玻璃体腔注射曲安奈德治疗视网膜分支静脉阻塞继发黄斑囊样水肿疗效观察

观察玻璃体腔注射曲安奈德(intravitreal triamcinolone acetonide,IVTA)治疗视网膜分支静脉阻塞(brach retinal vein occlusion,BRVO)继发黄斑囊样水肿(cystoid macular edema,CME)的临床效果.对33例(34眼)BRVO继发CME...     

Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut

An automated method is reported for segmenting 3-D fluid-associated abnormalities in the retina, so-called symptomatic exudate-associated derangements (SEAD), from 3-D OCT retinal images of subjects suffering from exudative age-related macular degeneration. In the first stage of a two-stage approach, retinal layers are segmented, candidate SEAD regions identified, and the retinal OCT image is flattened using a candidate-SEAD aware approach. In the second stage, a probability constrained combined graph search-graph cut method refines the candidate SEADs by integrating the candidate volumes into the graph cut cost function as probability constraints. The proposed method was evaluated on 15 spectral domain OCT images from 15 subjects undergoing intravitreal anti-VEGF injection treatment. Leave-one-out evaluation resulted in a true positive volume fraction (TPVF), false positive volume fraction (FPVF) and relative volume difference ratio (RVDR) of 86.5%, 1.7%, and 12.8%, respectively. The new graph cut-graph search method significantly outperformed both the traditional graph cut and traditional graph search approaches (p < 0.01, p < 0.04) and has the potential to improve clinical management of patients with choroidal neovascularization due to exudative age-related macular degeneration.     

Retinal thickness measurements from optical coherence tomography using a Markov boundary model

We present a system for detecting retinal boundaries in optical coherence tomography (OCT) B-scans. OCT is a relatively new imaging modality giving cross-sectional images that are qualitatively similar to ultrasound. However, the axial resolution with OCT is much higher, on the order of 10 microm. Objective, quantitative measures of retinal thickness may be made from OCT images. Knowledge of retinal thickness is important in the evaluation and treatment of many ocular diseases. The boundary-detection system presented here uses a one-dimensional edge-detection kernel to yield edge primitives. These edge primitives are rated, selected, and organized to form a coherent boundary structure by use of a Markov model of retinal boundaries as detected by OCT. Qualitatively, the boundaries detected by the automated system generally agreed extremely well with the true retinal structure for the vast majority of OCT images. Only one of the 1450 evaluation images caused the algorithm to fail. A quantitative evaluation of the retinal boundaries was performed as well, using the clinical application of automatic retinal thickness determination. Retinal thickness measurements derived from the algorithm's results were compared with thickness measurements from manually corrected boundaries for 1450 test images. The algorithm's thickness measurements over a 1-mm region near the fovea differed from the corrected thickness measurements by less than 10 microm for 74% of the images and by less than 25 microm (10% of normal retinal thickness) for 98.4% of the images. These errors are near the machine's resolution limit and still well below clinical significance. Current, standard clinical practice involves a qualitative, visual assessment of retinal thickness. A robust, quantitatively accurate system such as ours can be expected to improve patient care.     

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。     

Automated Solder Inspection Technique for BGA-Mounted Substrates by Means of Oblique Computed Tomography

High-density large-scale integration (LSI) packages such as ball grid arrays (BGAs) are being utilized in car electronics and communication infrastructure products. These products require a high speed and reliable inspection technique for their solder joints. Oblique computed tomography (OCT) was proposed as a novel imaging technique for BGA-mounted substrates, and it is being introduced in many manufacturing factories. Although operators examine OCT images manually, the establishment of an automated inspection technique is required from the viewpoint of an operator's load and the fluctuation of the inspection results. In this paper, a novel automated solder inspection technique by means of OCT is proposed. This technique consists of position adjustment, bump extraction, character extraction and judgement. Moreover, by combining five characteristic features, the condition of a solder bump was determined in computer algorithm. In this paper, linear discriminate analysis and an artificial neural network technique are introduced as the determination methods. In the experiments, these techniques are evaluated by using actual BGA-mounted substrates. The correct rate of inspection reached 99.7% in both the determination methods, which clearly indicates that proposed method may be useful in practice.     

Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach

Optical coherence tomography (OCT) is a noninvasive, depth-resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present a semi-automated segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for the OCT images, which suffer from low contrast and are highly corrupted by noise. A multiphase framework with a circular shape prior is adopted in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on OCT images of rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy even in the presence of intensity inhomogeneity resulting from blood vessels. Our algorithm achieved an average Dice similarity coefficient of 0.84 over all segmented retinal layers, and of 0.94 for the combined nerve fiber layer, ganglion cell layer, and inner plexiform layer which are the critical layers for glaucomatous degeneration.     

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.     

光学相干层析图像的散斑去躁和边缘特征提取

提出采用各向异性扩散（AD）算法对光学相干层析（OCT）图像作噪声消除和边缘增强的预处理,然后结合改进的相位一致（PC）算法进行边缘特征提取,并引入巴特沃斯高通提升滤波进一步衰减低频噪声,增强高频边缘细节。对不同的OCT图像进行处理实验,结果表明,上述三种算法的综合应用,使OCT图像噪声得到有效抑制,轮廓清晰,特征部位得到加强。     

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。     

Tracking the optic nervehead in OCT video using dual eigenspaces and an adaptive vascular distribution model

Optical coherence tomography (OCT) is a new ophthalmic imaging modality generating cross sectional views of the retina. OCT systems are essentially Michelson interferometers that form images in 1.5 s by directing a superluminescent diode (SLD) beam over the retinal surface. Involuntary eye motions frequently cause incorrect locations to be imaged. This motion may leave no obvious artifacts in the scan data and can easily go undetected. For glaucoma monitoring especially, knowing the measurement path, typically a circle concentric with the nerve head, is crucial. The commercially available OCT system displays a near-infrared video of the retina showing the SLD beam. This paper presents a prototype system to detect the nerve head and SLD beam in the video, and report the true scan path relative to the nerve head. Low image contrast and limited resolution make the reliable detection of retinal features difficult. In an adaptive model construction phase, the system directly detects retinal vasculature and the nerve head and incrementally builds a model of the current subject's vascular pattern relative to the optic disk. The nerve head identification is multitiered, using a novel dual eigenspace technique and a geometric comparison of detected vessel positions and nerve head hypotheses. In its operational phase, a correspondence is achieved between the currently detected vasculature and the model. Using subjects not included in training, the system located the optic nerve head to within 5 pixels (0.07 optic disk diameters, an error well below clinical significance) in 99.75% of 2800 video fields. In current clinical practice, motions as large as 1-2 disc diameters may go undetected, so this is a vast improvement.     

利用纹理相关性对OCT影像进行血管分割

随着医学的发展,OCT影像技术越来越广泛的应用于多个医学领域,对OCT影像的处理就显得更加重要。基于图像纹理相关性的影像分割技术已经开始应用于活体微循环OCT影像的处理,如视网膜组织,皮肤血管组织等。对活体OCT影像的处理及其血管的分割有助于医生更好的诊断血管疾病。本文我们提出了一种基于灰度期望和精典Niblack结合的二值化阈值选取方法,并结合图像相关性对活体小白鼠耳朵血管的OCT影像进行图像分割,提取血管的轮廓,再经过形态学处理,得到较为准确的血管轮廓。      

Two Proposed Blind Equalizers Using Different Constellation Matched Error Functions for QAM Signals

Although Constant Modulus Algorithm (CMA) is effective to equalize non-minimum phase channels blindly, it suffers from residual intersymbol interference (ISI) and large Mean Square Error (MSE) when applied to higher order constellations (QAM). Methods based on cost function matched to the signal constellation namely alphabet matched algorithm (AMA) were previously reported and proves its superiority on CMA concerning the MSE. Thus dual mode algorithms between CMA and AMA were introduced. A hybrid technique combining CMA and AMA using a cosine square function as a constellation matched error (CME) was lately reported. In this paper two different CME functions are introduced. The MSE of the proposed algorithms are calculated using Matlab simulation under multipath slow fading channels for different signal to noise ratios (SNR) and different levels of QAM constellations. A comparison is established among them. Depicted results show the effectiveness of the two proposed CME functions. Hebat-Allah M. Mourad received her B.Sc., M.Sc. and Ph.D. degrees in electrical communication engineering from Cairo University, Egypt, in 1983, 1987 and 1994 respectively. Since 1983, she has been with the Department of Electronics and Communications, Faculty of Engineering, Cairo University, and is currently associate professor there. Her research interests include optical fiber communications, mobile and satellite communications.     

