Blood Vessel Segmentation Research Articles

Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding

Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. In this paper, a novel method is proposed for the detection and grading of neovascularization. The proposed system first performs pre-processing on input retinal images to enhance the vascular pattern, followed by blood vessel segmentation and optic disc localization. Then various features are tested on the candidate regions with different thresholds. In this way, positive and negative advanced diabetic retinopathy cases are separated. Optic disc coordinates are applied for the grading of neovascularization as NVD or NVE. The proposed algorithm improves the quality of automated diagnostic systems by eliminating normal blood vessels and exudates that might cause hindrances in accurate disease detection, thus resulting in more accurate detection of abnormal blood vessels. The evaluation of the proposed system has been carried out using performance parameters such as sensitivity, specificity, accuracy, and positive predictive value (PPV) on a publicly available standard retinal image database and one of the locally available databases. The proposed algorithm gives an accuracy of 98.5% and PPV of 99.8% on MESSIDOR and an accuracy of 96.5% and PPV of 100% on the local database.

A Novel Hybrid Retinal Blood Vessel Segmentation Algorithm for Enlarging the Measuring Range of Dual-Wavelength Retinal Oximetry

The non-invasive measurement of hemoglobin oxygen saturation (SO2) in retinal vessels is based on spectrophotometry and the absorption spectral characteristics of the tissue. The dual-wavelength retinal images are simultaneously captured via retinal oximetry. SO2 is calculated by processing a series of images and by calculating the optic density ratio of two images. However, existing SO2 research is focused on the thick vessels in the high-clarity region of retinal images. However, the thin vessels in the low-clarity region could provide significant information for the detection and diagnosis of neovascular diseases. To this end, we proposed a novel hybrid vessel segmentation algorithm. Firstly, a median filter was employed for image denoising. Secondly, high- and low-clarity region segmentation was carried out based on a clarity histogram. The vessels in the high-clarity areas were segmented after implementing a Gaussian filter, a matched filter, and morphological segmentation. Additionally, the vessels in the low-clarity areas were segmented using a guided filter, matched filtering, and dynamic threshold segmentation. Finally, the results were obtained through image merger and morphological operations. The experimental results and analysis show that the proposed method can effectively segment thick and thin vessels and can extend the measuring range of dual-wavelength retinal oximetry.

Blood flow characterization in nailfold capillary using optical flow-assisted two-stream network and spatial-temporal image

The blood flow velocity in the nailfold capillary is an important indicator of the status of microcirculation. The conventional manual processing method is both laborious and prone to human artifacts. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. Inspired by the Two-Stream Convolutional Networks, this study proposes an optical flow-assisted two-stream network to segment nailfold blood vessels. Firstly, we use U-Net as the spatial flow network and the dense optical flow as the temporal stream. The results show that the optical flow information can effectively improve the integrity of the segmentation of blood vessels. The overall accuracy is 94.01 %, the Dice score is 0.8099, the IoU score is 0.6806, and the VOE score is 0.3194. Secondly, The flow velocity of the segmented blood vessel is determined by constructing the spatial-temporal (ST) image. The blood flow velocity evaluated is consistent with the typical blood flow speed reported. This study proposes a novel two-stream network for blood vessel segmentation of nailfold capillary images. Combined with ST image and line detection method, it provides an effective workflow for measuring the blood flow velocity of nailfold capillaries.

SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation

Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we propose SegR-Net, a deep learning framework for robust retinal vessel segmentation. SegR-Net utilizes a combination of feature extraction and embedding, deep feature magnification, feature precision and interference, and dense multiscale feature fusion to generate accurate segmentation masks. The model consists of an encoder module that extracts high-level features from the input images and a decoder module that reconstructs the segmentation masks by combining features from the encoder module. The encoder module consists of a feature extraction and embedding block that enhances by dense multiscale feature fusion, followed by a deep feature magnification block that magnifies the retinal vessels. To further improve the quality of the extracted features, we use a group of two convolutional layers after each DFM block. In the decoder module, we utilize a feature precision and interference block and a dense multiscale feature fusion block (DMFF) to combine features from the encoder module and reconstruct the segmentation mask. We also incorporate data augmentation and pre-processing techniques to improve the generalization of the trained model. Experimental results on three fundus image publicly available datasets (CHASE_DB1, STARE, and DRIVE) demonstrate that SegR-Net outperforms state-of-the-art models in terms of accuracy, sensitivity, specificity, and F1 score. The proposed framework can provide more accurate and more efficient segmentation of retinal blood vessels in comparison to the state-of-the-art techniques, which is essential for clinical decision-making and diagnosis of various eye diseases.

