Aligning scan acquisition circles in optical coherence tomography images of the retinal nerve fibre layer

Optical coherence tomography (OCT) is widely used in the assessment of retinal nerve fibre layer thickness (RNFLT) in glaucoma. Images are typically acquired with a circular scan around the optic nerve head. Accurate registration of OCT scans is essential for measurement reproducibility and longitudinal examination. This study developed and evaluated a special image registration algorithm to align the location of the OCT scan circles to the vessel features in the retina using probabilistic modelling that was optimised by an expectation-maximization algorithm. Evaluation of the method on 18 patients undergoing large number of scans indicated improved data acquisition and better reproducibility of measured RNFLT when scanning circles were closely matched. The proposed method enables clinicians to consider the RNFLT measurement and its scan circle location on the retina in tandem, reducing RNFLT measurement variability and assisting detection of real change of RNFLT in the longitudinal assessment of glaucoma.     

Detection of anatomic structures in human retinal imagery

The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.     

Optic nerve head segmentation

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 microns/pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.     

Automatic recovery of the optic nervehead geometry in optical coherence tomography

Optical coherence tomography (OCT) uses retroreflected light to provide micrometer-resolution, cross-sectional scans of biological tissues. OCT's first application was in ophthalmic imaging where it has proven particularly useful in diagnosing, monitoring, and studying glaucoma. Diagnosing glaucoma is difficult and it often goes undetected until significant damage to the subject's visual field has occurred. As glaucoma progresses, neural tissue dies, the nerve fiber layer thins, and the cup-to-disk ratio increases. Unfortunately, most current measurement techniques are subjective and inherently unreliable, making it difficult to monitor small changes in the nervehead geometry. To our knowledge, this paper presents the first published results on optic nervehead segmentation and geometric characterization from OCT data. We develop complete, autonomous algorithms based on a parabolic model of cup geometry and an extension of the Markov model introduced by Koozekanani, et al. to segment the retinal-nervehead surface, identify the choroid-nervehead boundary, and identify the extent of the optic cup. We present thorough experimental results from both normal and pathological eyes, and compare our results against those of an experienced, expert ophthalmologist, reporting a correlation coefficient for cup diameter above 0.8 and above 0.9 for the disk diameter.     

基于交叉网络的眼底视神经乳头自动定位

该文研究基于交叉网络的眼底视神经乳头自动定位的新方法。为描述眼底血管网络空间属性,该文提出一种新的概念交叉网络,并给出了交叉网络属性测度。依据眼底组织结构模型构建交叉网络,利用血管网络交叉密度测度,实现眼底图像视神经乳头的自动定位。采用国际通用的STARE,DRIVE眼底图像库以及临床采集图像进行不同图像质量下定位成功率测试,实验结果验证了算法的有效性。同时运算速度较已有算法也有明显提高,可以满足眼科临床检查的需求。     

基于视网膜扫描的头戴显示器研究现状

随着头戴显示器的轻小型化发展,基于视网膜扫描的头戴显示器逐渐成为近年来虚拟现实领域和头戴显示器领域的一个研究热点。此类显示器通过扫描装置控制激光束进行二维扫描,扫描图像经成像后可直接在观察者的视网膜上进行显示,具有大视场、高亮度、结构紧凑等独特优势,也被称为视网膜扫描显示器。鉴于国内该方向的研究较为薄弱,结合国外视网膜扫描显示器的研究基础,阐述了视网膜扫描显示器的工作原理,论述了该领域的技术发展及关键技术研究现状,总结了视网膜扫描显示器的技术发展趋势和应用前景,为国内相关领域的研究和发展指出了方向。     

Registration of stereo and temporal images of the retina

The registration of retinal images is required to facilitate the study of the optic nerve head and the retina. The method we propose combines the use of mutual information as the similarity measure and simulated annealing as the search technique. It is robust toward large transformations between the images and significant changes in light intensity. By using a pyramid sampling approach combined with simulated reannealing we find that registration can be achieved to predetermined precision, subject to choice of interpolation and the constraint of time. The algorithm was tested on 49 pairs of stereo images and 48 pairs of temporal images with success.     

采用频域光延迟线的光学相干层析成像

频域光延迟线是一种光学扫描结构,其基本组成是一个衍射光栅、一个透镜和一个扫描镜.把频域光延迟线应用到光学相干层析成像系统中,可以使参考臂扫描速率至少达到数m/s以上,从而实现视频速率的图像获取.从几何光学的角度分析了频域光延迟线的工作原理,分析结果表明在扫描角很小的情况下,入射光的光程或相位变化频率与扫描角频率有较好的线性关系.基于这一结构的光学相干层析成像的结果证明可以避免活体生物组织位移引入的图像模糊.另外,还提出了一种基于微执行器的新扫描装置.     

