Blood Vessel Segmentation Research Articles

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

A Convolutional Autoencoder Approach for Boosting the Specificity of Retinal Blood Vessels Segmentation

Automated retina vessel segmentation of the human eye plays a vital role as it can significantly assist ophthalmologists in identifying many eye diseases, such as diabetes, stroke, arteriosclerosis, cardiovascular disease, and many other human illnesses. The fast, automatic and accurate retina vessel segmentation of the eyes is very desirable. This paper introduces a novel fully convolutional autoencoder for the retina vessel segmentation task. The proposed model consists of eight layers, each consisting of convolutional2D layers, MaxPooling layers, Batch Normalisation layers and more. Our model has been trained and evaluated on DRIVE and STARE datasets with 35 min of training time. The performance of the autoencoder model we introduce is assessed on two public datasets, the DRIVE and the STARE and achieved quite competitive results compared to the state-of-the-art methods in the literature. In particular, our model reached an accuracy of 95.73, an AUC_ROC of 97.49 on the DRIVE dataset, and an accuracy of 96.92 and an AUC ROC of 97.57 on the STARE dataset. Furthermore, our model has demonstrated the highest specificity among the methods in the literature, reporting a specificity of 98.57 on the DRIVE and 98.7 on the STARE dataset, respectively. The above statement can be noticed in the final blood vessel segmentation images produced by our convolutional autoencoder method since the segmentations are more accurate, sharp and noiseless than the result images of other proposed methods.

Retinal disease prediction through blood vessel segmentation and classification using ensemble-based deep learning approaches

Retinal disease prediction through blood vessel segmentation and classification using ensemble-based deep learning approaches

Retinal Vessel Segmentation to Support Foveal Avascular Zone Detection

Purpose: This study aims to perform retinal vessel segmentation to support foveal avascular zone detection. Methodology: The proposed approach consists of a multi-stage image processing approach, including preprocessing, image quality enhancementt, and segmentation of retinal blood vessel using matched filter and length filter techniques.Findings: The proposed framework has achieved remarkable results with an average sensitivity, specificity, and accuracy of 77.99%, 86.43%, and 85.24%, respectively.Value: This achievement has the potential to significantly enhance the accuracy and efficiency of detecting and diagnosing medical conditions related to the retina, improving the quality of life for countless individuals.

Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization

The structure of the retinal blood vessels can be obtained by segmenting the fundus images. A fundus image can be gained through color fundus photography or fluorescein angiography (FA). The fundus image produced by the camera can cause noise which can reduce the quality of the fundus image. To reduce the noise, this research uses the non-local means filter (NLMF). For texture analysis, the study uses Gabor filters due to the frequencies of this filter as the same as the human visual system. The segmenting process of the retinal blood vessel is performed using K-means optimized by particle swarm optimization (PSO). The accuracy of 0.9525, the precision of 0.8330, the sensitivity of 0.5817, and the specificity of 0.9880 are obtained using the proposed method.

Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system.

The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity. 317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients is classified into C0-C4 according to the Meta Analysis of the Pathologic Myopia (META-PM) classification and their vascular morphological characteristics in ultra-wide field imaging were analyzed using transfer learning methods and RU-net. Correlation with axial length (AL), best corrected visual acuity (BCVA) and age was analyzed. In addition, the vascular morphological characteristics of myopic choroidal neovascularization (mCNV) patients and their matched high myopia patients were compared. The RU-net and transfer learning system of blood vessel segmentation had an accuracy of 98.24%, a sensitivity of 71.42%, a specificity of 99.37%, a precision of 73.68% and a F1 score of 72.29. Compared with healthy control group, high myopia group had smaller vessel angle (31.12 ± 2.27 vs. 32.33 ± 2.14), smaller fractal dimension (Df) (1.383 ± 0.060 vs. 1.424 ± 0.038), smaller vessel density (2.57 ± 0.96 vs. 3.92 ± 0.93) and fewer vascular branches (201.87 ± 75.92 vs. 271.31 ± 67.37), all P < 0.001. With the increase of myopia maculopathy severity, vessel angle, Df, vessel density and vascular branches significantly decreased (all P < 0.001). There were significant correlations of these characteristics with AL, BCVA and age. Patients with mCNV tended to have larger vessel density (P < 0.001) and more vascular branches (P = 0.045). The RU-net and transfer learning technology used in this study has an accuracy of 98.24%, thus has good performance in quantitative analysis of vascular morphological characteristics in Ultra-wide field images. Along with the increase of myopic maculopathy severity and the elongation of eyeball, vessel angle, Df, vessel density and vascular branches decreased. Myopic CNV patients have larger vessel density and more vascular branches.