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.     

Alignment of 3-D optical coherence tomography scans to correct eye movement using a particle filtering

Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.     

Automatic optic disc detection from retinal images by a line operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.     

Predictive haemodynamics in a one-dimensional human carotid artery bifurcation. Part II: Application to graft design

A Bayesian surrogate modeling technique is proposed that may be able to predict an optimal bypass graft configuration for patients suffering with stenosis in the internal carotid artery (ICA). At the outset, this statistical technique is considered as a means for identifying key geometric parameters influencing haemodynamics in the human carotid bifurcation. This methodology uses a design of experiments (DoE) technique to generate candidate geometries for flow analysis. A pulsatile one dimensional Navier-Stokes solver incorporating fluid-wall interactions for a Newtonian fluid which predicts pressure and flow in the carotid bifurcation (comprising a stenosed segment in the internal carotid artery) is used for the numerical simulations. Two metrics, pressure variation factor (PVF) and maximum pressure (p(*)(m)) are employed to directly compare the global and local effects, respectively, of variations in the geometry. The values of PVF and p(*)(m) are then used to construct two Bayesian surrogate models. These models are statistically analyzed to visualize how each geometric parameter influences PVF and p(*)(m). Percentage of stenosis is found to influence these pressure based metrics more than any other geometric parameter. Later, we identify bypass grafts with optimal geometric and material properties which have low values of PVF on five test cases with 70%, 75%, 80%, 85%, and 90% stenosis in the ICA, respectively.5%, 80%, 85%, and 90% stenosis in the ICA, respectively.     

Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search

Current techniques for segmenting macular optical coherence tomography (OCT) images have been 2-D in nature. Furthermore, commercially available OCT systems have only focused on segmenting a single layer of the retina, even though each intraretinal layer may be affected differently by disease. We report an automated approach for segmenting (anisotropic) 3-D macular OCT scans into five layers. Each macular OCT dataset consisted of six linear radial scans centered at the fovea. The six surfaces defining the five layers were identified on each 3-D composite image by transforming the segmentation task into that of finding a minimum-cost closed set in a geometric graph constructed from edge/regional information and a priori determined surface smoothness and interaction constraints. The method was applied to the macular OCT scans of 12 patients (24 3-D composite image datasets) with unilateral anterior ischemic optic neuropathy (AION). Using the average of three experts' tracings as a reference standard resulted in an overall mean unsigned border positioning error of 6.1 $pm$ 2.9 $mu$m, a result comparable to the interobserver variability (6.9 $pm$ 3.3 $mu$m). Our quantitative analysis of the automated segmentation results from AION subject data revealed that the inner retinal layer thickness for the affected eye was 24.1 $mu$m (21%) smaller on average than for the unaffected eye $(p≪0.001)$, supporting the need for segmenting the layers separately.      

全光纤光学相干层析系统用于人造指纹的识别

目前商用指纹识别系统由于仅依赖于手指表面二 维图像的脊模式和细节特征进行识别,对人造 指纹有极大的安全漏洞。本文提出一种利用光学相干层析(OCT)技术识别 人造指纹的方法。在分析OCT系统原理及皮肤光学特性的基础上,使用研制的全光纤OCT系 统分别对 人体手指以及被商用指纹识别系统识别通过的人造指模进行成像,获得了二者的二维OCT图 像和对应的一 维信号,同时获得人造指模的三维OCT图像,通过对比分析二者深度方向 上的微结构信息 以及光学特性可以准确地识别出人造指模。实验结果表明,OCT技术不仅可以用于指纹的防 伪,有效地提高生物特征识别系统的安全性能,而且还具有用于指纹识别的潜力。     

Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN

Wireless Personal Communications - In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. OCT...