Real-time algorithm for retinal tracking

Conventional retinal laser photocoagulation is presently performed by an ophthalmologist manually aiming a low-power laser beam at a desired site and firing a high-power laser for a preselected interval of time. To automate this process a retinal tracker must acquire a target, track small saccades, and identify loss of track during a large saccade. The authors successfully implemented a real-time algorithm that used a simple computer, video digitizing card, low light video camera, and fundus camera to perform rudimentary tracking on a photograph of a retina undergoing smooth circular motion. The algorithm tracked speeds up to 5 Hz, or 27°/s, which equated to the retina moving in a 525 μm diameter circle     

自适应光学相干层析在视网膜高分辨成像中的应用

视网膜光学相干层析（OCT）技术利用外部低相干光源照射人眼眼底,并将人眼眼底散射信号进行干涉成像,获得人眼视网膜的断层图像信息,以实现人眼视网膜无创、实时、在体的光学活检。传统光学相干层析在视网膜成像时的轴向分辨率可达3 μm以上,但由于人眼个体差异和不可避免的像差限制了视网膜OCT的横向分辨率,只能达到约15~20 μm。而自适应光学作为一项波前校正的先进技术,可以校正OCT色差以及人眼有限视场和眼球运动导致的像差,将OCT横向分辨率提高到低于2 μm,以实现视网膜细胞及微细血管近衍射极限成像,及时发现患者眼底存在的早期病变。在介绍自适应光学和视网膜光学相干层析的技术特点基础上,对自适应光学在视网膜光学相干层析成像应用的国内外发展现状进行了论述,总结了自适应光学OCT视网膜高分辨成像在宽带光源色差校正、眼球运动伪影减少、自适应光学视场扩大和波前传感与校正系统简化的关键技术和未来发展趋势,以实现大视场、高效率、高灵敏度、高分辨率的高速人眼视网膜成像,为未来自适应光学OCT视网膜成像技术的研究和应用提供参考和借鉴。     

眼底图像中黄斑中心与视盘自动检测新方法

在眼底图像自动分析中,视盘与黄斑的定位是糖尿病性视网膜病变计算机辅助诊断或筛查的先决条件。该文提出一种应用方向局部对比度滤波结合局部血管密度的方法,直接先行检测黄斑中心再行定位视盘,不同于现有的先行检测视盘或血管再行定位黄斑的一般方法,有效地提高黄斑定位正确率,能更好地应用于糖尿病性黄斑水肿的自动评估。实验选取了网络公开的HEI-MED数据集中169幅黄斑水肿眼底图像,黄斑和视盘的定位正确率同步达到98.2%,算法简单且无监督,优于现有的方法,具有良好的临床应用前景。     

SLD光源相干特性研究

利用自相关的方法,从理论上分析计算了超辐射发光二极管(SLD)等宽谱光源的相干特性.并利用光纤陀螺的光路,从实验上研究了SLD的相干特性.实验结果与理论计算的一致,从而验证了这种理论计算的正确性.     

Quantitative Television Fluoroangiography?The Optical Measurement of Dye Concentrations and Estimation of Retinal Blood Flow

The development of a system for the measurement of dye concentrations from single retinal vessels during retinal fluorescein angiography is presented and discussed. The system uses a fundus camera modified for TV viewing. Video gating techniques define the areas of the retina to be studied, and video peak detection yields dye concentrations from retinal vessels. The time course of dye concentration is presented and blood flow into the retina is estimated by a time of transit technique.     

Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets

The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.     

视网膜OCT B-scan图像分割算法综述

依靠光学相干层析(OCT)技术能够获得清晰的视网膜结构图,对于视网膜病变的诊断具有重要的意义。文章回顾了既往研究中典型的视网膜OCT B-scan图像分割算法,根据算法的输出特征,将其分为视网膜边界提取法和视网膜层提取法两类,旨在明确不同的方法获取信息的差异性,为视网膜病变诊断提供参考。     

Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology.     

Image processing algorithms for retinal montage synthesis, mapping,and real-time location determination

Although laser retinal surgery is the best available treatment for choroidal neovascularization, the current procedure has a low success rate (50%). Challenges, such as motion-compensated beam steering, ensuring complete coverage and minimizing incidental photodamage, can be overcome with improved instrumentation. This paper presents core image processing algorithms for (1) rapid identification of branching and crossover points of the retinal vasculature; (2) automatic montaging of video retinal angiograms; (3) real-time location determination and tracking using a combination of feature-tagged point-matching and dynamic-pixel templates. These algorithms tradeoff conflicting needs for accuracy, robustness to image variations (due to movements and the difficulty of providing steady illumination) and noise, and operational speed in the context of available hardware. The algorithm for locating vasculature landmarks performed robustly at a speed of 16-30 video image frames/s depending upon the field on a Silicon Graphics workstation. The montaging algorithm performed at a speed of 1.6-4 s for merging 5-12 frames. The tracking algorithm was validated by manually locating six landmark points on an image sequence with 180 frames, demonstrating a mean-squared error of 1.35 pixels. It successfully detected and rejected instances when the image dimmed, faded, lost contrast, or lost focus     

Optimum hierarchical segmentation of OCT images in retina based on macular distance

Retina layering was the basis of optic disc structure analysis and 3D feature extraction of glaucoma.In order to improve the layering effect of retinal OCT images,an intensity based multilayer segmentation algorithm for two-dimensional retinal OCT images was proposed.Through preprocessing and filtering operation,the segmentation algorithm calculated the intensity and intensity gradient of each A-scan in the retinal OCT image to obtain the upper bound RNFL,the dividing line IS and OS,and the lower bound RPE.Then macular distance strategy,calculated by the shortest distance,was used to further optimize the layering result of macular area,so as to achieve layering segmentation of the retinal OCT images.The experimental results show the algorithm has good optimization effect,low time complexity and fast running speed.