Deep Retinal Image Segmentation with Regularization Under Geometric Priors.

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

This paper proposes a convolutional neural network architecture for blood vessel segmentation in retinal images. The network structure is designed on 7 layers using MatConvNet (three convolutional layers, two pooling layers, one dropout layer and a Softmax layer). The input data, selected from the DRIVE database, of the neural network is preprocessed in Matlab on Green channel. The retinal image was partitioned in patches of dimension 27 × 27 pixels using a sliding box algorithm. The whole network was trained using a number of 400,000 patches. The CNN was implemented using GPU Programming in MATLAB. The results are promising taking into account speed of processing and the simplicity of the network.

Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.

Vessel segmentation is an important problem in medical image analysis and is often challenging due to large variations in vessel appearance and profiles, as well as image noises. To address these challenges, we propose a solution by combining heterogeneous context-aware features with a discriminative learning framework. Our solution is characterized by three key ingredients: First, we design a hybrid feature pool containing recently invented descriptors including the stroke width transform (SWT) and Weber's local descriptors (WLD), as well as classical local features including intensity values, Gabor responses and vesselness measurements. Second, we encode context information by sampling the hybrid features from an orientation invariant local context. Third, we treat pixel-level vessel segmentation as a discriminative classification problem, and use a random forest to fuse the rich information encoded in the hybrid context-aware features. For evaluation, the proposed method is applied to retinal vessel segmentation using three publicly available benchmark datasets. On the DRIVE and STARE datasets, our approach achieves average classification accuracies of 0.9474 and 0.9633, respectively. On the high-resolution dataset HRFID, our approach achieves average classification accuracies of 0.9647, 0.9561 and 0.9634 on three different categories, respectively. Experiments are also conducted to validate the superiority of hybrid feature fusion over each individual component.

Accurate vessel segmentation in retinal images is vital for retinopathy diagnosis and analysis. However, existence of very thin vessels in low image contrast along with pathological conditions (e.g., capillary dilation or microaneurysms) render the segmentation task difficult. In this work, we present a novel approach for retinal vessel segmentation focusing on improving thin vessel segmentation. We develop a deep convolutional neural network (CNN), which exploits the specific characteristics of the input retinal data to use deep supervision, for improved segmentation accuracy. In particular, we use the average input retinal vessel width and match it with the layer-wise effective receptive fields (LERF) of the CNN to determine the location of the auxiliary supervision. This helps the network to pay more attention to thin vessels, that otherwise the network would ‘ignore’ during training. We verify our method on three public retinal vessel segmentation datasets (DRIVE, CHASE_DB1, and STARE), achieving better sensitivity (10.18% average increase) than state-of-the-art methods while maintaining comparable specificity, accuracy, and AUC.

The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.

PurposeTo propose a novel deep learning–based methodology for drusen detection and quantification in early age-related macular degeneration (AMD) using retinal multispectral images. The retinal multispectral images highlight features in several nonoverlapping spectral bands that the deep learning models leverage for automatic drusen detection and quantification in dry AMD.MethodsThe proposed novel methodology comprises quality assessment of retinal images, region of interest extraction, drusen segmentation, and drusen quantification stages. Different deep learning models (such as UNet++ convolutional neural network with EfficientNetV2 encoder) have been implemented for these stages. A total of 170 drusen and 150 nondrusen retinal images (single eye) were split into four training and validation data sets to analyze the performance of a deep learning model for drusen segmentation.ResultsThe proposed methodology achieved an average score, recall, and precision of 0.691, 0.668, and 0.776, respectively, across all four validation sets. This work also analyzed the performance of the proposed deep learning model for discriminating drusen and drusen-like lesions, achieving a pixel-wise segmentation accuracy of 99.998%. The number and the diameter of the detected drusen were also computed. A Dice score distribution for drusen segmentation with different numbers and sizes of drusen per eye is also shown.ConclusionsThis work demonstrates that deep learning models applied to retinal multispectral images can provide accurate and clinically significant drusen segmentation and quantification, thereby facilitating early detection, longitudinal monitoring, and reduction of the risk of vision loss from AMD.Translational RelevanceDeep learning–assisted detection of drusen from multispectral retinal images will refine and improve clinical diagnosis of early nonexudative age-related macular degeneration.