Spatial multi-scale attention U-improved network for blood vessel segmentation

Spatial multi-scale attention U-improved network for blood vessel segmentation

Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction.

The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.

Optimized ResUNet++-Enabled Blood Vessel Segmentation for Retinal Fundus Image Based on Hybrid Meta-Heuristic Improvement

In recent years, several studies have undergone automatic blood vessel segmentation based on unsupervised and supervised algorithms to reduce user interruption. Deep learning networks have been used to get highly accurate segmentation results. However, the incorrect segmentation of pathological information and low micro-vascular segmentation is considered the challenges present in the existing methods for segmenting the retinal blood vessel. It also affects different degrees of vessel thickness, contextual feature fusion in technique, and perception of details. A deep learning-aided method has been presented to address these challenges in this paper. In the first phase, the preprocessing is performed using the retinal fundus images employed by the black ring removal, LAB conversion, CLAHE-based contrast enhancement, and grayscale image. Thus, the blood vessel segmentation is performed by a new deep learning model termed optimized ResUNet[Formula: see text]. As an improvement to this deep learning architecture, the activation function is optimized by the J-AGSO algorithm. The objective function for the optimized ResUNet[Formula: see text]-based blood vessel segmentation is to minimize the binary cross-entropy loss function. Further, the post-processing of the images is carried out by median filtering and binary thresholding. By verifying the standard benchmark datasets, the proposed model outperforms and attains enhanced performance.

DA-Res2UNet: Explainable blood vessel segmentation from fundus images

Blood vessel segmentation in fundus images is necessary for the diagnosis of ophthalmic diseases. In recent years, deep learning has achieved eminent performance in blood vessel segmentation, and there still exist challenges to reduce misidentification and improve microvascular segmentation accuracy. One reason is that traditional Convolutional Neural Network (CNN) can not effectively extract multiscale information and discard the unnecessary information. Another reason is we can’t explain why some blood vessels fail to be identified. On the one hand, this paper proposes a Dual Attention Res2UNet (DA-Res2UNet) model. The DA-Res2UNet model uses Res2block rather than CNN to obtain more multiscale information and adds Dual Attention to help the model focus on important information and discard unnecessary information. On the other hand, the explainable method based on a pre-trained fundus image generator is adopted to explore how the model identifies blood vessels. We deduce several special situations that lead to the misidentification based on the model’s explanation and adjust the dataset for these special cases. The adjusted datasets significantly reduce the misidentification in the CHASE_DB1 dataset. Finally, the model trained by the adjusted datasets achieves the state-of-the-art F1-score of 81.88%, 82.77%, and 83.96% on the CHASE_DB1, DRIVE and STARE datasets, respectively.

Application of Novel Down-sampling Method in Retinal Vessel Segmentation

Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F1-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.

