Blood Vessels In Fundus Images Research Articles

Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images.

Automated retinal blood vessel segmentation in fundus images provides important evidence to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive way. However, segmenting blood vessels in fundus images is a challenging task, due to the high variety in scale and appearance of blood vessels and the high similarity in visual features between the lesions and retinal vascular. Inspired by the way that the visual cortex adaptively responds to the type of stimulus, we propose a Stimulus-Guided Adaptive Transformer Network (SGAT-Net) for accurate retinal blood vessel segmentation. It entails a Stimulus-Guided Adaptive Module (SGA-Module) that can extract local-global compound features based on inductive bias and self-attention mechanism. Alongside a light-weight residual encoder (ResEncoder) structure capturing the relevant details of appearance, a Stimulus-Guided Adaptive Pooling Transformer (SGAP-Former) is introduced to reweight the maximum and average pooling to enrich the contextual embedding representation while suppressing the redundant information. Moreover, a Stimulus-Guided Adaptive Feature Fusion (SGAFF) module is designed to adaptively emphasize the local details and global context and fuse them in the latent space to adjust the receptive field (RF) based on the task. The evaluation is implemented on the largest fundus image dataset (FIVES) and three popular retinal image datasets (DRIVE, STARE, CHASEDB1). Experimental results show that the proposed method achieves a competitive performance over the other existing method, with a clear advantage in avoiding errors that commonly happen in areas with highly similar visual features. The sourcecode is publicly available at: https://github.com/Gins-07/SGAT.

Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE).

Diabetic Retinopathy is a persistent disease of eyes that may lead to permanent loss of sight. In this paper, methodology is proposed to segment region of interest (ROI) i.e. new blood vessels in fundus images of retina of Diabetic Retinopathy (DR). The database of 50 fundus retinal images of healthy subjects and DR patients is fetched from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. The experimental set up consists of three set of experiments for the disease. For DR, in the first stage of automated blood vessel segmentation, gray-scale image is produced from the colored image using Principal Component Analysis (PCA) in the preprocessing step. The contrast enhancement by the Contrast Limited Adaptive Histogram Equalization (CLAHE) highlights the retinal blood vessels in the gray-scale image i.e. it unsheathed newly formed retinal blood vessels whereas PCA preserved their texture and color discrimination in DR images. The expert ophthalmologist(s) scrutiny on both internet repository and real time data acted as the gold standard for further analysis and formation of the proposed method. Further, ophthalmologists ascertained the forming of new blood vessels only on the disc region and divulging them, which were impossible with the naked eye. These operations help in extracting retinal blood vessels present on the disc and non-disc region of the image. The comparison of the results are done with the state of art methods like watershed transform. It is observed from the results that the new blood vessels are better segmented by the proposed methodology and are marked by the experienced ophthalmologist for validation. Further, for quantitative analysis, the features are extracted from new blood vessels as they are crucial for scientific interpretation. The results of the features lie in permissible limits such as no. of segments vary from 2 to 5 and length of segments varies from 49 to 164 pixels. Similarly, other features such as gray level of new blood vessels lie in 0.296-0.935 normalized range, coefficient with variations in gray level in the range of 0.658-10.10 and distance from vessel origin lie in the range of 56-82 pixels respectively. Both quantitative and qualitative results show that the methodologies proposed boosted the ophthalmic and clinical diagnosis. The developed method further handled the false detection of vessels near the optic disk boundary, under-segmentation of thin vessels, detection of pathological anomalies such as exudates, micro-aneurysms and cotton wool spots. From the numerical analysis, ophthalmologist extracted the information of number of vessels formed, length of the new vessels, observation that the new vessels appearing are less homogenous than the normal vessels. Also about the new vessels, whether they lie on the centre of disc region or towards its edges. These parameters lie as per the findings of the ophthalmologists on retinal images and automated detection helped in monitoring and comprehensive patient assessment. The experimental results show case that the proposed method has higher sensitivity, specificity and accuracy as compared to state of art methods i.e. 0.9023, 0.9610 and 0.9921, respectively. Similar results are obtained on retinal fundus images of PGIMER Chandigarh with sensitivity-0.9234, specificity-0.9955 and accuracy-0.9682.

IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.

