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Background: Diagnosis of ophthalmologic and cardiovascular systems most often rely on the prerequisite step of segmentation of retinal blood vessels. Analysis of vascular structures in the retinal fundus images can aid in the early screening or detection of many ophthalmological diseases like glaucoma, diabetic retinopathy, vein occlusions, hemorrhages etc. In most cases, optic nerve gets damaged causing a blind spot. In this paper, a method of blood vessel segmentation using improved SOM (iSOM) and ANN classifier is presented. Methods: Morphological operations are carried out to enhance the input image. Clustering of pixels is done using improved Kohonen Self- Organizing Map (SOM) based on texture feature wherein a new node is introduced and new learning methodology is adopted using constrained weight updation. Finally, modified Otsu method is designed to label the output neuron class as vessel and non -vessel. Results: Segmentation is tested on public image sets, High Resolution Fundus (HRF) images and DRIONS-DB databases for Accuracy, Recall rate, Precision, F-Score, AUC and JC. The results achieve an appreciable level of accuracy (~97%) as compared to other similar methods of classification. The average time taken is less in estimating the neuron class and is about 12.1 sec per image when evaluated on Intel Core i5 CPU running at 2.30 GHz coupled with 4 GB RAM. The mean squared error for the segmented images is found to be in the range of 4-5%. Conclusion: Segmentation of retinal blood vessels based on artificial neural networks employing iSOM preserves the topology consuming less time for constrained weight updation achieving better results than SOM. A new model to detect vessels can be developed by concatenating iSOMs in parallel for multi class functions.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.

Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet.

Medical imaging is the practice of visualizing internal human body parts in order to make diagnoses. The discipline of medicine now relies heavily on medical imaging as a tool. Digital image processing and pattern recognition techniques in medical imaging aid in the detection of eye disorders. Changes in retinal Blood Vessels (BVs) are used in retinal image analysis to help diagnose retinal vascular illnesses such Diabetic Retinopathy (DR), Macular Edema, and Glaucoma. The retinal blood vessels are given a brief introduction in this review paper, along with a detailed description of the methods/techniques utilised to segment the retinal blood vessels. This paper also discusses the difficulties in segmenting BVs.

To obtain the ophthalmic findings for diagnosis and treatment, the ophthalmologist has been allowed to observe with a handheld ophthalmoscope in which the medical photos of findings cannot be recorded. Here, the simple optical system has been designed and implemented a prototype using 3D printing that can adapt to the smartphone with conventional app camera for direct imaging retinal blood vessels. A plano-convex lens of +20D was used to adjust the lens power of the smartphone camera. The lens wheel containing 16 lenses ranging from-8D to +8D and blank of 0D was used to compensate for tolerances and errors in human-eye sight. As a result, the prototype was constructed and able to capture images of the retinal blood vessels (RBV) in subject’s eyes having been either nearsighted or farsighted vision. Using this optical technique can provide the simple, portable and inexpensive prototype adapted to smartphone and can take advantage of global networks resource for telemedicine.

The diagnosis and screening of retinal illnesses is one use of image processing in the medical field that is being explored in the current study. DR is an eye condition that worsens over time, eventually resulting in blindness. A more advanced stage of DR, known as PDR, is when new retinal vessels (neovascularization) begin to form. PDR is characterised by fragile new arteries developing on the retina that leak blood onto the retina's surface, resulting in significant vision loss. Automatic segmentation of retinal arteries and PDR diagnosis are presented in this article. Extraction of the blood vessel is done using matching filtering and adjusted local entropy thresholding. A new machine learning technique known as the Extreme Learning Machine (ELM) classifier is used to classify the images as either normal, NPDR, or PDR. SVM classifier performance is compared to that of the algorithm.

Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods.

Vision loss is one of the main complications of eye disease, especially diabetic retinopathy (DR), because DR is a silent disease affecting the retina of the eye and resulting in loss of vision. Manual observation of eye disease takes time and delays effective treatment, so computerized methods are used to diagnose eye disease by extracting their features such as blood vessels, optic disc, and other abnormalities. Many computerized methods are proposed but it is still lacking to obtain small vessels. To overcome this problem, we have proposed methods based on image processing techniques for detection of retinal vessels. The proposed method is based on the elimination of uneven illumination using morphological tactics and principal component analysis. These initial steps are known as the preprocessing module, and our post-processing module contains the vessel coherence and the double threshold binarization method to obtain an image of the segmented vessels. Our proposed method obtained comparable results (average results (sensitivity: 0.78, specificity: 0.95 and precision: 0.951)) compared to the existing methods on the DRIVE and STARE databases.

