Hernandez-Matas Carlos, Zabulis Xenophon, Argyros Antonis A
Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, 70013 Greece; Computer Science Department, University of Crete, Heraklion, 70013 Greece.
Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, 70013 Greece.
Comput Methods Programs Biomed. 2021 Feb;199:105900. doi: 10.1016/j.cmpb.2020.105900. Epub 2020 Dec 17.
The study of small vessels allows for the analysis and diagnosis of diseases with strong vasculopathy. This type of vessels can be observed non-invasively in the retina via fundoscopy. The analysis of these vessels can be facilitated by applications built upon Retinal Image Registration (RIR), such as mosaicing, Super Resolution (SR) or eye shape estimation. RIR is challenging due to possible changes in the retina across time, the utilization of diverse acquisition devices with varying properties, or the curved shape of the retina.
We employ the Retinal Image Registration through Eye Modelling and Pose Estimation (REMPE) framework, which simultaneously estimates the cameras' relative poses, as well as eye shape and orientation to develop RIR applications and to study their effectiveness.
We assess quantitatively the suitability of the REMPE framework towards achieving SR and eye shape estimation. Additionally, we provide indicative results demonstrating qualitatively its usefulness in the context of longitudinal studies, mosaicing, and multiple image registration. Besides the improvement over registration accuracy, demonstrated via registration applications, the most important novelty presented in this work is the eye shape estimation and the generation of 3D point meshes. This has the potential for allowing clinicians to perform measurements on 3D representations of the eye, instead of doing so in 2D images that contain distortions induced because of the projection on the image space.
RIR is very effective in supporting applications such as SR, eye shape estimation, longitudinal studies, mosaicing and multiple image registration. Its improved registration accuracy compared to the state of the art translates directly in improved performance when supporting the aforementioned applications.
对小血管的研究有助于分析和诊断具有严重血管病变的疾病。这类血管可通过检眼镜在视网膜上进行非侵入性观察。基于视网膜图像配准（RIR）构建的应用，如拼接、超分辨率（SR）或眼睛形状估计，可促进对这些血管的分析。由于视网膜随时间可能发生变化、使用具有不同特性的多种采集设备或视网膜的弯曲形状，RIR具有挑战性。
我们采用通过眼睛建模和姿态估计进行视网膜图像配准（REMPE）框架，该框架同时估计相机的相对姿态以及眼睛形状和方向，以开发RIR应用并研究其有效性。
我们定量评估了REMPE框架对实现SR和眼睛形状估计的适用性。此外，我们提供了指示性结果，定性地证明了其在纵向研究、拼接和多图像配准方面的有用性。除了通过配准应用证明的配准精度提高外，这项工作中最重要的新颖之处在于眼睛形状估计和3D点网格的生成。这有可能使临床医生能够在眼睛的3D表示上进行测量，而不是在因图像空间投影而包含失真的2D图像中进行测量。
RIR在支持诸如SR、眼睛形状估计、纵向研究、拼接和多图像配准等应用方面非常有效。与现有技术相比，其提高的配准精度在支持上述应用时直接转化为性能的提升。
