Girard Michaël J A, Ang Marcus, Chung Cheuk Wang, Farook Mohamed, Strouthidis Nick, Mehta Jod S, Mari Jean Martial
In vivo Biomechanics Laboratory Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore ; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Transl Vis Sci Technol. 2015 May 15;4(3):3. doi: 10.1167/tvst.4.3.3. eCollection 2015 May.
To improve the contrast of optical coherence tomography (OCT) images of the cornea (post processing).
We have recently developed standard compensation (SC) algorithms to remove light attenuation artifacts. A more recent approach, namely adaptive compensation (AC), further limited noise overamplification within deep tissue regions. AC was shown to work efficiently when all A-scan signals were fully attenuated at high depth. But in many imaging applications (e.g., OCT imaging of the cornea), such an assumption is not satisfied, which can result in strong noise overamplification. A corneal adaptive compensation (CAC) algorithm was therefore developed to overcome such limitation. CAC benefited from local A-scan processing (rather than global as in AC) and its performance was compared with that of SC and AC using Fourier-domain OCT images of four human corneas.
CAC provided considerably superior image contrast improvement than SC or AC did, with excellent visibility of the corneal stroma, low noise overamplification, homogeneous signal amplification, and high contrast. Specifically, CAC provided mean interlayer contrasts (a measure of high stromal visibility and low noise) greater than 0.97, while SC and AC provided lower values ranging from 0.38 to 1.00.
CAC provided considerable improvement compared with SC and AC by eliminating noise overamplification, while maintaining all benefits of compensation, thus making the corneal endothelium and corneal thickness easily identifiable.
CAC may find wide applicability in clinical practice and could contribute to improved morphometric and biomechanical understanding of the cornea.
改善角膜光学相干断层扫描（OCT）图像的对比度（后处理）。
我们最近开发了标准补偿（SC）算法以去除光衰减伪影。一种更新的方法，即自适应补偿（AC），进一步限制了深部组织区域内的噪声过度放大。当所有A扫描信号在高深度处完全衰减时，AC被证明能有效工作。但在许多成像应用中（例如角膜的OCT成像），这种假设并不成立，这可能导致强烈的噪声过度放大。因此，开发了一种角膜自适应补偿（CAC）算法来克服这种限制。CAC受益于局部A扫描处理（而非AC中的全局处理），并使用四张人角膜的傅里叶域OCT图像将其性能与SC和AC的性能进行了比较。
与SC或AC相比，CAC在改善图像对比度方面具有显著优势，角膜基质的可见性极佳，噪声过度放大低，信号放大均匀，对比度高。具体而言，CAC提供的平均层间对比度（衡量高基质可见性和低噪声的指标）大于0.97，而SC和AC提供的较低值范围为0.38至1.00。
与SC和AC相比，CAC通过消除噪声过度放大提供了显著改善，同时保留了补偿的所有优点，从而使角膜内皮和角膜厚度易于识别。
CAC可能在临床实践中找到广泛应用，并有助于提高对角膜形态学和生物力学的理解。
