Davidson Benjamin, Kalitzeos Angelos, Carroll Joseph, Dubra Alfredo, Ourselin Sebastien, Michaelides Michel, Bergeles Christos
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK.
Biomed Opt Express. 2018 Aug 15;9(9):4317-4328. doi: 10.1364/BOE.9.004317. eCollection 2018 Sep 1.
The field of view of high-resolution ophthalmoscopes that require the use of adaptive optics (AO) wavefront correction is limited by the isoplanatic patch of the eye, which varies across individual eyes and with the portion of the pupil used for illumination and/or imaging. Therefore all current AO ophthalmoscopes have small fields of view comparable to, or smaller than, the isoplanatic patch, and the resulting images have to be stitched off-line to create larger montages. These montages are currently assembled either manually, by expert human graders, or automatically, often requiring several hours per montage. This arguably limits the applicability of AO ophthalmoscopy to studies with small cohorts and moreover, prevents the ability to review a real-time captured montage of all locations during image acquisition to further direct targeted imaging. In this work, we propose stitching the images with our novel algorithm, which uses oriented fast rotated brief (ORB) descriptors, local sensitivity hashing, and by searching for a 'good enough' transformation, rather than the best possible, to achieve processing times of 1-2 minutes per montage of 250 images. Moreover, the proposed method produces montages which are as accurate as previous methods, when considering the image similarity metrics: normalised mutual information (NMI), and normalised cross correlation (NCC).
需要使用自适应光学(AO)波前校正的高分辨率检眼镜的视野受眼睛等晕区的限制,该区域在不同个体眼睛之间以及随着用于照明和/或成像的瞳孔部分而变化。因此,目前所有的AO检眼镜的视野都很小,与等晕区相当或更小,并且所得到的图像必须离线拼接以创建更大的蒙太奇图像。这些蒙太奇图像目前要么由专业人工分级人员手动组装,要么自动组装,通常每个蒙太奇图像需要几个小时。这可以说是限制了AO检眼镜在小队列研究中的适用性,而且在图像采集期间无法查看所有位置的实时捕获蒙太奇图像以进一步指导靶向成像。在这项工作中,我们提出用我们的新算法拼接图像,该算法使用定向快速旋转简要(ORB)描述符、局部敏感哈希,并通过寻找“足够好”的变换,而不是尽可能好的变换,以实现每个包含250张图像的蒙太奇图像的处理时间为1 - 2分钟。此外,考虑到图像相似性指标:归一化互信息(NMI)和归一化互相关(NCC)时,所提出的方法产生的蒙太奇图像与以前的方法一样准确。