时间：
2016-04
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
--
作者：
Lang A;Carass A;Jedynak BM;Solomon SD;Calabresi PA;Prince JL
通讯作者：
Prince JL