Blood Vessels In Retinal Images Research Articles

DEEP LEARNING APPROACHES FOR MULTI-CLASS DISEASE DETECTION AND CLASSIFICATION IN RETINAL IMAGES

Many techniques are designed to increase accuracy diagnoses of diseases in retinal images such as hypertensive retinopathy. Convolutional neural networks (CNN) have been employed in various techniques to segment blood vessels in retinal images. These techniques sometimes fail to effectively segment the blood vessels of retina of eye and produce extra noise and we have with this drawback, we present a technique to detect blood vessels in retina of eye images using thousands of blocks (patches) from each of the image. This technique is on a CNN where it contains four convolutional layers followed by relu, max pooling four layers, two layers are fully connected and one layer is the softmax for segmenting blood vessels.

Automated extraction of blood vessels in retinal images using rolling guidance filter

Automated extraction of blood vessels in retinal images using rolling guidance filter

CHANGES IN FRACTAL DIMENSION OF THIN AND THICK BLOOD VESSELS FROM RETINAL FUNDUS IMAGES FOR DIFFERENT STAGES IN DIABETIC RETINOPATHY

Retinal vasculature feature extraction plays a critical role in the diagnosis and treatment of systemic conditions, particularly in the cases of diabetic retinopathy (DR). This research introduces an algorithm that utilizes segmented blood vessels in retinal images to identify and differentiate five stages of DR, including mild, moderate, severe and proliferative. The algorithm effectively extracts retinal blood vessels by integrating morphological operators and matched filters, yielding a more precise output. The algorithm’s performance is evaluated using the database IDRiD, demonstrating precision and sensitivity scores comparable to those of a trained observer. A box-counting method was incorporated to measure the fractal dimension (FD) of DR-segmented vessel images at various stages to enhance the accuracy of DR staging. The FD analysis was applied to both thick and thin segments of the blood vessels, enabling the assessment of accuracy, sensitivity and specificity. The results indicate that the algorithm successfully identifies the different stages of DR with an accuracy of 93.65% for the mild stage, of 93.33% for the moderate and severe stages and of 92.71% for proliferative DR compared to the images without DR. The study reveals that the variation in FD between the thick and thin vessel components can be an effective biomarker for identifying the different stages of DR, contributing to a better understanding of disease progression. By combining morphological operators, matched filters and fractal dimension analysis, this research presents a promising approach for specialists involved in diagnosing and treating DR, eventually leading to improved patient care and consequences.

Unfolded deep kernel estimation-attention UNet-based retinal image segmentation

Retinal vessel segmentation is a critical process in the automated inquiry of fundus images to screen and diagnose diabetic retinopathy. It is a widespread complication of diabetes that causes sudden vision loss. Automated retinal vessel segmentation can help to detect these changes more accurately and quickly than manual evaluation by an ophthalmologist. The proposed approach aims to precisely segregate blood vessels in retinal images while shortening the complication and computational value of the segmentation procedure. This can help to improve the accuracy and reliability of retinal image analysis and assist in diagnosing various eye diseases. Attention U-Net is an essential architecture in retinal image segmentation in diabetic retinopathy that obtained promising results in improving the segmentation accuracy especially in the situation where the training data and ground truth are limited. This approach involves U-Net with an attention mechanism to mainly focus on applicable regions of the input image along with the unfolded deep kernel estimation (UDKE) method to enhance the effective performance of semantic segmentation models. Extensive experiments were carried out on STARE, DRIVE, and CHASE_DB datasets, and the proposed method achieved good performance compared to existing methods.

Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation.

Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network with matrix factorization (CNN with MF) and detailed U-shaped U-Net (D_U-Net) to minimize image noise, culminating in the selection of the most suitable image based on the PSNR and SSIM values. The second step is dynamic data imputation, utilizing multiple models for the purpose of filling in missing data. The third step is data augmentation through the utilization of a latent diffusion model (LDM) to expand the training dataset size. The second stage of the framework is segmentation, where the U-Nets with a multi-residual attention block are used to segment the retinal images after they have been preprocessed and noise has been removed. The experiments show that the framework is effective at segmenting retinal blood vessels. It achieved Dice scores of 95.32, accuracy of 93.56, precision of 95.68, and recall of 95.45. It also achieved efficient results in removing noise using CNN with matrix factorization (MF) and D-U-NET according to values of PSNR and SSIM for (0.1, 0.25, 0.5, and 0.75) levels of noise. The LDM achieved an inception score of 13.6 and an FID of 46.2 in the augmentation step.

The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.

A comprehensive survey on segmentation techniques for retinal vessel segmentation

In recent years, enormous research has been carried out on the segmentation of blood vessels. Segmentation of blood vessels in retinal images is crucial for diagnosing, treating, evaluating clinical results, and early detection of eye disorders. A successful segmentation accurately reflects the blood vessels’ structure and helps obtain patterns that can be used to identify retinal disorders and diseases. Most recent research on vessel segmentation employs multiple processes to determine the appropriate segmentation. Finding the best techniques for segmentation is a complex process. In certain circumstances, it requires a thorough understanding of every step that can only be acquired through years of training. Comprehension and expertise in segmentation procedures are essential for accurate segmentation. This paper briefly introduces the segmentation of blood vessels in retinal images, describes many preprocessing and segmentation techniques, and summarizes challenges and trends. Furthermore, the limitations of the current systems will be identified.