MS-CANet: Multi-Scale Subtraction Network with Coordinate Attention for Retinal Vessel Segmentation

Retinal vessel segmentation is crucial in the diagnosis of certain ophthalmic and cardiovascular diseases. Although U-shaped networks have been widely used for retinal vessel segmentation, most of the improved methods have insufficient feature extraction capability and fuse different network layers using element or dimension summation, leading to redundant information and inaccurate retinal vessel localization with blurred vessel edges. The asymmetry of small blood vessels in fundus images also increases the difficulty of segmenting blood vessels. To overcome these challenges, we propose a novel multi-scale subtraction network (MS-CANet) with residual coordinate attention to segment the vessels in retinal vessel images. Our approach incorporates a residual coordinate attention module during the encoding phase, which captures long-range spatial dependencies while preserving precise position information. To obtain rich multi-scale information, we also include multi-scale subtraction units at different perceptual field levels. Moreover, we introduce a parallel channel attention module that enhances the contrast between vessel and background, thereby improving the detection of marginal vessels during the decoding phase. We validate our proposed model on three benchmark datasets, namely DRIVE, CHASE, and STARE. The results demonstrate that our method outperforms most advanced methods under different evaluation metrics.

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

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.

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.

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.

Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE_DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method.

A new approach for retinal vessel differentiation using binary particle swarm optimization

ABSTRACT Hypertensive Retinopathy is an ocular disease that occurs due to the presence of hypertension in the body. Hypertensive Retinopathy is quantified using a parameter known as Arteriovenous Ratio. To calculate Arteriovenous Ratio, retinal blood vessels in fundus images are segmented, classified, and measured. The complex structure of retinal vessels and uneven illumination in fundus images make retinal vessel classification a difficult task. In this paper, we propose a method for an improved differentiation of retinal vessels, that employs Binary Particle Swarm Optimization to select the optimal retinal vessel features. We design an objective function that considers the size and relevancy of the feature subset in classifying the retinal vessels. The designed objective function ensures the selected feature subset contains the minimum number of features and it corresponds to maximum vessel classification accuracy. Once, the optimization process has reached the stopping criteria, the selected feature subset is evaluated using Support Vector Machines. We compare the retinal vessel classification accuracy obtained using the proposed framework with the existing state-of-the-art approaches and found our method more accurate and robust on healthy as well as diseased images. The effectiveness of the proposed method is validated using the public as well as a private dataset.

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.

Dilated Deep Neural Network for Segmentation of Retinal Blood Vessels in Fundus Images

Medical diagnosis is being assisted by numerous expert systems that have been developed to increase the accuracy of such diagnoses. The development of image processing techniques along with the rapid development in areas like machine learning and computer vision help in creating such expert systems that almost nearly match the accuracy of the expert human eye. The medical condition of diabetic retinopathy is diagnosed by analyzing the retinal blood vessels for damages, abnormal new growths and ruptures. Various techniques using convolutional neural networks have been used to segment retinal blood vessels from fundus images, but these techniques often do not segment the retinal blood vessels accurately and add additional noise due to the limited receptive field of the convolutional filters. The limited receptive field of the convolutional layer prevents the convolutional neural network from getting an accurate context of objects that extend beyond the size of the filter. The proposed architecture uses a dilated convolutional filter to obtain a larger receptive field which leads to a greater accuracy in segmenting the retinal blood vessels with near human accuracy. The convolutional neural networks were trained using the popular datasets. The proposed architecture produced an area under ROC curve (AUC) of 0.9794 and an accuracy of 95.61% and required very few iterations to train the network.

Extraction of Blood Vessels in Fundus Images of
 Retina through Hybrid Segmentation Approach

A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for images of different eye-related diseases and the degradation of the vessels differ frompatient to patient. If the blood vessels are extracted from the fundus images, it will make thediagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithmexclusively for the extraction of blood vessels from the fundus image. The proposed algorithm ishybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement(MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, thearea-based morphological operator is applied to highlight the blood vessels. To validate theproposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus(HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments theblood vessels with more accuracy than the existing algorithms.

Retinal Health Investigation by Segmentation of Major and Minor Blood Vessels in Fundus Images for Diabetes Patients

Retinal Health Investigation by Segmentation of Major and Minor Blood Vessels in Fundus Images for Diabetes Patients

Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.

Multi‐level deep neural network for efficient segmentation of blood vessels in fundus images

The exact blood vessel trees segmented from fundus images provide important information required for screening and following-up of diabetic retinopathy and age-related macular degeneration. The trained deep neural network presents an automated prediction of the blood vessels in retinal fundus camera images in the publicly DRIVE database with accuracy up to 0.9533 and area under the receiver operating characteristic curve up to 0.9752, which is better than manual recognition by expert human eyes. A resizing technique is introduced and applied to the multi-level network combining dropout and spatial-dropout layers to obtain more generalised training. The proposed model has the potential for the classification of other types of images.