An optimal approach to detect retinal diseases by performing segmentation of retinal blood vessels using image processing

An optimal approach to detect retinal diseases by performing segmentation of retinal blood vessels using image processing

Digital subtraction angiography image segmentation based on multiscale Hessian matrix applied to medical diagnosis and clinical nursing of coronary stenting patients

ObjectiveTo introduce an improved method of digital subtraction angiography image segmentation based on a multi-scale Hessian matrix, to accurately segment vascular images before and after coronary stent implantation, which will have strong applications in medical diagnostics and clinical nursing of patients who underwent coronary stents implantation. MethodsFirstly, a vessel edge enhancement algorithm is proposed, which enhances the gradient of the vessel edge, which not only makes the obtained vessel edge smoother, but also enhances the visual effect of the intersection of vessels; secondly, introduces noise filtering based on morphology, which can detect and remove linear noise similar to blood vessels; finally, in view of the problem that each image in the DSA blood vessel sequence can only show part of the blood vessels, which is not conducive to observing the overall state of the blood vessels, a blood vessel sequence image fusion is designed to display the blood vessels in each image on the same image. The state of blood vessels can be understood as a whole in one image. ResultsCompared with the segmentation method in the literature, the blood vessel overlap rate increased by 0.0089, and the mis-segmentation rate decreased by 0.0334. In the quantitative analysis, the improved blood vessel segmentation algorithm in this paper improves the overlapping rate and reduces the mis-segmentation rate. ConclusionFor the comparison of visual effects, the blood vessels segmented by the algorithm in this paper have higher clarity, which is helpful for doctors and nurses to comprehensively synthesize blood vessel clinical information. There is a more objective image basis in the preoperative diagnosis and postoperative rehabilitation nursing of coronary angiographic stent implantation.

Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images

Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images

Robust retinal blood vessel segmentation using a patch-based statistical adaptive multi-scale line detector

The diagnosis of diseases using the retinal vasculature has long been a focus of medical research. Segmenting the vessels from retinal images is still a difficult task because of the variability in retinal vascular thickness and the inhomogeneity of image intensity. In this research work, a two-stage retinal vessel segmentation method is proposed. At the first stage, the large vessels are segmented by using multi-scale line detection by suppressing the small vessels through non-local mean filtering. At the second stage, the image is enhanced through noise suppression attributed largely to the block-matching 3-D filtering inside the nonsubsampled contourlet transform. Then a patch-based multi-scale line detector with adaptive statistical thresholding is used. Finally, both large and small vessels are combined to reconstruct the final vessel segmented image. The proposed method is evaluated on two publicly available databases and achieves an average sensitivity of 82.61%, and 81.67% on the DRIVE and STARE datasets respectively. The proposed method outperforms other state-of-the-art methods and has proven to be effective in boosting the sensitivity of retinal vessel segmentation.

Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning.

Recent advances in optical tissue clearing and three-dimensional (3D) fluorescence microscopy have enabled high resolution in situ imaging of intact tissues. Using simply prepared samples, we demonstrate here "digital labeling," a method to segment blood vessels in 3D volumes solely based on the autofluorescence signal and a nuclei stain (DAPI). We trained a deep-learning neural network based on the U-net architecture using a regression loss instead of a commonly used segmentation loss to achieve better detection of small vessels. We achieved high vessel detection accuracy and obtained accurate vascular morphometrics such as vessel length density and orientation. In the future, such digital labeling approach could easily be transferred to other biological structures.

Decision Support System for Blood Vessel and Optic Disc Segmentation

Decision Support System for Blood Vessel and Optic Disc Segmentation

Deep Learing Technique for Diabetic Retinopath Classification

Abstract: Strict control of blood glucose and blood pressure is critical for reduction of the incidence and progression of diabetic retinopathy (DR). Follow-up of patients with diabetes mellitus is protocol based and not based solely on the presence of symptoms. Staging of the level of DR (mild, moderate, or severe nonproliferative DR vs. proliferative DR, PDR) drives the follow-up interval. The most common cause of visual loss in diabetic patients is diabetic macular edema (DME). Detection of Eye Disorders Through Retinal Image Analysis Blood Vessel Segmentation, Optic Disc Segmentation and Fuzzy Logic Image Processing. Common Retinal Eye Disorders that has been solved in this project. The results of multicenter, randomized studies suggest that the best visual results for DME currently are achieved with intravitreal ranibizumab injections ± focal laser photocoagulation. Results using bevacizumab seem quite comparable to those with ranibizumab. In addition to treating DME, this approach also seems to reduce the likelihood of progression of DR. Selected patients also may benefit from intravitreal steroid treatment and focal laser therapy, but there is a relatively higher rate of glaucoma and cataract formation. An increase in intraocular pressure inside the retina led to the development of the neuro-degenerative eye condition known as glaucoma. Being the second largest cause of blindness worldwide, It can lead the person towards complete blindness if an early diagnosis does not take place. The architecture of CNN is cognate to that of the linking form of neurons in the brain of humans and was inspired by the suggestion of the visual cortex. The CNN algorithm has a faster prediction along with better accuracy.

