Retinal Blood Vessel Extraction Research Articles

Screening Retinal Images and Extraction of the Retinal Blood Vessel for Identifying Diseases and Classification of Arteries and Veins by Using Deep Learning

Abstract: In recent years, the extraction of retinal blood vessels from low contrast retinal images has become a challenging task for diagnosing retinal diseases such as Diabetic Retinopathy, Agerelated Macular Degeneration (AMD), Retinopathy of Prematurity (ROP), cataract, and glaucoma. Another challenge is screening the retinal image to identify the disease early on. However, data analysis from a large population-based study of retinal diseases is required to help resolve the uncertainty in identifying the retinal disease based on retinal image classification using deep learning approaches from the retinal diseases dataset. Therefore, we proposed the survey on the deep learning approach for screening the retinal image to identify the early stages of the disease and discussed retinal disease analysis based on deep learning approaches to detect Diabetic Retinopathy, AMD ROP, and Glaucoma. We also discuss deep learning applications in the segmentation of retinal blood vessels, extraction of the optic disc, optic cup, and fovea, and OCT segmentation to detect retinal disease for diagnosis of diseases. Finally, discuss the classification of arteries/veins using a deep learning approach.

Extraction of Retinal Blood Vessels in Colour Retinal Images: An Analysis

Extraction of Retinal Blood Vessels in Colour Retinal Images: An Analysis

RETINAL VESSELS EXTRACTION BASED ON IMPROVING LINE DETECTION

In the medical field, medical images have supported doctors in detecting some dangerous problems related to health. These problems are caused by abnormalities in retinal blood vessels. Therefore, analysis of retinal vascular features is a good way to detect these abnormalities that help doctors make suitable treatment plans in the early stage. One of the retinal analysis tasks is retinal blood vessel extraction which plays an important role because it needs to be done before any measurement can be created. This paper proposed an approach for retinal blood vessel extraction based on multi-scale line detection. In the proposed method, the input image is applied by a multi-scale line detection technique to detect retinal blood vessels. As a result, our method can work effectively to extract more vessels. The performance of the proposed method evaluates both quantitatively and qualitatively on publicly available DRIVE datasets with an accuracy average reaching 96%. The result of the proposed method is better than the other methods.

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

The person’s retina has its uniqueness that can be used as biometric recognition. The use of the retina as a marking feature in biometrics is more accurate in making calls, verification, and authentication. Retinal biometric characteristics are unique and difficult to manipulate, thus making the retinal biometric system one of the most reliable biometrics compared to other biometric characteristics. The retinal biometric system can be formed using extracted retinal vessels. The difficulty in extracting retinal vessels is a characteristic of retinal vessels. itself includes (central artery, central branch artery, central vein and central branch vein), the ratio of the thickness ratio between the different retinal arteries and veins (2:3), the location of the retinal artery and vein and the color. This complexity often results in errors in the retinal blood vessel extraction process, where not all blood vessel objects can be extracted properly which can reduce the accuracy of the retinal biometric system. This study will address the problem of extracting retinal vessels by proposing the use of an extraction method to produce truly unique retinal features to be included in the retinal biometric system by tracing all branches of the retinal vessels (consisting of: bifurcation, trifurcation and crossover). ). The accuracy results show that 99.81% of the images were correctly detected. The blood pattern is obtained by doing extraction which includes the preprocessing stage and is continued by doing the blood extraction stage. This pattern extraction result is used as a unique pattern to be included in the feature vector of the biometric system in identifying person based on the retina.

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions.

Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.

An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf.

Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4s, which has been identified on boundary condition of Pdf function.

A Study on Automatic Extraction of Retinal Blood Vessels in Fundus Images

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.

Extraction of retinal blood vessels and diagnosis of proliferative diabetic retinopathy

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.

Comparison Between Median Filter and Wiener Filter to Get High Accuracy for Blood Vessel Image Extraction

With today's advancing technology, support of developing hardware and software systems, the developments in the field of medicine have increased considerably. In particular, medical image analysis and processing systems have taken a considerable lead. The development of an automatic system could provide great convenience for doctors and practitioners in the field. The image processing techniques proposed in this study can contribute to more effective analysis and more accurate diagnosis, regardless of the individual levels of experience of the users or particular situations and conditions such as fatigue or image quality. This paper presents a robust method for retinal blood vessel segmentation and some automatic algorithms for analyzing the vessel network and pixel classification into vessel and non-vessel .The aim of the work extraction or segmentation of retinal blood vessels used computer vision and image processing for getting high accuracy (comparison with manual) .Also We used the preprocessing techniques for enhancement of the image .And used two types of filter for comparing result to get best scenario. Also for simulation result we used the matlab and implement on DRIVE database.

Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

Segmentasi Citra Pembuluh Darah Retina Menggunakan Operasi Morfologi Iteratif

Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels. All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.

Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network.

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach

Retinal blood vessels are the only deep microvessels in the blood circulation system that can be observed directly and noninvasively, providing us with a means of observing vascular pathologies. Cardiovascular and cerebrovascular diseases, such as glaucoma and diabetes, can cause structural changes in the retinal microvascular network. Therefore, the study of effective retinal vessel segmentation methods is of great significance for the early diagnosis of cardiovascular diseases and the vascular network’s quantitative results. This paper proposes an automatic retinal vessel segmentation method based on an improved U-Net network. Firstly, the image patches are rotated to amplify the image data, and then, the RGB fundus image is preprocessed by normalization. Secondly, after the improved U-Net model is constructed with 23 convolutional layers, 4 pooling layers, 4 upsampling layers, 2 dropout layers, and Squeeze and Excitation (SE) block, the extracted image patches are utilized for training the model. Finally, the fundus images are segmented through the trained model to achieve precise extraction of retinal blood vessels. According to experimental results, the accuracy of 0.9701, 0.9683, and 0.9698, sensitivity of 0.8011, 0.6329, and 0.7478, specificity of 0.9849, 0.9967, and 0.9895, F1-Score of 0.8099, 0.8049, and 0.8013, and area under the curve (AUC) of 0.8895, 0.8845, and 0.8686 were achieved on DRIVE, STARE, and HRF databases, respectively, which is better than most classical algorithms.

Retinal image blood vessel extraction and quantification with Euclidean distance transform approach

Image processing applications remarkably contributes to modern ophthalmology. This technology is designed to analyse the characteristics of the human eye microvasculature images. The retinal microvasculature is an excellent non-invasive screening window for the assessment of systemic diseases such as diabetes, hypertension, and stroke. Retinal microvasculature character such as widening vessel diameter is recognised as an analysable feature for stroke or transient ischemic attack for predicting the progression of this pathology. Thus, in this study, a computer-assisted method has been developed for this task applying the Euclidean distance transform (EDT) technique. This newly developed algorithm computes the Euclidean distance of the remaining white pixels on the area of interest. Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of Retinal Vessel Image set for Estimation of Width database were used for the performance evaluation of the proposed algorithm that showed 98.1 and 97.7% accurate result for both CLRIS and VDIS, respectively. The significantly high accuracy in this newly developed vessel diameter quantification algorithm indicates excellent potential for further development, evaluation, validation, and integration into ophthalmic diagnostic instruments.

Automated Approach for Extraction of Retinal Blood Vessels

In the field of ophthalmology, retinal image analysis is crucial to extract many details that help doctors identifying retinal diseases at early stages, such details are the blood vessels, optic disk, and fovea. The exponential increase in number of diabetic retinopathy patients necessitates the use of computers to help doctors to diagnose and treat retinal diseases of the human eyes. Computer vision effectively helps doctors by analyzing and treating human retinas. In the field of ophthalmology, a color image (RGB image) of the eye fundus is captured by ophthalmoscopy. In this work, an automatic technique for extraction of human retinal blood vessels is proposed. The proposed method is based on three main stages, namely image pre-processing, initial segmentation of blood vessels and image post-processing. In the first stage, the contrast-limited adaptive histogram equalization is applied to the green component image to improve its contrast, and then it is remerged again with the red and blue components. In the second stage, the blood vessels are extracted using mean-C thresholding. Finally, in the third stage, many morphological operations are used to refine the segmented blood vessels image. The proposed method is validated using the expert ground truths with the DRIVE dataset in terms of pixels, and experimental results show sensitivity, specificity, accuracy and positive predictive value of 0.770816, 0.977575, 0.959993 and 0.7611835, respectively. The performance measures are compared with many recent related works and found to outperform most of them. The superior performance of this method proves that it is promising for mass screening of human fundus images.

Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks

In medicine, diagnosis is as important as treatment. Retinal blood vessels are the most easily visible vessels in the whole body, and therefore, play a key role in the diagnosis of numerous diseases and eye disorders. Systematic and eye diseases cause morphologic variations, such as the growing, narrowing or branching of retinal blood vessels. Imaging-based screening of retinal blood vessels plays an important role in the identification and follow-up of eye diseases. Therefore, automatic retinal vessel segmentation can be used to diagnose and monitor those diseases. Computer-aided algorithms are required for the analysis of progression of eye diseases. This study proposes a hybrid method that provides a combination of pre-processing and data augmentation methods with a deep learning model. Pre-processing was used to solve the irregular clarification problems and to form a contrast between the background and retinal blood vessels. After pre-processing step, a convolutional neural network (CNN) was designed and then trained for the extraction of retinal blood vessels. In the training phase, data augmentation was performed to improve training performance. The CNN was trained and tested in the DRIVE database, which is commonly used in retinal blood vessel segmentation and publicly available for studies in this area. Results showed that the proposed system extracted vessels with a sensitivity of 77.78%, specificity of 97,84%, precision of 84.17% and accuracy of 95.27%. This study also compared the results to those of previous studies. The comparison showed that the proposed method is an efficient and successful method for extracting retinal blood vessels. Moreover, the pre-processing phases improved the system performance. We believe that the proposed method and results will make contribution to the literature.