Enhance the frame accumulation accuracy of breast multispectral transmission image by the improved Powell registration algorithm

Multispectral transmission imaging provides a new direction for the early screening of breast cancer. Frame accumulation technology can effectively solve the problem of low grayscale and poor multispectral transmission images quality, but the accuracy of the result will be reduced due to the shift between image frames caused by human respiration and jitter. Therefore, an improved Powell registration algorithm based on normalized mutual information is proposed to register sequence images to improve the accuracy of images after frame accumulation. Firstly, the video is acquired and preprocessed through the designed breast transmission image acquisition system. Then, the coarse registration parameters are obtained by the "Rotation Translation scanning" method, which are used as the initial point of Powell algorithm for fine registration to obtain the registered image sequence. Finally, frame accumulation is performed on the registered images. The registration accuracy and the image quality of frame accumulation after registration are evaluated respectively. The results show that the improved method can raise the registration accuracy of the set floating image to more than 98.9%. This method not only improves the registration accuracy of Powell algorithm, but also solves the influence of jitter on frame accumulation technology, and provides a reference for the recognition and segmentation of breast blood vessels or tumors in multispectral transmission images.

Human retinal biometric recognition system based on multiple feature extraction

Retinal biometric trait is one of the safest and most secure biometric traits that can be used for personal identification and authentication. The poor contrast in captured retinal images initially requires the adoption of a preprocessing technique, especially when the vessel segmentation is required. As a result, an effective preprocessing technique is employed before the feature extraction process. The reliability of the segmentation depends on the consistency of the contrast across the images. Due to the complexity of the retinal vascular patterns, segmenting the vessels manually is a time-consuming task that also requires extensive training and abilities to distinguish the thinner blood vessel pixels from the background of the retinal images. We present a unique blood vessel segmentation technique based on Radial Chebyshev Moments (RCMs) shape descriptors. Moment-based shape descriptors are typically strong, concise, and simple to compute. They are also insensitive to the object’s scale, rotation, and translation. Furthermore, to eliminate the eye movement and to overcome the various lighting problems, the following multiple features are extracted in the feature extraction stage to accurately authenticate a person: statistical (gray-level) features, shape-based (RCM) features, and structural features, such as minutiae, bifurcation points, optical disc, and enclosed regions. The principal components analysis methodology is used to optimize the feature vector, and the target image is identified using the support vector machine classifier. This step helps to determine whether the test image belongs to the authorized person or not. RIDB and VARIA are the two standard retinal authentication databases and DRIVE and STARE are the two general retinal databases that are used for evaluating the performance of the system. The system yielded a high identification rate of 98%, 97.5%, 97.2% and 94.4% with average processing time of 0.987 s, 0.40 s, 1.262 s and 0.65 s, respectively. From the results, it is evident that the proposed method is more effective and efficient for authenticating individuals.

A retinal vessel segmentation method based improved U-Net model

There are two problems in retinal blood vessel segmentation, which are the insufficient segmentation of small vessels due to the complex curvature morphology of blood vessels and the segmentation difficulty of blood vessels due to uneven brightness background of lesion fundus images. To solve the problems, a series deformable convolution structure is proposed in this paper, which could improve theadaptive features extraction ability to the blood vessels with various shapes and sizes, enhance feature transmissions and alleviate exploding gradients. On this basis, a retinal vessel segmentation method with series deformable convolution and attention mechanism based on U-Net structure (SDAU-Net) is proposed. In SDAU-Net, the convolution module in U-Net is replaced by series deformable convolution module, the lightweight attention module and dual attention module are applied in the decoder part, which effectively improve the U-Net feature extraction ability for the small vessels with complex morphology and the retinopathy images. To verify the SDAU-Net effect, the comparative experiments are conducted on datasets of DRIVE, STARE, CHASE_DB1 and IOSTAR. The results show that SDAU-Net is superior to comparative methods in accuracy. The Se and Acc on the DRIVE and STARE are 0.8293, 0.9675, 0.8973 and 0.9833 respectively, which indicate that SDAU-Net has more advantages in small vessel segmentation and lesion images. To verify the generalization and extendibility, cross-dataset and cross-modality experiments are conducted on DRIVE, STARE and IOSTAR, the results demonstrate outstanding generalization and extendibility of SDAU-Net.

Efficacy of ultrasound vector flow imaging in tracking omnidirectional pulsatile flow.