Background: Retinal fundus images, obtained from routine eye screenings, contain important information, not only concerning microvascular lesions of the retina, but potentially also of the progression and manifestation of various other diabetes complications. Recent advances in the field of machine learning, so-called deep learning neural networks (DNN), make it possible to automatically detect retinal lesions from fundus photos. Our overall aim is to develop a DNN to detect retinal vascular lesions. In this abstract, we describe a pre-processing step consisting of application of a DNN. Methods: For each eye, 5 retinal images of parts of the eye are taken, and collected into a joint mosaic image, used for image grading. There is no automated labelling of the images to identify left or right eyes. To distinguish left- from right eye images, we used a set of 1,916 unique, high-definition retina mosaic fundus images from diabetes patients followed at Steno Diabetes Center Copenhagen (SDCC) between 2003-2017. We trained and validated a DNN, using 964 left-eye (training set: 713, validation: 251) and 952 right-eye images (training set: 706, validation: 246). A DNN is a method that finds the optimal algorithm for relating a large set of input (mosaic fundus images) to output (left or right eye). The DNN used was based on the InceptionV3 architecture and run for 100 epochs with 100 gradient descent steps pr. epoch. Results: The DNN classification obtained a validation AUC-ROC of 0.92 and a validation accuracy of 0.86. Conclusions: The DNN successfully distinguished left-eye from right-eye images. We plan to use the DNN to identify right- and left-eye images in the SDCC image database comprising ∼ 80,000 left-eye and right-eye images. We will subsequently develop a DNN for automated detection of referable diabetic retinopathy of retinal fundus images, for use in the clinic. Disclosure L. Díaz: None. D. Vistisen: Stock/Shareholder; Self; Novo Nordisk A/S. M.E. Jørgensen: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. M. Valerius: None. J.N. Hajari: None. H. Lund-Andersen: None. S. Byberg: None.

In this paper, we present an automatic method based on context-dependent characteristics for the detection and classification of arterial vessels and venous vessels in retinal fundus images. It provides a non-invasive opportunity and effective foundation for the diagnosis of several medical pathologies. In the proposed method, a combination of shifted filter responses is used, which can selectively respond to vessels. It achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussian filters, whose supports are aligned in a collinear manner. We then configure two combinations of shifted filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel detection by summing up the responses of the two filters. Then we extract the morphology and topological characteristics based on the vessel segmentation, and specifically present context-dependent features of blood vessels, including the shape, structure, relative position, context information and other important features. Based on these features, we use JointBoost classifier to construct potential function for conditional random fields (CRFs) model, and train the labeled samples to classify arteriovenous blood vessels in retinal images. The training and testing data sets were prepared according to the results based on DRIVE dataset provided by Estrada et al. The experimental results show that the accuracy of the proposed method for vein and artery detection is 91.1% and 94.5%, respectively, which is superior to that of the state-of-the-art methods. It can be used as a clinical reference for computer-assisted quantitative analysis of fundus images.

Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, this makes accurate segmentation of blood vessels from retinal images still challenging. In this paper, we propose an effective framework for retinal vascular segmentation, which is innovative mainly in the retinal image pre-processing stage and segmentation stage. First, we perform image enhancement on three publicly available fundus datasets based on the multiscale retinex with color restoration (MSRCR) method, which effectively suppresses noise and highlights the vessel structure creating a good basis for the segmentation phase. The processed fundus images are then fed into an effective Reverse Fusion Attention Residual Network (RFARN) for training to achieve more accurate retinal vessel segmentation. In the RFARN, we use Reverse Channel Attention Module (RCAM) and Reverse Spatial Attention Module (RSAM) to highlight the shallow details of the channel and spatial dimensions. And RCAM and RSAM are used to achieve effective fusion of deep local features with shallow global features to ensure the continuity and integrity of the segmented vessels. In the experimental results for the DRIVE, STARE and CHASE datasets, the evaluation metrics were 0.9712, 0.9822 and 0.9780 for accuracy (Acc), 0.8788, 0.8874 and 0.8352 for sensitivity (Se), 0.9803, 0.9891 and 0.9890 for specificity (Sp), area under the ROC curve(AUC) was 0.9910, 0.9952 and 0.9904, and the F1-Score was 0.8453, 0.8707 and 0.8185. In comparison with existing retinal image segmentation methods, e.g. UNet, R2UNet, DUNet, HAnet, Sine-Net, FANet, etc., our method in three fundus datasets achieved better vessel segmentation performance and results.

Hypertensive Retinopathy (HR) cause serious damage to human body if not diagnosed early. Therefore, the early diagnosis of this anomaly is very important and complicated process. The most prominent aspect of HR is that the thickness of the blood vessels on the retina change. This change can be calculated with Arteriovenous ratio (AVR). Just before AVR calculation, the veins must be found and extracted on the retina image using some image processing techniques. In this study, the detection of vessels which have a very important place in the diagnosis of HR was performed on GPUs. The retina images taken from the DRIVE database were used as test data and the vessels were successfully detected in a much shorter time and without compromising sensitivity.