Machine Learning Technique for Feature Extraction and Segmentation of Retinal Blood Vessels

The term Diabetic retinopathy is the serious issue that is caused by the diabetes, which affects the eyes that may lead to blindness. DR takes place due to damaged arteries and veins that are a part of the fundus of the eye. Although DR can be prevalent now days, its prevention remains challenging. Visual analysis of the funds and consideration of colour photographs Ophthalmologists directly examine the existence and severity of DR. This process is expensive as well as time consuming as there are huge number of diabetes affected people worldwide. The automatic Diabetic Retinopathy system is expanded to predict various related diseases that are analysed. The proposed methodology uses EYEPACS dataset that consists on 35,126 fundus images. These images are pre-processed and sent to neural network that detects type of DR. CNN detects clusters of pixels that are damaged in the macula region and in turn evaluates the overall damaged area in the macula from the retinal images. The retinal fundus images present structural and impulsive noise.

A novel automatic retinal vessel extraction using maximum entropy based EM algorithm

The extraction of blood vessels helps in the diagnosis of diseases and to develop advances of medicine. Retinal blood vessel extraction plays a crucial role in early detection and treatment of retinal diseases. This paper provides an automatic segmentation of blood vessels in retinal images. First, the fundus images go through preprocessing steps of image acquisition, grey scale conversion, bias correction and adaptive histogram equalization to enhance the appearance of retinal blood vessels. Then the retinal blood vessels are extracted using a probabilistic modeling and maximum entropy based expectation maximization algorithm which uses maximum entropy uniform distribution as the initial condition. The vessels are more accurately confined using image profiles computed perpendicularly across each of the detected vessel centerline. The algorithm is implemented in MATLAB and the performance is tested on retinal images from DRIVE and STARE databases. When validated, we conclude that the segmentation of retinal images using the proposed method shows a sensitivity of 98.9%, a specificity of 83.74%, and an Accuracy score of 98.8%.

Blood vessel extraction in fundus images using hessian eigenvalues and adaptive thresholding

Retinal image blood vessels are having significant role in different eye related diseases such as diabetic retinopathy, glaucoma, cataract, age-related macular degeneration and many more. Vasculature retinal feature extraction is an important factor to different doctors for treatment and diagnosis of different diseases. For automatic extraction of retinal blood vessels, different types of clustering related approaches (i.e. k-means/fuzzy c-means) are introduced to explore blood vessels from real time retinal images. Novel blood vessel extraction approach is introduced to explore retinal blood vessels with unsupervised clustering procedures like fuzzy c-means followed with Gabor filter. In this paper, we propose Enhanced blood vessel exploration approach (EBVEA) to improve the segmentation and visualization of vasculature retinal images or fundus images in blood vessel extraction with a combination of hessian based center-to boundary (BB) filters. These filters are used to indicate elongated boundary structures by enhancing the functions based on hessian Eigen values represented in (nxn) matrix. Performance of proposed enhanced approach with traditional approaches in terms of true positive rate (tpr), accuracy etc. are tested. Experimental results carried out and tested on bench mark data sets like DRIVE and STARE datasets produced better results.

Retinal Blood Vessel Extraction using Wavelet Decomposition

One important part of the eye that is critical for processing visual information before it is sent through the optic nerve to the visual cortex is the retina. The retina of each individual has its own uniqueness that can be used as a characteristic feature in identifying, verifying, and authenticating. The traditional authentication process has various weaknesses such as forgetting the PIN code or losing the ID card used for obtaining system authentication. The results of extracted retinal blood vessels can be used as a feature in the formation of an individual identification system. In the imaging using a fundus camera, the retina’s blood vessel has distinguishing shape and number of candidates from one human retina to another. In this research, researchers will develop an algorithm for extracting the retinal fundus image’s blood vessels. The feature extraction is done by taking the fundus image feature which is the blood vessel as one of the unique characteristics in forming an individual identification system. The number of blood vessel candidates will then be calculated from the extracted blood vessel result. This research uses wavelet function by looking at the very complex texture of blood vessels using the approximation coefficient. The direction detail coefficient on the wavelet is also used to perform the extraction of retinal blood vessels where the structure of the retinal blood vessels in the fundus image is in all directions. The results of these blood vessel candidates will be used in further research to formulate a biometric system that is formed by unique features in the retinal fundus image which will be used to identify individuals using body traits.