Bauman Moscow State Technical University, Laboratory of Terahert Technology, Moscow, Russia.
Sechenov First Moscow State Medical University, Moscow, Russia.
J Biomed Opt. 2018 Apr;23(9):1-9. doi: 10.1117/1.JBO.23.9.091406.
We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.
我们提出了一种基于实验训练的基于小波域的光相干层析成像（OCT）去噪方法。该方法采用基于测试物体成像的实验训练算法，该测试物体由单分散纳米粒子的胶体悬浮液和包含微尺度夹杂的胶体悬浮液组成。测试物体的几何形状和散射特性是预先知道的，这使得我们能够为训练算法设置标准。使用广泛的小波核和小波域滤波方法，根据测试物体成像构建适当的滤波器。我们应用了所提出的方法，并通过考虑来自五个小波族的分解基与八种滤波阈值类型的组合，选择了一种有效的小波去噪过程。我们证明了小波滤波在人脑脑膜瘤的体外 OCT 图像中的适用性。观察到的结果证明了所提出的 OCT 图像去噪技术的高效性。