An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

Algorithms for retinal vessel segmentation are powerful tools in automatic tracking systems for early detection of ophthalmological and cardiovascular diseases, and for biometric identification. In order to create more robust and reliable systems, the algorithms need to be accurately evaluated to certify their ability to emulate specific human expertise. The main contribution of this paper is an unsupervised method to detect blood vessels in fundus images using a coarse-to-fine approach. Our methodology combines Gaussian smoothing, a morphological top-hat operator, and vessel contrast enhancement for background homogenization and noise reduction. Here, statistics of spatial dependency and probability are used to coarsely approximate the vessel map with an adaptive local thresholding scheme. The coarse segmentation is then refined through curvature analysis and morphological reconstruction to reduce pixel mislabeling and better estimate the retinal vessel tree. The method was evaluated in terms of its sensitivity, specificity and balanced accuracy. Extensive experiments have been conducted on DRIVE and STARE public retinal images databases. Comparisons with state-of-the-art methods revealed that our method outperformed most recent methods in terms of sensitivity and balanced accuracy with an average of 0.7819 and 0.8702, respectively. Also, the proposed method outperformed state-of-the-art methods when evaluating only pathological images that is a more challenging task. The method achieved for this set of images an average of 0.7842 and 0.8662 for sensitivity and balanced accuracy, respectively. Visual inspection also revealed that the proposed approach effectively addressed main image distortions by reducing mislabeling of central vessel reflex regions and false-positive detection of pathological patterns. These improvements indicate the ability of the method to accurately approximate the vessel tree with reduced visual interference of pathological patterns and vessel-like structures. Therefore, our method has the potential for supporting expert systems in screening, diagnosis and treatment of ophthalmological diseases, and furthermore for personal recognition based on retinal profile matching.

Automatic parameters selection of Gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation

Retinal images play an important role in the early diagnosis of diseases such as diabetes. In the present study, an automatic image processing technique is proposed to segment retinal blood vessels in fundus images. The technique includes the design of a bank of 180 Gabor filters with varying scale and elongation parameters. Furthermore, an optimization method, namely, the imperialism competitive algorithm (ICA), is adopted for automatic parameter selection of the Gabor filter. In addition, a systematic method is proposed to determine the threshold value for reliable performance. Finally, the performance of the proposed approach is analyzed and compared with that of other approaches on the basis of the publicly available DRIVE database. The proposed method achieves an area under the receiver operating characteristic curve of 0.953 and an average accuracy of up to 0.9392. Thus, the results show that the proposed method is well comparable with alternative methods in the literature.

A new tool to connect blood vessels in fundus retinal images.

This paper presents a novel tool that allows a user to reconstruct the retinal vascular network from fundus images. The retinal vasculature consists of trees of arteries and veins. Common segmentation algorithms are not able to completely segment out the blood vessels in fundus images. This failure results in a set of disconnected or broken up vascular segments. Reconstructing the whole network has crucial importance because it can offer insight into global features not considered so far, including retinal fluid dynamics. This tool uses implicit neural cost functions to join vessel segments. Results have shown that the quality of the segmentation affects the outcome of connectivity algorithms and by enhancing the segmentation the connectivity can be improved.

Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection

The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

Digital Image Processing for Ophthalmology: Detection and Modeling of Retinal Vascular Architecture

The monitoring of the effects of retinopathy on the visual system can be assisted by analyzing the vascular architecture of the retina. This book presents methods based on Gabor filters to detect blood vessels in fundus images of the retina. Forty images of the retina from the Digital Retinal Images for Vessel Extraction (DRIVE) database were used to evaluate the performance of the methods. The results demonstrate high efficiency in the detection of blood vessels with an area under the receiver operating characteristic curve of 0.96. Monitoring the openness of the major temporal arcade (MTA) could facilitate improved diagnosis and optimized treatment of retinopathy. This book presents methods for the detection and modeling of the MTA, including the generalized Hough transform to detect parabolic forms. Results obtained with 40 images of the DRIVE database, compared with hand-drawn traces of the MTA, indicate a mean distance to the closest point of about 0.24mm. This book illustrates a plications of the methods mentioned above for the analysis of the effects of proliferative diabetic retinopathy and retinopathy of prematurity on retinal vascular architecture.