Retinal optic disc localization using convergence tracking of blood vessels

Optic disc localization is of great diagnostic value related to retinal diseases, such as glaucoma and diabetic retinopathy. However, the detection process is quite challenging because positions of optic discs vary from image to image, and moreover, pathological changes, like hard exudates or neovascularization, may alter optic disc appearance. In this paper, we propose a robust approach to accurately detect the optic disc region and locate the optic disc center in color retinal images. The proposed technique employs a kernelized least-squares classifier to decide the area that contains optic disc. Then connected-component labeling and lumination information are used together to find the convergence of blood vessels, which is thought to be optic disc center. The proposed method has been evaluated over two datasets: the Digital Retinal Images for Vessel Extraction (DRIVE), and the Non-fluorescein Images for Vessel Extraction (NIVE) datasets. Experimental results have shown that our method outperforms existing methods, achieving a competitive accuracy (97.52 %) and efficiency (1.1577s).     

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Segmentation of the left ventricle (LV) is a hot topic in cardiac magnetic resonance (MR) images analysis. In this paper, we present an automatic LV myocardial boundary segmentation method using the parametric active contour model (or snake model). By convolving the gradient map of an image, a fast external force named gradient vector convolution (GVC) is presented for the snake model. A circle-based energy is incorporated into the GVC snake model to extract the endocardium. With this prior constraint, the snake contour can conquer the unexpected local minimum stemming from artifacts and papillary muscle, etc. After the endocardium is detected, the original edge map around and within the endocardium is directly set to zero. This modified edge map is used to generate a new GVC force filed, which automatically pushes the snake contour directly to the epicardium by employing the endocardium result as initialization. Meanwhile, a novel shape-similarity based energy is proposed to prevent the snake contour from being strapped in faulty edges and to preserve weak boundaries. Both qualitative and quantitative evaluations on our dataset and the publicly available database (e.g. MICCAI 2009) demonstrate the good performance of our algorithm.     

基于YOLO算法的眼底图像视盘定位方法

视盘的各个参数是衡量眼底健康状况和病灶的重要指标,视盘的检测和定位对于观察视盘的形态尤为重要。在以往的视盘定位研究中,主要根据视盘的形状、亮度、眼底血管的走向等特征使用图像处理的方法对眼底图像中视盘进行定位。由于人为因素影响较大,特征提取时间较长,且视盘定位效率低,因此提出一种基于YOLO算法的眼底图像视盘定位方法。利用YOLO算法将眼底图像划分为N×N的格子,每个格子负责检测视盘中心点是否落入该格子中,通过多尺度的方式和残差层融合低级特征对视盘进行定位,得到不同大小的边界框,最后通过非极大抑制的方式筛选出得分最高的边界框。通过在3个公开的眼底图像数据集(DRIVE、DRISHTI-GS1和MESSIDOR)上,对所提出的视盘定位方法进行测试,定位准确率均为100%,实验同时定位出视盘的中心点坐标,与标准中心点的平均欧氏距离分别为22.36 px、2.52 px、21.42 px,验证了该方法的准确性和通用性。     

Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods

The automatic determination of the optic disc area in retinal fundus images can be useful for calculation of the cup-to-disc (CD) ratio in the glaucoma screening. We compared three different methods that employed active contour model (ACM), fuzzy c-mean (FCM) clustering, and artificial neural network (ANN) for the segmentation of the optic disc regions. The results of these methods were evaluated using new databases that included the images captured by different camera systems. The average measures of overlap between the disc regions determined by an ophthalmologist and by using the ACM (0.88 and 0.87 for two test datasets) and ANN (0.88 and 0.89) methods were slightly higher than that by using FCM (0.86 and 0.86) method. These results on the unknown datasets were comparable with those of the resubstitution test; this indicates the generalizability of these methods. The differences in the vertical diameters, which are often used for CD ratio calculation, determined by the proposed methods and based on the ophthalmologist's outlines were even smaller than those in the case of the measure of overlap. The proposed methods can be useful for automatic determination of CD ratios.     

