IEEE Trans Med Imaging. 2024 Feb;43(2):760-770. doi: 10.1109/TMI.2023.3320977. Epub 2024 Feb 2.
An improved analysis of Optical Coherence Tomography (OCT) images of the retina is of essential importance for the correct diagnosis of retinal abnormalities. Unfortunately, OCT images suffer from noise arising from different sources. In particular, speckle noise caused by the scattering of light waves strongly degrades the quality of OCT image acquisitions. In this paper, we employ a Modified Morphological Component Analysis (MMCA) to provide a new method that separates the image into components that contain different features as texture, piecewise smooth parts, and singularities along curves. Each image component is computed as a sparse representation in a suitable dictionary. To create these dictionaries, we use non-data-adaptive multi-scale ( X -let) transforms which have been shown to be well suitable to extract the special OCT image features. In this way, we reach two goals at once. On the one hand, we achieve strongly improved denoising results by applying adaptive local thresholding techniques separately to each image component. The denoising performance outperforms other state-of-the-art denoising algorithms regarding the PSNR as well as no-reference image quality assessments. On the other hand, we obtain a decomposition of the OCT images in well-interpretable image components that can be exploited for further image processing tasks, such as classification.
对视网膜的光学相干断层扫描（OCT）图像进行改进分析对于正确诊断视网膜异常至关重要。不幸的是，OCT 图像会受到来自不同来源的噪声的影响。特别是，由于光波散射引起的散斑噪声会严重降低 OCT 图像采集的质量。在本文中，我们采用改进的形态成分分析（MMCA）来提供一种新的方法，将图像分成包含不同特征的成分，如纹理、分段平滑部分和沿曲线的奇异点。每个图像成分都作为合适字典中的稀疏表示来计算。为了创建这些字典，我们使用非数据自适应多尺度（X-let）变换，事实证明，这些变换非常适合提取特殊的 OCT 图像特征。通过这种方式，我们同时实现了两个目标。一方面，我们通过分别对每个图像成分应用自适应局部阈值技术来实现显著的去噪效果。去噪性能在 PSNR 以及无参考图像质量评估方面优于其他最先进的去噪算法。另一方面，我们获得了 OCT 图像的可解释性成分分解，可用于进一步的图像处理任务，如分类。