In this paper, a deep convolutional neural network (CNN) is proposed for accurate segmentation of retinal blood vessels. This method plays a significant role in observing many eye diseases. A strided-CNN model is proposed for accurate segmentation of retinal vessels, especially the tiny vessels. The model is a fully convolutional model consisting of an encoder part and a decoder part where the pooling layers are replaced with strided convolutional layers. The strided convolutional layer approach was chosen over the pooling layers approach as the former can be trained. The morphological mappings along with the Principal Component Analysis (PCA)- based pre-processing steps are used to generate contrast images for training dataset. Skip connections are implemented to concatenate features from the encoder part and the decoder part to enhance the vessels segmentation especially the tiny vessels and to make the vessel’s edges sharper. We used a class balancing loss function to train and optimize the proposed model to improve vessel image quality. The impact of the proposed segmentation method is evaluated on four databases namely DRIVE, STARE, CHASE-DB1 and HRF. Overall model performance, particularly with respect to tiny vessels, is primarily influenced by sensitivity and accuracy metrics. We demonstrate that our model outperforms other models with a sensitivity of 0.87, 0.808, 0.886 and 0.829 on DRIVE, STARE, CHASE_DB1 and HRF respectively, along with respective accuracies of 0.956, 0.954, 0.976 and 0.962.

The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre-processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k-nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.

BACKGROUND AND OBJECTIVE: Proof of concept for the first system of noninvasive human retinal vessel and tissue oxygenation measurement in axial and radial directions. MATERIALS AND METHODS: A confocal imaging system capable of calculating and mapping relative retinal blood oxygenation in radial and axial directions from three eyes of two healthy subjects was built. The relationship between oxygenation and retinal depth in vivo was analyzed to illustrate application of this novel system. RESULTS: The system shows capacity for measuring oxygenation along retinal depth for the first time. (1) Arteriovenous oxygenation difference decreases with blood vessel diameter. (2) Artery-tissue oxygenation difference is greater than vein-tissue oxygenation difference in the same region. (3) Intravascular-extravascular oxygenation difference decreases with blood vessel diameter. (4) Oxygenation data reported with a 95% CI are as follows: A1 91.5% ± 18.2%, V1 32.8% ± 18.6%, A2 97.3% ± 17.8%, V2 64.4% ± 11.2%, A3 73.2% ± 19.1%, V3 52.9% ± 15.3%, and Tissue 56.6% ± 00.4%. CONCLUSION: This article demonstrates proof of concept for retinal oxygenation calculation in radial and axial dimensions for the first time. Initial results provide biological validity to this method. Future aims include further characterization of this system's results in healthy subjects and subsequent comparison of oxygenation between diseased and healthy retinae. [ Ophthalmic Surg Lasers Imaging Retina . 2022;53:275–283. ]

Retinal blood vessels are one of the most significant features in the fundus image of the eye, which plays a crucial role in the early screening of different ocular diseases like glaucoma, diabetic retinopathy, cataract and hypertensive retinopathy. Also in the area of biometric systems, blood vessel structure plays a vital role as retina scan is one of the finest and reliable methods. This paper proposed a segmentation technique which accurately extracts retinal blood vessels. The proposed algorithm conducted in three phases (i) pre -processing of image using AHE, CLAHE and average filtering, (ii) Multi-threshold based novel segmentation technique is used and (iii) post processing is done to remove the imperfections and for this morphological operation is employed. This method efficiently segments the vessels and improves the performance parameters. The accuracy of 95.3% is achieved. Implementation part was done in MATLAB 2015a using a DRIVE database openly available online.

In clinical field, wide assortments of utilizations can be managed utilizing image handling. Recognition and screening of retinal sicknesses was one such application in picture preparing. Diabetic retinopathy is an inconvenience of diabetes. The ailment influences veins inside the retina. The retina is a region lying at the rear of the eyeball. In the most punctual phase of the infection, the little veins, or vessels, gotten slenderer, more fragile and inevitably they spill blood. A patient's sight at this stage is still acceptable yet an ophthalmologist can identify and see the irregularities in the retina. As the sickness advances, some veins are obstructed. These trigger the retina to develop fresh blood vessels, which are unusual, delicate, and effectively drain. In the later phase of the ailment, fresh blood vessels are developed ceaselessly just as scar tissue. Eventually, retina will be isolates from an eye. Another strategy for removing the retinal veins from the shading fundus retinal picture dependent on include grouping was proposed in this undertaking, to decrease the ophthalmologists' time and vitality for checking the retinal pictures. The veins are separated from the shading fundus picture by applying the preprocessing strategies and division procedures utilizing coordinated channel and adjusted nearby entropy thresholding activity. The preprocessed picture was then utilized for highlight extraction and these highlights were utilized for order reason. At long last, arrangement procedure was utilized for diagnosing the proliferative diabetic retinopathy.