视盘与黄斑同时定位检测的FPGA方法研究

在眼底图像自动分析中,视盘与黄斑的定位是实现利用计算机辅助诊断或筛查糖尿病视网膜病变的先决条件。提出一种实现眼底图像中视盘与黄斑同时定位检测的新方法,使用YOLOv4-tiny算法定位检测,将该算法移植到现场可编程逻辑门阵列（field programmable gate array,FPGA）。与传统方法相比,该方法不仅可以快速准确地同时定位眼底图像中视盘和黄斑的位置,而且也是利用高层综合（high level synthesis,HLS）语言和时分复用技术实现38层中型神经网络的首次尝试。实验采用公认的COCO数据集和Kaggle-Diabetic Retinopathy Detection竞赛中的381幅眼底图像对算法进行训练,将训练后的算法移植到FPGA平台后视盘和黄斑定位的平均正确率（mean average precision,mAP）为96.11%,检测一张图片只需要150.445?ms,在相关领域具有良好的临床应用前景。     

一种新的心脏核磁共振图像分割方法

心脏核磁共振图像分割一直是医学影像分析领域的研究热点和难点,文中提出了一种基于梯度矢量流Snake模型的左心室分割方法.作为对梯度矢量流(GVF)的改进,提出了退化最小曲面梯度矢量流(dmsGVF).该模型对弱边界泄漏有更好的鲁棒性;挖掘了左心室的形状特点,采用相应的形状约束,克服了由于图像灰度不均而导致的局部极小,也大大减弱了分割结果对初始轮廓的依赖;对于左室壁外膜的分割,挖掘了左室壁内、外膜的位置关系,通过重新组合梯度分量来构造新的外力场.这种外力场能够克服原始梯度矢量流的不足,使得室壁外膜边缘很弱时也能得到保持,以左室壁内膜分割结果作为初始化能够自动地分割出左室壁外膜.实验结果表明,该方法能高效准确地同时分割左室壁内、外膜.     

视网膜图像中的黄斑中心检测

目的 在眼底图像分析中,准确的黄斑中心定位对于糖尿病性视网膜病变的计算机辅助诊断系统具有重要的意义。然而,由于光照不均匀、计算量大及病变的干扰给黄斑中心定位带来了巨大的挑战。因此,为了实现更为准确且高效的黄斑中心检测,提出一种基于血管投影和数学形态学的黄斑中心检测方法。方法 首先,基于数学形态学,提出一种自动的血管检测方法。其次,利用视盘区域的血管分布实现视盘中心的自动定位。再次,根据视盘和黄斑的解剖学结构先验信息,提取感兴趣区域。最后,在感兴趣区域内,通过数学形态学和特征提取定位黄斑中心。结果 本文提出的方法在两个标准的糖尿病视网膜病变数据库DIARETDB0和DIARETDB1上分别取得了96.92%和96.63%的成功率,且总成功率达到96.35%。此外,平均的执行时间分别为8.236 s和8.912 s。结论 实验结果表明,本文方法能快速和准确地定位黄斑中心且其性能明显地优于现有的黄斑中心检测方法。     

Supervised Vessels Classification Based on Feature Selection

Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification (AVC) method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc (OD) localization. Then, a region of interest (ROI) is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix (GLCM) features, and an adaptive local binary patten (A-LBP) feature by using a max-relevance and min-redundancy (mRMR) scheme. Finally, a feature-weighted K-nearest neighbor (FW-KNN) algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.     

Optic disk feature extraction via modified deformable model technique for glaucoma analysis

A deformable-model based approach is presented in this paper for robust detection of optic disk and cup boundaries. Earlier work on disk boundary detection up to now could not effectively solve the problem of vessel occlusion. The method proposed here improves and extends the original snake, which is essentially a deforming-only technique, in two aspects: knowledge-based clustering and smoothing update. The contour deforms to the location with minimum energy, and then self-clusters into two groups, i.e., edge-point group and uncertain-point group, which are finally updated by the combination of both local and global information. The modifications enable the proposed algorithm to become more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results on the 100 testing images show that the proposed method achieves better success rate (94%) when compared to those obtained by GVF-snake (12%) and modified ASM (82%). The proposed method is extended to detect the cup boundary and then extract the disk parameters for clinical application, which is a relatively new task in fundus image processing. The resulted cup-to-disk (C/D) ratio shows good consistency and compatibility when compared with the results from Heidelberg Retina Tomograph (HRT) under clinical validation.     

