Minimising retinal vessel artefacts in optical coherence tomography images

Optical coherence tomography (OCT) is commonly used to investigate the layers of the retina including retinal nerve fiber layer (RNFL) and retinal pigment epithelium (RPE). OCT images are altered by vessels on the retinal surface producing artefacts. We propose a new approach to compensate for these artefacts and enhance quality of OCT images. A total of 28 (20 normal and 8 glaucoma subjects) OCT images were obtained using Spectralis (Heidelberg, Germany). Shadows were detected along the image and compensated by the A-Scan intensity difference from surrounding non-affected areas. Images were then segmented and the area and thickness of RNFL and RPE were measured and compared. 10 subjects were tested twice to determine the effect of this on reproducibility of measurements. Shadow-suppressed images reflected the profile of the retinal layers more closely when assessed qualitatively, minimising distortion. The segmentation of RNFL and RPE thickness demonstrated a mean change of 2.4% ± 1 and 6% ± 1 from the original images. Much larger changes were observed in areas with vessels. Reproducibility of RNFL thickness was improved, specifically in the higher density vessel location, i.e. inferior and superior. Therefore, OCT images can be enhanced by an image processing procedure. Vessel artefacts may cause errors in assessment of RNFL thickness and are a source of variability, which has clinical implications for diseases such as glaucoma where subtle changes in RNFL need to be monitored accurately over time.     

结合PCANet与线性判别分析的视网膜光学相干断层扫描图像分类

目的 主成分分析网络（PCANet）能提取图像的纹理特征,线性判别分析（LDA）提取的特征有类别区分性。本文结合这两种方法的优点,提出一种带线性判别分析的主成分分析网络（PCANet-LDA）,用于视网膜光学相干断层扫描（OCT）图像中的老年性黄斑变性（AMD）、糖尿病性黄斑水肿（DME）及正常（NOR）这3类的全自动分类。方法 PCANet-LDA算法是在PCANet的基础上添加了LDA监督层,该层加入了类标签对特征进行监督投影。首先,对OCT视网膜图像进行去噪、二值化及对齐裁剪等一系列预处理,获得感兴趣的视网膜区域;然后,将预处理图像送入一个两层的PCA卷积层,训练PCA滤波器组并提取图像的PCA特征;接着,将PCA特征送入一个非线性输出层,通过二值散列和块直方图等处理,得到图像的特征;之后,将带有类标签的图像特征送入一个LDA监督层,学习LDA矩阵并用其对图像特征进行投影,使特征具有类别区分性;最后,将投影的特征送入线性支持向量机（SVM）中对分类器进行训练和分类。结果 实验分别在医院临床数据集和杜克数据集上进行,先对OCT图像预处理进行前后对比实验,然后对PCANet特征提取的有效性进行分析,最后对PCANet算法、ScSPM算法以及提出的PCANet-LDA3种分类算法的分类效果进行对比实验。在临床数据集上,PCANet-LDA算法的总体分类正确率为97.20%,高出PCANet算法3.77%,且略优于ScSPM算法;在杜克数据集上,PCANet-LDA算法的总体分类正确率为99.52%,高出PCANet算法1.64%,略优于ScSPM算法。结论 PCANet-LDA算法的分类正确率明显高于PCANet,且优于目前用于2D视网膜OCT图像分类的先进的ScSPM算法。因此,提出的PCANet-LDA算法在视网膜OCT图像的分类上是有效且先进的,可作为视网膜OCT图像分类的基准算法。     

River channel segmentation in polarimetric SAR images: Watershed transform combined with average contrast maximisation

This publication presents a computer method allowing river channels to be segmented based on SAR polarimetric images. Solutions have been proposed which are based on a morphological approach using the watershed segmentation and combining regions by maximising the average contrast. The image processing methods were developed so that their computational complexity is as low as possible, which is of particular importance in analysing high resolution SAR/polarimetric SAR images, where it has a measurable impact on the total segmentation time. What is more, compared to the existing solutions known from the literature review: (1) in the proposed approach, there is no need to execute further steps necessary to eliminate objects (i.e. background components) located outside the river channel from the image as a result of the segmentation carried out, (2) there is no need to sample the entire image and carry out a pixel–wise classification to prepare the segmentation process. If the steps listed in items (1) – (2) are performed, they can, unfortunately, extend the segmentation time. The experiments completed on images acquired from the ALOS PALSAR satellite for different regions of the world have shown a high quality of the segmentations carried out and a high computational efficiency compared to state–of–the art methods. Consequently, the proposed method can be used as a useful tool for monitoring changes in river courses and adopted in expert and intelligent systems used for analysing remote sensing data.     