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.

A Retinal Lesion Detection of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common cause of vision loss among people with diabetes and leading to the cause of blindness. Microaneurysms (MAs) are primary lesion of Diabetic retinopathy, so their detection can give time to prevent further vision loss. Hemorrhages (HMs) and exudates (EXs) are the other type of lesions occurs during Diabetic retinopathy. The Retinal lesion detection consists of retinal image preprocessing, Optic disc detection and removal, Blood vessel segmentation, Candidate Lesion detection, Feature extraction, Classification and post processing . Publicly available DiaretDB1 dataset is used for performance evaluation.

3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis.

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.

Hybrid adaptive deep learning classifier for early detection of diabetic retinopathy using optimal feature extraction and classification.

Diabetic retinopathy (DR) is one of the leading causes of blindness. It is important to use a comprehensive learning method to identify the DR. However, comprehensive learning methods often rely heavily on encrypted data, which can be costly and time consuming. Also, the DR function is not displayed and is scattered in the high-definition image below. Therefore, learning how to distribute such DR functions is a big challenge. In this work, we proposed a hybrid adaptive deep learning classifier for early detection of diabetic retinopathy (HADL-DR). First, we provide an improved multichannel-based generative adversarial network (MGAN) with semi-maintenance to detect blood vessels segmentation. By reducing the reliance on the encoded data, the following high-resolution images can be used to detect the indivisible features of some semi-observed MGAN references. Scale invariant feature transform (SIFT) function is then extracted and the best function is selected using the improved sequential approximation optimization (SAO) algorithm. After that, a hybrid recurrent neural network with long short-term memory (RNN-LSTM) is utilized for DR classification. The proposed RNN-LSTM classifier evaluated through standard benchmark Kaggle and Messidor datasets. Finally, the simulation results are compared with the existing state-of-art classifiers in terms of accuracy, precision, recall, f-measure and area under cover (AUC), it is seen that more successful results are obtained.

Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness. Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. Transfer learning has been applied to diabetic retinopathy classification with promising results. Pre-trained models, such as convolutional neural networks (CNNs), can be fine-tuned on a new dataset of retinal images to classify diabetic retinopathy. This manuscript aims at developing an automated scheme for diagnosing and grading DR and HR. The retinal image classification has been performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology has been proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques have been applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images has been performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification have been performed using transfer learning models. The segmented images were then classified using Modified ResNet 101 architecture. The performance for enhanced images has been evaluated on PSNR and shows better results as compared to the existing literature. The network is trained on more than 6000 images from MESSIDOR and ODIR datasets and achieves the classification accuracy of 98.72%.

Near-infrared vascular image segmentation using improved level set method

Near-infrared vascular images are crucial for vascular disease diagnosis and treatment. To solve the problems of difficult segmentation and inaccurate computation issues, we propose a series of image processing methods. First, the images are preprocessed, including background removal, contrast stretching, and noise suppression. After that, the images are enhanced with a two-stage image enhancement method, which successfully combines the benefits of convolutional neural network and traditional image enhancement method. Finally, the images are segmented by an Adaptive Prior Shape Level Set Evolution (APSLSE) method, which effectively improves the common problems of initial contour sensitivity and unidirectional variation of area terms. A shape restriction item is designed to the level set function to improve its suitability for the segmentation of blood vessels. A dataset of 360 images is collected for the research and validation of above algorithms. Extensive experimental results show that the proposed algorithms can effectively process the poor quality near-infrared blood vessel images and segment their vascular shape. Compared the segmentation results with those manually labelled by experts, the False Negative Rate (FNR) is 0.1930 and False Positive Rate (FPR) is 0.04633.

DBCU-Net: deep learning approach for segmentation of coronary angiography images.

Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bi-directional ConvLSTM(BConvLSTM). The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bi-directional ConvLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively.

Attention aware fully convolutional deep learning model for retinal blood vessel segmentation

Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases.

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.