基于L*a*b*色彩空间的视神经边缘自动提取

视神经(Optic nerve)形状、面积和深度等参数是衡量眼底健康状况的重要指标, 其边缘提取是量化这些参数的前提. 为精确识别视神经边缘, 本文提出了一种基于L*a*b*色彩空间眼底图像视神经边缘自动提取算法. 该方法通过L*a*b*色彩空间自适应形态学方法与区域辅助几何活动轮廓模型边缘提取方法, 结合基于交叉网络的视神经自动定位, 实现视神经边缘的自动提取. 采用国际上通用的DRIVE眼底图像库和临床图像进行实验, 验证了该算法的有效性.     

Combining algorithms for automatic detection of optic disc and macula in fundus images

This paper proposes an efficient combination of algorithms for the automated localization of the optic disc and macula in retinal fundus images. There is in fact no reason to assume that a single algorithm would be optimal. An ensemble of algorithms based on different principles can be more accurate than any of its individual members if the individual algorithms are doing better than random guessing. We aim to obtain an improved optic disc and macula detector by combining the prediction of multiple algorithms, benefiting from their strength and compensating their weaknesses. The location with maximum number of detectors’ outputs is formally the hotspot and is used to find the optic disc or macula center. An assessment of the performance of integrated system and detectors working separately is also presented. Our proposed combination of detectors achieved overall highest performance in detecting optic disc and fovea closest to the manually center chosen by the retinal specialist.     

Hybrid feature vector based detection of Glaucoma

This paper focus on the investigation of the potential in retinal image analysis for the detection of Glaucoma. The computer-based analysis of the parameter involves the use of image processing algorithms for pre-processing, localization and segmentation of the region of interest (ROI), feature extraction from ROI, and classification. The initial step in computer based detection system includes the enhancing scheme for improving the contrast of the fundus image from the three databases, Drishti-GS1, FAU and RIMONE. The optic disc region has been localized from the enhanced image. Structural deformation of the optic disc region, one of the primary indicators of the glaucoma demands more accuracy in segmentation process. As a solution to this problem, non-morphological features are extracted from the enhanced optic disc region. The non-morphological features from spatial domain include Local Binary Pattern, Histogram of Oriented Gradient and Fractal features. The significant feature extracted from the spatial domain are selected using Sequential Floating Forward Selection method and are then fed into the Support Vector Machine, Naive Bayes and Logistic Regression classifiers. Performance of the classifier is analyzed by computing the accuracy, sensitivity, specificity and positive prediction value. The performance of the classifier is also validated using the receiver operating characteristics plot. The hybrid feature from the spatial domain contributes to increase the efficiency of classification.

     

Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template

This study developed a computerised method for fovea centre detection in fundus images. In the method, the centre of the optic disc was localised first by the template matching method, the disc–fovea axis (a line connecting the optic disc centre and the fovea) was then determined by searching the vessel-free region, and finally the fovea centre was detected by matching the fovea template around the centre of the axis. Adaptive Gaussian templates were used to localise the centres of the optic disc and fovea for the images with different resolutions. The proposed method was evaluated using three publicly available databases (DIARETDB0, DIARETDB1 and MESSIDOR), which consisted of a total of 1419 fundus images with different resolutions. The proposed method obtained the fovea detection accuracies of 93.1%, 92.1% and 97.8% for the DIARETDB0, DIARETDB1 and MESSIDOR databases, respectively. The overall accuracy of the proposed method was 97.0% in this study.     

Multiply distorted image quality assessment based on feature level fusion and optimal feature selection

No reference image quality assessment (NR-IQA) has received considerable importance in the last decade due to a rise in the use of multimedia content in our daily lives. Due to limitations in technology, multiple distortions may be introduced in the images that need to be assessed. Recently feature selection has shown promising results for single distorted NR-IQA and their effectiveness on multiple distorted images still need to be addressed. In this paper, impact of feature level fusion and feature selection on multiple distorted image quality assessment is presented. To this end features are extracted from multiple distorted images using six NR-IQA techniques (BLIINDS-II, BRISQUE, CurveletQA, DIIVINE, GM-LOG, SSEQ) that extract features in different (discrete cosine transform, spatial, curvelet transform, wavelet transform, spatial and gradient, spatial and spectral) domains. The extracted features from different domains are fused to generate a single feature vector. All combinations of feature-level fusion from six different techniques have been evaluated. Three different feature selection algorithms (genetic search, linear forward search, particle swarm optimization) are then applied to select optimum features for NR-IQA. The selected features are then used by the support vector regression model to predict the quality score. The performance of the proposed methodology is evaluated for two multiple distorted IQA databases (LIVE multiple distorted image dataset (LIVEMD), multiply distorted image database (MDID2017)), two singly synthetically distorted IQA databases (Tampere image database (TID2013), Computational and subjective image quality database (CSIQ)), and one screen content IQA database (Screen content image quality database (SIQAD)). Experimental results show that the fusion of features from different domains gives better performance in comparison to existing multiple-distorted NR-IQA techniques with SROCC scores of 0.9555, 0.9587, 0.6892, 0.9452, and 0.7682 on the LIVEMD, MDID, TID2013, CSIQ, and SIQAD databases respectively. Moreover, the performance is further improved when the genetic search feature selection algorithm is applied to fused features to remove the redundant and irrelevant features. The SROCC scores are improved to 0.9691, 0.9723, and 0.6897 for LIVEMD, MDID, and TID2013 databases respectively.

