Das Vineeta, Dandapat Samarendra, Bora Prabin Kumar
Electro Medical and Speech Technology Lab, Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati 781039, India.
Comput Med Imaging Graph. 2021 Dec;94:101997. doi: 10.1016/j.compmedimag.2021.101997. Epub 2021 Oct 1.
High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagnostic information. Therefore, researchers have a growing interest in developing image processing techniques to recover HR images from low-resolution (LR) OCT images. In this paper, we present an automated super-resolution (SR) scheme using diagnostic information weighted sparse representation framework to reconstruct HR images from LR OCT images. The proposed method performs fast and reliable reconstruction of the LR images. We also propose a 2D- variational mode decomposition (VMD) based OCT diagnostic distortion measure (Q) to quantify diagnostic distortion in the reconstructed OCT images. The SR method is evaluated on clinical grade OCT images with the proposed diagnostic distortion measure along with the conventional non-diagnostic measures like the contrast to noise ratio (CNR), the equivalent number of looks (ENL) and the peak signal to noise ratio (PSNR). The results show an average CNR of 4.07, ENL of 58.96 and PSNR of 27.72 dB. An average score of 1.53 is obtained using the proposed diagnostic distortion measure. Experimental results quantify that the proposed Q metric can effectively capture diagnostic distortion.
眼科医生更喜欢使用高分辨率（HR）视网膜光学相干断层扫描（OCT）图像来诊断视网膜疾病。这些图像可以在采集过程中通过对目标视网膜区域进行密集扫描获得。然而，密集扫描会增加图像采集时间并引入运动伪影，从而破坏诊断信息。因此，研究人员对开发图像处理技术以从低分辨率（LR）OCT图像中恢复HR图像的兴趣与日俱增。在本文中，我们提出了一种自动超分辨率（SR）方案，该方案使用诊断信息加权稀疏表示框架从LR OCT图像重建HR图像。所提出的方法能够快速且可靠地重建LR图像。我们还提出了一种基于二维变分模态分解（VMD）的OCT诊断失真度量（Q），以量化重建的OCT图像中的诊断失真。使用所提出的诊断失真度量以及传统的非诊断度量（如对比度噪声比（CNR）、等效视数（ENL）和峰值信噪比（PSNR））对临床级OCT图像上的SR方法进行评估。结果显示平均CNR为4.07，ENL为58.96，PSNR为27.72 dB。使用所提出的诊断失真度量获得的平均分数为1.53。实验结果表明，所提出的Q度量可以有效地捕捉诊断失真。