The retina offers a unique opportunity to directly visualize blood vessels in vivo noninvasively. Over the past few decades, several new imaging techniques have been adapted to study the retinal vasculature in the laboratory in animal models and in the clinic in human subjects. High-contrast, finely detailed fundus images can be acquired by confocal scanning laser ophthalmoscopy (cSLO). With fluorescein angiography (FA), the retinal microcirculation can be visualized. High-resolution spectral-domain optical coherence tomography (SD-OCT) is able to acquire cross-section images resolving the microarchitecture of the retina, similar to histology. The techniques and protocols for acquiring cSLO, FA, and SD-OCT imaging of the retinal vasculature and morphology in the rodent are described.

Several methods are been used for the segmentation of blood vessels in the retina. The research paper detailed a novel method for conducting the segmentation of retinal images. In this, retinal blood vessel segmentation is driven out in computer-based evaluation to extract images of retinal vessels. The morphology opening operation and FCM are combined to segment the vessels. CLAHE is used to enhance the blood vessels and then a smoothing operation is performed on the retinal image. Then this retinal image is segmented using FCM where clusters are formed. The images from DRIVE databases have been used for conducting segmentation. The results have been obtained on different parameters relevant to the segmentation of retinal images (Sensitivity=0.7637, Speciflcity= 0.9887, Accuracy=0.9880).

A figure with a zoomed version of the USAF test chart. / Graphs of the rest of the tested metrics. / Figures of other segmentation algorithms tested.

A figure with a zoomed version of the USAF test chart. / Graphs of the rest of the tested metrics. / Figures of other segmentation algorithms tested.;Retinal blood vessel segmentation code

The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular ailment, high blood pressure, arteriosclerosis, and age-related macular degeneration. The manual inspection of retinal vasculature is an extremely challenging and tedious task for medical experts due to the complex structure of an eye, tiny blood vessels, and variation in vessels width. Several automatic retinal vessels extraction techniques have been proposed in contemporary literature, which assist ophthalmologists in the timely identification of an eye disorders. However, due to the fast evolution of such techniques, a comprehensive survey is needed. This survey presents a comprehensive review of such techniques, strategies, and algorithms presented to date. The techniques are classified into logical groups based on the underlying methodology employed for retinal vessel extraction. The performance of existing techniques is reported on the publicly accessible datasets in term of various performance measures, providing a valuable comparison among the techniques. Thus, this survey presents a valuable resource for researchers working toward automatic extraction of retinal vessels.

Automatically classifying retinal blood vessels appearing in fundus camera imaging into arterioles and venules can be problematic due to variations between people as well as in image quality, contrast and brightness. Using the most dominant features for retinal vessel types in each image rather than predefining the set of characteristic features prior to classification may achieve better performance. In this paper, we present a novel approach to classifying retinal vessels extracted from fundus camera images which combines an Orthogonal Locality Preserving Projections for feature extraction and a Gaussian Mixture Model with Expectation-Maximization unsupervised classifier. The classification rate with 47 features (the largest dimension tested) using OLPP on our own ORCADES dataset and the publicly available DRIVE dataset was $90.56\%$ and $86.7\%$ respectively.

In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with PCA and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The average accuracy rate obtained for the gray level image with PCA after the study was 0,9443, while the average accuracy rate obtained for the green channel was 0,9685. The study was performed using 40 images in the DRIVE data set which is one of the most common retina data sets known.

Diabetes mellitus (DM) is a worldwide major medical problem. Diabetic retinopathy (DR) staging is important for the estimation of DM and the evaluation of associated retinopathy. According to the international clinical diabetic retinopathy & diabetic macular edema disease severity scales, most of the dilated ophthalmoscopy observable findings are associated with retinal blood vessels. In order to objectively and accurately determine the diabetic retinopathy stages, it is essential to automatically detect and extract retinal blood vessels in fundoscopic images. This paper introduces and compares various convolutional neural networks to recognize retinal blood vessels in fundoscopic images. The experimental results demonstrate the effectiveness of the proposed approach.

This paper presents an algorithm that will segment the retinal blood vessels with an accuracy of 96.17%. This algorithm will extract the features from input images present in STARE and CHASE_DB1 databases. The extracted features will be large in number, but all the features are not useful. So, the feature optimization is done by Lion Optimization which has effectively chosen only the features which are useful in representing the extracted features as blood vessels or non-blood vessels. The algorithm was applied first on training images which have results of manually segmented images already. Then the algorithm was implemented on training images and evaluated on training images and it successfully detects the normal as well as abnormal images. The quantitative results were checked using parameters sensitivity, specificity, accuracy, positive predictive rate and false predictive rate and proved to give better results in comparison to existing techniques.