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)

IterNet++: An improved model for retinal image segmentation by curvelet enhancing, guided filtering, offline hard‐sample mining, and test‐time augmenting

In clinical medicine, the segmentation of blood vessels in retinal images is essential for subsequent analysis in clinical diagnosis. However, retinal images are often noisy and their vascular structure is relatively tiny, which poses significant challenges for vessel segmentation. To improve the performance of vessel segmentation, an improved model IterNet++ based on the architecture of IterNet is proposed. First, curvelet signal analysis is applied to enhance retinal images. Second, residual convolution (ResConv) blocks and guided filters are introduced to utilise the encoder features of previous iterations in the model to reduce overfitting. Third, offline hard-sample mining is used to improve segmentation performance by utilising training samples with low segmentation accuracy as many possible on a few-sample training set. In addition, a test-time augmentation method is applied to testing samples in test dataset during inference. Extensive experiments show that this model achieves Dice scores of 0.8313, 0.8277, and 0.8372 on DRIVE, CHASE-DB1, and STARE datasets, respectively, demonstrating the best performance compared with IterNet and other baseline models.

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.

MC-DMD: A data-driven method for blood vessel enhancement in retinal images using morphological closing and dynamic mode decomposition

Detection of blood vessels in retinal images is an important step in any computer-aided pathological system for the early screening and detection of eye-related ailments such as retinal detachment, diabetic retinopathy, and macular degeneration. Propriety of the vessel enhancement process, as a pre-processing step, has been well established in medical corpus, for enhanced accuracy of vessel segmentation. Algorithms for retinal image extraction are still not qualitatively sufficient to be appropriate for diagnostics. This paper presents an effective and robust approach for retinal vessel enhancement that overcome prevailing deficiencies, using Morphological Closing based Dynamic Mode Decomposition (MC-DMD). The proffered algorithm exploits the power of mathematical morphology for the generation of input channel to the Dynamic Mode Decompostion (DMD) system, decomposing the vessel and non-vessel features of retinal images. We confirmed the effectiveness of the proposed enhancement method on three publicly available retinal image datasets: DRIVE, STARE and HRF, across nine existing vessel enhancement methods in terms of Receiver Operating Characteristic (ROC) curve and Area Under the ROC Curve (AUC). In addition, segmentation on final vessel map is performed and validated by enhanced performance in comparison with eight conventional vessel segmentation algorithms in terms of Sensitivity (SEN), Specificity (SP), Accuracy (ACC).

Automated segmentation of blood vessels in retinal images based on entropy weighted thresholding

ABSTRACT Automated extraction of retinal vasculature is becoming a crucial task during the study of pathological disorders present in the retinal fundus image. In this work, we propose an unsupervised approach for automatically extracting blood vessels from fundus photographs. In this paper, Logarithmic transformation (Log) and contrast limited adaptive histogram equalisation (CLAHE) are being used to boost the contrast level of the fundus photographs. After that, Matched filtering technique is used for further vessel enhancement. Then, different thresholding techniques are evaluated and compared to find out the best thresholding scheme for this segmentation. We have found that Otsu with entropy weighting thresholding is the best threshold method for segmenting the blood vessels. In the post-processing step, the area-based thresholding process is considered, and it gives the final vessel segmented image. Using the following statistical measures, the final vessel segmented image is compared to the ground truth image, such as sensitivity (Sens), precision (Spec), and accuracy (Acc). This work is evaluated by freely accessible DRIVE (Digital Retinal Images for Vessel Extraction), STARE (Structured Analysis of the Retina), and CHASE_DB1 (Child Heart and Health Study in England) datasets. This work achieves an average accuracy of 0.9562, 0.946, and 0.946 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively.

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively.

Multi-scale retinal vessel segmentation using encoder-decoder network with squeeze-and-excitation connection and atrous spatial pyramid pooling.

The segmentation of blood vessels in retinal images is crucial to the diagnosis of many diseases. We propose a deep learning method for vessel segmentation based on an encoder-decoder network combined with squeeze-and-excitation connection and atrous spatial pyramid pooling. In our implementation, the atrous spatial pyramid pooling allows the network to capture features at multiple scales, and the high-level semantic information is combined with low-level features through the encoder-decoder architecture to generate segmentations. Meanwhile, the squeeze-and-excitation connections in the proposed network can adaptively recalibrate features according to the relationship between different channels of features. The proposed network can achieve precise segmentation of retinal vessels without hand-crafted features or specific post-processing. The performance of our model is evaluated in terms of visual effects and quantitative evaluation metrics on two publicly available datasets of retinal images, the Digital Retinal Images for Vessel Extraction and Structured Analysis of the Retina datasets, with comparison to 12 representative methods. Furthermore, the proposed network is applied to vessel segmentation on local retinal images, which demonstrates promising application prospect in medical practices.

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%.