一种快速的视网膜光学相干图像分割方法

在医学领域,黄斑厚度可以用来量化糖尿病黄斑水肿和年龄相关性黄斑变性等疾病,临床上通常使用光学相干断层扫描的影像技术来获取黄斑图像。但现有的黄斑图像分割方法运算速度较慢,阻碍了其临床使用。本文提出一种新的基于多分辨率及水平集的黄斑图像分割方法,首先使用高斯滤波对原始图像按行进行滤波,再运用多分辨率方法获取图像初始局部轮廓,最后使用水平集方法可以快速获取黄斑图像的中间轮廓,得到最终的图像分割结果。通过在311幅黄斑图像的仿真实验对比,本文方法在边缘检测结果和运算速度上比传统方法有很大改进。     

Segmentation of three-dimensional retinal image data

We have combined methods from volume visualization and data analysis to support better diagnosis and treatment of human retinal diseases. Many diseases can be identified by abnormalities in the thicknesses of various retinal layers captured using optical coherence tomography (OCT). We used a support vector machine (SVM) to perform semi-automatic segmentation of retinal layers for subsequent analysis including a comparison of layer thicknesses to known healthy parameters. We have extended and generalized an older SVM approach to support better performance in a clinical setting through performance enhancements and graceful handling of inherent noise in OCT data by considering statistical characteristics at multiple levels of resolution. The addition of the multi-resolution hierarchy extends the SVM to have "global awareness." A feature, such as a retinal layer, can therefore be modeled.     

Toward objective evaluation of image segmentation algorithms

Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms - the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley segmentation data set     

Ant Colony Optimization-based method for optic cup segmentation in retinal images

An accurate detection of the cup region in retinal images is necessary to obtain relevant measurements for glaucoma detection. In this work, we present an Ant Colony Optimization-based method for optic cup segmentation in retinal fundus images. The artificial agents will construct their solutions influenced by a heuristic that combines the intensity gradient of the optic disc area and the curvature of the vessels. On their own, the exploration capabilities of the agents are limited; however, by sharing the experience of the entire colony, they are capable of obtaining accurate cup segmentations, even in images with a weak or non-obvious pallor. This method has been tested with the RIM-ONE dataset, yielding an average overlapping error of 24.3% of the cup segmentation and an area under the curve (AUC) of 0.7957 using the cup to disc ratio for glaucoma assessment.     

Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review

The retina is a tiny layer at the posterior pole of an eye and is made up of tissues sensitive to light, these tissues generate nerve signals that pass through the optic nerve to the brain. A retinal disorder occurs when the retina malfunctions; glaucoma, diabetic retinopathy and pathologic myopia are retinal disorders and principal causes of blindness worldwide. These retinal disorders are often diagnosed and treated by an ophthalmologist. However, to accurately assess a retinal disease, ophthalmologist would need qualitative and quantitative analysis of the disease, it’s early and current statistics, but acquisition of these measurements are not possible through manual techniques, there should be automated computer aided diagnosis (CAD) systems to assist ophthalmologists. In this comprehensive review, an analysis and evaluation has been performed of different computer vision and image processing approaches applied to OCT images for automatic diagnosis of retinal disorders. We also reported disease causes, symptoms and pathologies manifestations within OCT images, which can serve as baseline knowledge for development of an automated CAD system. Hence, this disease specific review offers a good understanding to analyze visual impairments from retinal OCT images which will help researcher to design enhanced therapeutic systems for retinal disorders.     

Robust Region Detection via Consensus Segmentation of Deformable Shapes

We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non‐rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis.     

Automating image segmentation verification and validation by learning test oracles

An image segmentation algorithm delineates (an) object(s) of interest in an image. Its output is referred to as a segmentation. Developing these algorithms is a manual, iterative process involving repetitive verification and validation tasks. This process is time-consuming and depends on the availability of experts, who may be a scarce resource (e.g., medical experts). We propose a framework referred to as Image Segmentation Automated Oracle (ISAO) that uses machine learning to construct an oracle, which can then be used to automatically verify the correctness of image segmentations, thus saving substantial resources and making the image segmentation verification and validation task significantly more efficient. The framework also gives informative feedback to the developer as the segmentation algorithm evolves and provides a systematic means of testing different parametric configurations of the algorithm. During the initial learning phase, segmentations from the first few (optimally two) versions of the segmentation algorithm are manually verified by experts. The similarity of successive segmentations of the same images is also measured in various ways. This information is then fed to a machine learning algorithm to construct a classifier that distinguishes between consistent and inconsistent segmentation pairs (as determined by an expert) based on the values of the similarity measures associated with each segmentation pair. Once the accuracy of the classifier is deemed satisfactory to support a consistency determination, the classifier is then used to determine whether the segmentations that are produced by subsequent versions of the algorithm under test, are (in)consistent with already verified segmentations from previous versions. This information is then used to automatically draw conclusions about the correctness of the segmentations. We have successfully applied this approach to 3D segmentations of the cardiac left ventricle obtained from CT scans and have obtained promising results (accuracies of 95%). Even though more experiments are needed to quantify the effectiveness of the approach in real-world applications, ISAO shows promise in increasing the quality and testing efficiency of image segmentation algorithms.