This paper deals with the analysis and modeling of the retinal blood vessels system. The aim of the analysis is the design and implementation of a fully automated segmentation model based on the morphological segmentation, allowing for extraction of the blood system area within the binary model, where other retinal structures are suppressed. An important feature of the model is sensitivity and robustness to declare the efficacy of segmentation in an environment with worse image parameters. For this reason, the designed model is also tested for data where the vascular system is visualized under a low contrast. Part of the analysis is the comparative testing of the designed model against selected segmentation methods based on objective criteria. The designed model was tested and verified on dataset from system RetCam 3 containing 22 images.

Segmentation of retinal blood vessels allows a quantitative analysis of vessels, hence it helps to diagnose several cardiovascular and ophthalmologic diseases. Manual segmentation is time-consuming, therefore, an automatic method is needed. Dilated convolutions have been recently adopted for semantic segmentation task and higher performances have been achieved. In this paper, we investigate the use of dilated convolutions for retinal vessel segmentation. The proposed architectures are evaluated in the DRIVE dataset. The context information provided by dilated convolutions demonstrated to be valuable for the presented task, leading to more accurate segmentations. Our best model achieves accuracy, specificity and sensitivity of 0.9567, 0.9813 and 0.7903, respectively., (undefined)

Segmentation of retinal blood vessels is an important diagnostic procedure in ophthalmology. In this paper we propose an automated blood vessels segmentation method that combines both supervised and un-supervised approaches. A novel descriptor named Local Haar Pattern (LHP) is proposed to describe retinal pixel of interest. The performance of the proposed method has been evaluated on three publicly available DRIVE, STARE and CHASE_DB1 datasets. The proposed method achieves an overall segmentation accuracy of 96%, 96% and 95% respectively on DRIVE, STARE, and CHASE DB1 datasets, which are better than the state-of-the-art methods.

Retinal vascular structure is an imperative marker of numerous retinal and systemic maladies, which has persuaded the origination of different image segmentation strategies for the veins. The core target of this article is to distinctively introduce the open research issues in the retinal blood vessels segmentation and to discuss the similarities and dissimilarities between different approaches utilized to isolate blood vessels in retina images. The contemporary implications, the future strategies, and the open complications in automatic retinal vessels detection are also encapsulated.

Automatic segmentation of retinal blood vessels from fundus images is of great importance in assessing the condition of vascular network in human eyes. The task is primary challenging due to the low contrast of images, the variety of vessels and potential pathology. Previous studies have proposed shallow machine learning based methods to tackle the problem. However, these methods require specific domain knowledge, and the efficiency and robustness of these methods are not satisfactory for medical diagnosis. In recent years, deep learning models have made great progress in various segmentation tasks. In particular, Fully Convolutional Network and U-net have achieved promising results in end-to-end dense prediction tasks. In this study, we propose a novel encoder-decoder architecture based on the vanilla U-net architecture for retinal blood vessels segmentation. The proposed deep learning architecture integrates hybrid dilation convolutions and pixel transposed convolutions in the encoder-decoder model. Such design enables global dense feature extraction and resolves the common “gridding” and “checkerboard” issues in the regular U-net. Furthermore, the proposed network can be efficiently and directly implemented for any semantic segmentation applications. We evaluate the proposed network on two retinal blood vessels data sets. The experimental results show that our proposed model outperforms the baseline U-net model.

Purpose: To analyze the inter-methods agreement in arteriovenous ratio (AVR) evaluation between spectral-domain optical coherence tomography (SD-OCT) and Dynamic Vessel Analyzer (DVA). Methods: Healthy volunteers underwent DVA and SD-OCT examination. AVR was measured by SD-OCT using the four external lines of the optic nerve head-centered 7-line cube and by DVA using an automated AVR estimation. The mean AVR was calculated, twice, separately by two independent readers for each tool. Results: Twenty-two eyes of 11 healthy subjects (five women and six men, mean age 35) were included. AVR analysis by DVA showed high inter-observer agreement between reader 1 and 2, and high intra-observer agreement for both reader 1 and reader 2. With regard to AVR analysis on SD-OCT, we found high inter-observer agreement between reader 1 and 2, and low intra-observer agreement for reader 2 but high intra-observer agreement for reader 1. Overall, the mean AVR measured on SD-OCT turned out to be significantly higher than mean AVR measured through DVA (reader 1, 0.9023 ± 0.06 vs 0.8036 ± 0.08; p < 0.001, and reader 2, 0.9067 ± 0.06 vs 0.8083 ± 0.05; p= 0.003). Conclusions: No inter-method agreement in AVR could be detected in the present study due to bias in measurements (shift between DVA and SD-OCT). We found significant difference in the two noninvasive methods for AVR measurement, with a tendency for SD-OCT to overestimate retinal vascular caliber in comparison to DVA. This may be useful for achieving greater accuracy in the evaluation of retinal vessel in ocular as well as systemic diseases.

Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.