Ultrasound vector flow imaging (VFI) shows potential as an emerging non-invasive modality for time-resolved flow mapping. However, its efficacy in tracking multidirectional pulsatile flow with temporal resolvability has not yet been systematically evaluated because of the lack of an appropriate test protocol. We present the first systematic performance investigation of VFI in tracking pulsatile flow in a meticulously designed scenario with time-varying, omnidirectional flow fields (with flow angles from 0° to 360°). Ultrasound VFI was performed on a three-loop spiral flow phantom (4mm diameter; 5mm pitch) that was configured to operate under pulsatile flow conditions (10ml/s peak flow rate; 1Hz pulse rate; carotid pulse shape). The spiral lumen geometry was designed to simulate recirculatory flow dynamics observed in the heart and in curvy blood vessel segments such as the carotid bulb. The imaging sequence was based on steered plane wave pulsing (-10°, 0°, +10° steering angles; 5MHz imaging frequency; 3.3kHz interleaved pulse repetition frequency). VFI's pulsatile flow estimation performance and its ability to detect secondary flow were comparatively assessed against flow fields derived from computational fluid dynamics (CFD) simulations that included consideration of fluid-structure interactions (FSI). The mean percentage error (MPE) and the coefficient of determination (R2 ) were computed to assess the correspondence of the velocity estimates derived from VFI and CFD-FSI simulations. In addition, VFI's efficacy in tracking pulse waves was analyzed with respect to pressure transducer measurements made at the phantom's inlet and outlet. Pulsatile flow patterns rendered by VFI agreed with the flow profiles computed from CFD-FSI simulations (average MPE: -5.3%). The shape of the VFI-measured velocity magnitude profile generally matched the inlet flow profile. High correlation exists between VFI measurements and simulated flow vectors (lateral velocity: R2 =0.8; axial velocity R2 =0.89; beam-flow angle: R2 =0.98; p<0.0001 for all three quantities). VFI was found to be capable of consistently tracking secondary flow. It also yielded pulse wave velocity (PWV) estimates (5.72±1.02m/s) that, on average, are within 6.4% of those obtained from pressure transducer measurements (6.11±1.15m/s). VFI can consistently track omnidirectional pulsatile flow on a time-resolved basis. This systematic investigation serves well as a quality assurance test of VFI.

Highly accurate blood vessel segmentation using texture‐based modified K‐means clustering with deep learning model

SummaryThis paper suggests a blood vessel segmentation using the Texture‐Based Modified K‐means Clustering (TBMKC) technique to lessen the detrimental effects of light lesions. In the suggested method, the K value is chosen automatically by maximizing the local pixel data of the input image. Utilizing the Gabor Filter and Scale‐Invariant Feature Transform (SIFT), respectively, texture and statistical characteristics are used to extract the local information. The number of pixels present in each class is used to alter the cluster centroid using Particle Swarm Optimization (PSO) methods. Local adaptive thresholding is then used to separate the blood vessel pattern. The proposed method is invariant to scales and rotations due to SIFT features. The thresholding output is given to the modified DCNN (Deep Convolutional Neural Network). Finally, the performance of the (MDCNN+ TBMKC+ PSO) scheme is computed over other extant schemes. The outcomes of the adopted scheme is determined and tested using the retinal image from DRIVE, STARE, HRF, and CHASE_DBI databases. The accuracy of the proposed method is 13.41%, 7.22%, 9.56%, 6.24%, 2.35%, 14.54%, 13.23%, 3.71%, 10.82%, 8.28%, 7.98%, and 54.01% better than the existing method like M‐GAN, MCET‐HHO, CNN, DBN, SVM, RNN, NN, MDCNN+PSO, MDCNN+GA, MCNN+FF, MDCNN+ABC, and MDCNN+LA.

