J Biomed Opt. 2017 Aug;22(8):1-12. doi: 10.1117/1.JBO.22.8.086011.
We demonstrate the power of the Gaussianization transform (GT) for modeling image content by applying GT for optical coherence tomography (OCT) denoising. The proposed method is a developed version of the spatially constrained Gaussian mixture model (SC-GMM) method, which assumes that each cluster of similar patches in an image has a Gaussian distribution. SC-GMM tries to find some clusters of similar patches in the image using a spatially constrained patch clustering and then denoise each cluster by the Wiener filter. Although in this method GMM distribution is assumed for the noisy image, holding this assumption on a dataset is not investigated. We illustrate that making a Gaussian assumption on a noisy dataset has a significant effect on denoising results. For this purpose, a suitable distribution for OCT images is first obtained and then GT is employed to map this original distribution of OCT images to a GMM distribution. Then, this Gaussianized image is used as the input of the SC-GMM algorithm. This method, which is a combination of GT and SC-GMM, remarkably improves the results of OCT denoising compared with earlier version of SC-GMM and even produces better visual and numerical results than the state-of-the art works in this field. Indeed, the main advantage of the proposed OCT despeckling method is texture preservation, which is important for main image processing tasks like OCT inter- and intraretinal layer analysis. Thus, to prove the efficacy of the proposed method for this analysis, an improvement in the segmentation of intraretinal layers using the proposed method as a preprocessing step is investigated. Furthermore, the proposed method can achieve the best expert ranking between other contending methods, and the results show the helpfulness and usefulness of the proposed method in clinical applications.
我们通过将高斯化变换（GT）应用于光学相干断层扫描（OCT）去噪，展示了高斯化变换在图像内容建模方面的强大功能。所提出的方法是空间约束高斯混合模型（SC - GMM）方法的改进版本，该方法假设图像中每个相似补丁簇具有高斯分布。SC - GMM试图通过空间约束补丁聚类在图像中找到一些相似补丁簇，然后使用维纳滤波器对每个簇进行去噪。尽管在该方法中对噪声图像假设了高斯混合模型（GMM）分布，但并未研究在数据集上保持这一假设的情况。我们表明，对噪声数据集做出高斯假设对去噪结果有显著影响。为此，首先获得适合OCT图像的分布，然后使用GT将OCT图像的原始分布映射到GMM分布。然后，将这个高斯化的图像用作SC - GMM算法的输入。这种将GT和SC - GMM相结合的方法，与早期版本的SC - GMM相比，显著提高了OCT去噪的结果，甚至在视觉和数值结果上比该领域的现有技术作品更好。实际上所提出的OCT去斑方法的主要优点是纹理保留，这对于诸如OCT视网膜层间和层内分析等主要图像处理任务很重要。因此，为了证明所提出的方法在此分析中的有效性，研究了将所提出的方法作为预处理步骤在视网膜内层分割方面的改进。此外，所提出的方法在其他竞争方法中可以获得最佳的专家排名，结果表明所提出的方法在临床应用中的实用性和有效性。