Identification of retinal diseases is a test for the ophthalmologists as the anomalies are just unmistakable at the beginning period. Early detection of these diseases can avoid lasting vision misfortune. Dealing with a lot of retinal images and location of variations from the norm because of these infections is difficult just as tedious. In this work, the deep learning algorithm has proposed to check the abnormality condition of retina with the help of retinal fundus images. In deep leaning a training set is produced with features of variations from the norm present in the retinal images and the infection the retina is experiencing. The deep Convolutional Neural Network (CNN) classifier predicts the infection for every retinal images in the wake of social event the learning from training the set. The rightness of desire is resolved to evaluate the viability of the classifier. The proposed technique was executed in MATLAB and assessed both normal and abnormal diabetic retinopathy retinal images of IDRID, ROC, and local datasets. The proposed technique has gotten better execution measurements, for example, sensitivity of 98.2%, Specificity of 98.45%, accuracy of 98.56% and average area under receiver operating characteristics of 0.9 when contrasted with different conditions of the workmanship strategies.

Diabetes mellitus, widely called diabetes, is a metabolic disorder that results in high glucose (blood sugar) level because of the inadequate production of the hormone called insulin. Diabetes, which affects the eyes and damages the retinal vessels, is known as diabetic retinopathy (DR). DR may even lead to blindness without prior symptoms. The existing handcrafted-based methods do not provide potential results in detecting retinal features and morphological differences in the retinal fundus images. Predominantly, in health care, deep learning makes a tremendous part in providing promising and more accurate results since it works well with unlabeled data and can easily learn low-level features from the training data. It acts as an assistant for the ophthalmologists in robustly examining retinal abnormalities. This chapter aims to discuss the state-of-the-art systems for detecting diabetic eye diseases with both traditional and deep learning techniques, and a statistical comparison is also made using various performance metrics. On comparing the accuracies and other metrics, the deep learning technique, convolutional neural network (CNN) solely surpasses the other conventional methods such as morphological methods, geometrical methods, pixel-based methods, and so on. Further, in this chapter, publicly available pertinent retinal image databases are also discussed.KeywordsConvolutional neural network (CNN)Deep learningDiabetes mellitusDiabetic retinopathy

To develop a robust and effective design, the proposed research uses a model that analyses blood vessels in retinal images by leveraging Deep Learning and Image Processing techniques. Deep learning, one of the AI techniques, has experienced rapid growth and is widely used in the medical field to analyse medical images, driving advancements in technology and medicine. The U-Net semantic segmentation neural network is the primary deep learning model used in our study. Training data consists of a set of retinal images, and the model’s performance is assessed on the training set. Once testing is complete, the results help evaluate the accuracy and effectiveness of the model and the techniques utilised in this research. This study indicates that deep learning segmentation techniques can offer valuable insights beyond standard pixel-based detection techniques. The proposed method gives a better understanding of retinal blood vessel analysis. Researchers used the DRIVE dataset for this research. It has 20,512x512 training and testing images for the model’s training and testing. The neural network's blood vessel shape recognition helps diagnose eye diseases. Diseases include hypertension, diabetic retinopathy, etc. The neural network's effectiveness makes the system suited for real-time blood vessel detection. This paper accurately mapped major and minor blood arteries. Thus, 96.53% of this research succeeded. The mean intersection over union is 8184. This exceeds the previous E-NET model of 7744.

The retinal vessel is one of the determining factors in an ophthalmic examination. Automatic extraction of retinal vessels from low-quality retinal images still remains a challenging problem. In this paper, we propose a robust and effective approach that qualitatively improves the detection of low-contrast and narrow vessels. Rather than using the pixel grid, we use a superpixel as the elementary unit of our vessel segmentation scheme. We regularize this scheme by combining the geometrical structure, texture, color, and space information in the superpixel graph. And the segmentation results are then refined by employing the efficient minimum spanning superpixel tree to detect and capture both global and local structure of the retinal images. Such an effective and structure-aware tree detector significantly improves the detection around the pathologic area. Experimental results have shown that the proposed technique achieves advantageous connectivity-area-length (CAL) scores of 80.92% and 69.06% on two public datasets, namely, DRIVE and STARE, thereby outperforming state-of-the-art segmentation methods. In addition, the tests on the challenging retinal image database have further demonstrated the effectiveness of our method. Our approach achieves satisfactory segmentation performance in comparison with state-of-the-art methods. Our technique provides an automated method for effectively extracting the vessel from fundus images.