Impact of Retinal Vessel Image Coherence on Retinal Blood Vessel Segmentation

Retinal vessel segmentation is critical in detecting retinal blood vessels for a variety of eye disorders, and a consistent computerized method is required for automatic eye disorder screening. Many methods of retinal blood vessel segmentation are implemented, but these methods only yielded accuracy and lack of good sensitivity due to the coherence of retinal blood vessel segmentation. Another main factor of low sensitivity is the proper technique to handle the low-varying contrast problem. In this study, we proposed a five-step technique for assessing the impact of retinal blood vessel coherence on retinal blood vessel segmentation. The proposed technique for retinal blood vessels involved four steps and is known as the preprocessing module. These four stages of the pre-processing module handle the retinal image process in the first stage, uneven illumination and noise issues using morphological operations in the second stage, and image conversion to grayscale using principal component analysis (PCA) in the third step. The fourth step is the main step of contributing to the coherence of retinal blood vessels using anisotropic diffusion filtering and testing their different schemes and get a better coherent image on the optimized anisotropic diffusion filtering. The last step included double thresholds with morphological image reconstruction techniques to produce a segmented image of the vessel. The performances of the proposed method are validated on the publicly available database named DRIVE and STARE. Sensitivity values of 0.811 and 0.821 on STARE and DRIVE respectively meet and surpass other existing methods, and comparable accuracy values of 0.961 and 0.954 on STARE and DRIVE databases to existing methods. This proposed new method for retinal blood vessel segmentations can help medical experts diagnose eye disease and recommend treatment in a timely manner.

An intelligent approach for detection and grading of diabetic retinopathy and diabetic macular edema using retinal images

ABSTRACT Early detection of diabetic retinopathy (DR) and diabetic macular edema (DME) is difficult due to the presence of abnormalities like micro-aneurysms in the fundus images which causes vision loss for diabetic patients. In this paper, we detect DR and DME in an earlier stage by performing preprocessing to enhance the quality of the input image, which includes three steps: noise filtering, artefact removal and contrast enhancement. Second, blood vessel segmentation is performed, using Improved Mask-Regional Convolutional Neural Networks (Mask RCNN), which increases the accuracy and precision rate of DR and DME detection. Finally, feature extraction and classification using VGG-16 is performed, which extracts structural features, colour and orientation features. Based on the extracted features, the proposed VGG-16 classifies the image into three classes: normal, DR and DME. After detecting DR and DME, we calculate the severity level of the disease using conditional entropy which classifies the severity into mild, moderate and severe. The proposed work is evaluated by two different datasets: IDRiD and MESSIDOR. The performance of the DR and DME detection is evaluated in terms of accuracy (80.7%), sensitivity (93.67%), specificity (93.67%), F1 score (94.61%), ROC curve and AUC curve compared to existing works.

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.

Retinal blood vessel segmentation using root Guided decision tree assisted enhanced Fuzzy C-mean clustering for disease identification

A number of groundbreaking computer-aided techniques have recently been established for the segmentation of retinal vessels, with clinical applications predominating. Early detection of these abnormalities is critical in preventing individuals from becoming blind. In recent days, automatic vessel segmentation is necessary, but it is a difficult task due to the complex structure and variation in vessel size and shape of retinal vessels. Therefore, in this work, a novel retinal Blood Vessel Segmentation (BVS) has been proposed that utilizes the potential of the Enhanced Fuzzy C-mean Clustering (EFCM) and Root Guided Decision Tree (RGTC). Initially, the input images are pre-processed to enhance the color channel contrast and the Gabor filter is utilized to enhance the small vessels. In the segmentation phase, a 13-dimensional feature vector is constructed, which is then reduced into an optimum feature set by utilizing Principal Component Analysis (PCA). The segmentation phase is utilized to further process the new feature vector. The RGDT is used to undertake reanalysis in non-vessel clustersin order to recover the incorrectly grouped vessel pixels. Finally, the accurate segmented image is obtained through post-processing morphological operations. Simulation has been performed using Python with OpenCV and an accuracy of 97.69% and 97.51% has been achieved using in STARE dataset and DRIVE dataset respectively. Experimental results indicate that the proposed algorithm delivers satisfactory results. As a result, this method is useful as an automated retinal BVS system to assist ophthalmologists.