An unsupervised approach to improve contrast and segmentation of blood vessels in retinal images using CLAHE, 2D Gabor wavelet, and morphological operations

Retinopathies are the leading cause of eyesight loss, especially among diabetics. Due to the low contrast of blood vessels in fundus images, the visual inspection is a challenging job even for specialists. In this context, this work aims to implement image processing techniques to support contrast enhancement and segmentation of retinal blood vessels. The initial proposal consisted only of green channel separation, contrast limited adaptive histogram equalization, and 2D Gabor wavelet and mathematical morphology. The new proposal includes the edge and mask detection and the vessel enhancement 2D to preserve image’s characteristics. The development and validation of this work, in MatLab® environment, involved 40 images from Digital Retinal Images for Vessel Extraction (DRIVE), 20 images from Structured Analysis of the Retina (STARE), and 45 images from High-Resolution Fundus (HRF) database. In the unsupervised method context, the proposal presented the best performance regarding sensitivity and second place for balanced-accuracy on all databases. A subjective validation involving eleven ophthalmology professionals showed higher levels of acceptance (above 80%) after contrast limited adaptive histogram equalization (CLAHE) and vessel enhancement 2D steps and 75.5% for overall quality system. The main contributions refer to the inclusion of techniques for automatic mask detection, image edge removal, and suppression of vessels background to improve the retinal vessels segmentation process. In addition, this work made a computational interface named “Retinal Lab - A Tool for Fundus Image Analysis” available, which permits the users to adjust the contrast and segmentation of blood vessels in retinal images.

Automatic segmentation of blood vessels in retinal images using 2D Gabor wavelet and sub-image thresholding resulting from image partition

The retina features the only blood vessel network in humans that is visible in a non-invasive imaging method. This, along with uniqueness and stability throughout life in healthy subjects, makes it an ideal target for personal identification methods in biometric systems and also for the screening and diagnosis of diseases. However, retinal images usually present low contrast of the vessels in relation to the retinal background and high level of noise stemming mainly from the acquisition process. This work aims to reduce noise and improve contrast to increase the accuracy of retinal vessel segmentation. 2D Gabor wavelet (GW) is usually employed to reduce noise and improve vessel contrast in relation to the background. In this work, it is proposed that, before the thresholding, the GW output images are partitioned into 20 sub-images in such a way that each can be treated independently. The images used were obtained from two public databases, DRIVE and STARE, and the algorithm was developed in MatLab® environment. The proposed approach reached an accuracy of 96.15%, sensitivity of 73.42%, and specificity of 98.30% in DRIVE. In STARE, the accuracy was 94.87%, sensitivity 71.74%, and specificity 96.93%. The methods proposed by the authors indicate gains in accuracy and specificity in the automatic detection of retinal vessels, in both databases used, when compared with those in the main published works. The accuracy is also higher than the 94.73% in interobserver accuracy previously determined as the gold standard.

Blood vessel detection based on fractional Hessian matrix with non-singular Mittag–Leffler Gaussian kernel

In this work, we proposed a novel method based on fractional Gaussian kernel involving the Atangana–Baleanu fractional derivative to detect blood vessels in retinal images. This method is based on the fractional Gaussian matrix eigenvalues analysis to obtain the principal curvature. The non-integer orders of the fractional Gaussian mask for retinal image denoising and the fractional Gaussian matrix were optimized by Cuckoo Search algorithm to get more accurate results. To evaluate the effectiveness and the robustness of the method it was compared with manual segmentation and other works reported in the literature.

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Diagnosis of computer-based retinopathic hypertension is done by analyzing of retinal images. The analysis is carried out through various stages, one of which is blood vessel segmentation in retinal images. Vascular segmentation of the retina is a complex problem. This is caused by non-uniform lighting, contrast variations and the presence of abnormalities due to disease. This makes segmentation not successful if it only relies on one method. The aims of this study to segment blood vessels in retinal images. The method used is divided into three stages, namely preprocessing, segmentation and testing. The first stage, preprocessing, is to improve image quality with the CLAHE method and the median filter on the green channel image. The second stage, segmenting using a number of methods, namely, frangi filter, 2D-convolution filtering, median filtering, otsu's thresholding, morphology operation, and background subtraction. The last step is testing the system using the DRIVE and STARE dataset. The test results obtained sensitivity 91.187% performance parameters, 86.896% specificity, and area under the curve (AUC) 89.041%. Referring to the performance produced, the proposed model can be used as an alternative for blood vessel segmentation of retinal images.

The multiscale bowler-hat transform for blood vessel enhancement in retinal images

Enhancement, followed by segmentation, quantification and modelling of blood vessels in retinal images plays an essential role in computer-aided retinopathy diagnosis. In this paper, we introduce the bowler-hat transform method a new approach based on mathematical morphology for vessel enhancement. The proposed method combines different structuring elements to detect innate features of vessel-like structures. We evaluate the proposed method qualitatively and quantitatively and compare it with the state-of-the-art methods using both synthetic and real datasets. Our results establish that the proposed method achieves high-quality vessel-like structure enhancement in both synthetic examples and clinically relevant retinal images. The bowler-hat transform is shown to be able to detect fine vessels while still remaining robust at junctions.