     

基于显著性检测和改进局部高斯分布拟合模型的眼底图像视盘边界自动提取

正确的视盘(OD)定位和分割是糖尿病视网膜病变自动筛选系统中的两个主要步骤.鉴于此,提出一种基于显著性目标检测和改进局部高斯分布拟合(LGDF)模型的视神经盘分割方法.该方法主要包含两个阶段:第一阶段,将显著性检测技术应用到增强的视网膜图像中实现视盘的自动定位;第二阶段,通过增加椭圆约束信息来改进局部高斯分布拟合(LGDF)模型分割视盘边界.使用公开数据库Diaretdbq对所提出方法的性能进行测试,并与其他先进的方法进行对比,结果验证了所提出方法的优越性和有效性.     

Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning

Diseases of the eye require manual segmentation and examination of the optic disc by ophthalmologists. Though, image segmentation using deep learning techniques is achieving remarkable results, it leverages on large-scale labeled datasets. But, in the field of medical imaging, it is challenging to acquire large labeled datasets. Hence, this article proposes a novel deep learning model to automatically segment the optic disc in retinal fundus images by using the concepts of semi-supervised learning and transfer learning. Initially, a convolutional autoencoder (CAE) is trained to automatically learn features from a large number of unlabeled fundus images available from the Kaggle’s diabetic retinopathy (DR) dataset. The autoencoder (AE) learns the features from the unlabeled images by reconstructing the input images and becomes a pre-trained network (model). After this, the pre-trained autoencoder network is converted into a segmentation network. Later, using transfer learning, the segmentation network is trained with retinal fundus images along with their corresponding optic disc ground truth images from the DRISHTI GS1 and RIM-ONE datasets. The trained segmentation network is then tested on retinal fundus images from the test set of DRISHTI GS1 and RIM-ONE datasets. The experimental results show that the proposed method performs on par with the state-of-the-art methods achieving a 0.967 and 0.902 dice score coefficient on the test set of the DRISHTI GS1 and RIM-ONE datasets respectively. The proposed method also shows that transfer learning and semi-supervised learning overcomes the barrier imposed by the large labeled dataset. The proposed segmentation model can be used in automatic retinal image processing systems for diagnosing diseases of the eye.

     

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.     

多相主动轮廓模型的眼底图像杯盘分割

目的 视盘及视杯的检测对于分析眼底图像和视网膜视神经疾病计算机辅助诊断来说十分重要,利用医学眼底图像中视盘和视杯呈现椭圆形状这一特征,提出了椭圆约束下的多相主动轮廓模型,实现视盘视杯的同时精确分割。方法 该算法根据视盘视杯在灰度图像中具有不同的区域亮度,建立多相主动轮廓模型,然后将椭圆形约束内嵌于该模型中。通过对该模型的能量泛函进行求解,得到椭圆参数的演化方程。分割时首先设定两条椭圆形初始曲线,根据演化方程,驱动曲线分别向视盘和视杯方向进行移动。当轮廓线到达视盘、视杯边缘时,曲线停止演化。结果 在不同医学眼底图像中对算法进行验证,对算法抗噪性、不同初始曲线选取等进行了实验,并与多种算法进行了对比。实验结果表明,本文模型能够同时分割出视盘及视杯,与其他模型的分割结果相比,本文算法的分割结果更加准确。结论 本文算法可以精确分割医学眼底图像中的视盘和视杯,该算法不需要预处理,具有较强的鲁棒性和抗噪性。