Vessel Segmentation Algorithm Research Articles

Laddernet retinal vessel segmentation algorithm fusing attentional mechanisms

A retinal blood vessel segmentation algorithm fusing the attention mechanism is proposed to address the problems of insufficient accuracy and incorrect segmentation of minutiae in existing retinal blood vessel segmentation algorithms. The network structure used in this algorithm is Laddernet (LCT-UNet for short), which fuses the Convolutional Block Attention Module (CBAM) and Transformer, i.e., the residual module of Laddernet, which shares the weights, is structurally optimised to decrease the parameter count; After the residual module, CBAM is incorporated to adaptively adjust the weights of channels and spatial features to enhance the feature representation; Transformer with global self-attention mechanism is introduced at the bottom of the network to overcome the shortcomings of UNet's inability to model long-range relationships and spatial dependencies, and gradually refine the features so that the segmentation results have finer boundary and detail information. The model validity was verified on the public datasets DRIVE and CHASEDB1, and the experimental results showed that the ACC of LCT-UNet was 95.81% and 96.88%, SE was 79.74% and 81.12%, SP was 98.15% and 98.83%, and AUC was 98.22% and 98.99%, and the F1 values were 82.88% and 85.14%, respectively, and the comprehensive segmentation performance of the LCT-UNet network showed significant enhancement over the algorithms such as U-Net, R2UNet, and Laddernet.

Advancing Opthalmic Diagnostics: U-Net for Retinal Blood Vessel Segmentation

This research project focuses on the development and evaluation of an advanced algorithm for retinal vessel segmentation, a critical component in the automated analysis of retinal images for diagnosing ocular diseases. Leveraging state-of-the-art image processing techniques and deep learning models, we propose a novel segmentation algorithm that significantly enhances the accuracy and efficiency of identifying retinal blood vessels from fundus photographs. Our methodology encompasses a comprehensive data preparation phase, including image normalization and augmentation, to improve the model's robustness and generalizability. We implemented a convolutional neural network (CNN)-based architecture optimized for the intricate patterns and variations inherent in retinal images. The performance of our algorithm was rigorously evaluated against established benchmarks, demonstrating superior precision, recall, and a higher Dice coefficient compared to existing methods. These findings indicate the potential of our approach to contribute substantially to the early detection and monitoring of ocular conditions such as diabetic retinopathy and glaucoma. The research underscores the importance of advanced computational techniques in enhancing the diagnostic capabilities of retinal image analysis and sets the stage for future innovations in medical imaging.

Bi-directional ConvLSTM residual U-Net retinal vessel segmentation algorithm with improved focal loss function

Accurate blood vessel segmentation on retinal blood vessel images is helpful for the early detection of ophthalmic diseases such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, and inhibits the deterioration of the disease. In current research within the field of retinal blood vessel segmentation, significant challenges exist in accurately segmenting small blood vessels and maintaining blood vessel continuity. The segmentation algorithm proposed in this article offers substantial improvements to address these issues. To enhance the segmentation performance of retinal blood vessels and facilitate more accurate diagnosis of fundus diseases by ophthalmologists, this paper introduces a novel bidirectional convolutional long short-term memory (LSTM) residual U-Net segmentation algorithm, incorporating improvements to the Focal loss function. Firstly, in the encoding part of U-Net, the multi-scale convolution kernels and Bi-ConvLSTM were adopted to improve the residual structure, obtain richer blood vessel features and enhance the detection ability of micro vessels and the continuity of blood vessel characteristics. At the same time, the class balanced cross entropy loss function was improved and the proportional modulation factor is introduced to enhance the learning ability of the network for difficult samples. By adding the Bi-ConvLSTM to the residual structure and introducing the proportional modulation coefficient to the loss function, the network structure realizes better feature information detection and greatly enhances the detection ability of small blood vessels. The experimental analysis on the DRIVE and CHASE_DB1 data sets showed that the sensitivity, specificity, accuracy and AUC reached 0.7961, 0.9796, 0.9563, 0.9792; 0.8344, 0.9665, 0.9547, 0.9758, respectively. The experimental results fully show that the Bi-ConvLSTM residual U-Net segmentation algorithm based on the improved Focal loss function enhances the detection ability of small blood vessel features, improves the continuity of blood vessel features and the network segmentation performance, and is superior to U-Net algorithm and some current mainstream retinal blood vessel segmentation algorithms.

756 A 3D Deep-learning Based Algorithm for Cerebral Vessel and Aneurysm Segmentation From CTA Images

INTRODUCTION: Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. METHODS: Using 50 paired CTA-DSA images, we trained (n = 27), validated (n = 3) and tested (n = 20) a deep-learning model (3D-DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based co-registration algorithm was used for registration and up-sampling of CTAs to paired DSAs. Segmented vessels from DSAs were used as the ground-truth. Accuracy of the model was evaluated using conventional metrics (dice similarity coefficient-DSC) and vessel-specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 experts detected and segmented IAs. For each rater, we recorded the rate of IA detection. For detected IAs, raters segmented IAs and calculated morphology parameters to quantify the differences in IA segmentation by raters to DeepMedic. The rater agreements were assessed using Krippendorf’s alpha. RESULTS: In testing, the DeepMedic model yielded a DSC and CAL of 0.971 ± 0.007 and 0.868 ± 0.008, respectively. The model delineated all IAs with errors <10% for all IA morphometrics. Conversely, average IA detection accuracy from raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA cavernous region and were significantly smaller). Error rates for IA morphometrics in rater-segmentations were significantly higher than in DeepMedic-segmentations, particularly for neck (p = 0.003), and surface-area (p = 0.003). For IA morphology, agreement between the raters was acceptable for metrics except UI (α=0.36) and NSI (α=0.69). The agreement between DeepMedic and ground truth was consistently higher. CONCLUSIONS: Our CTA segmentation algorithm provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.

Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network

Due to the limitations of traditional retinal blood vessel segmentation algorithms in feature extraction, vessel breakage often occurs at the end. To address this issue, a retinal vessel segmentation algorithm based on a modified U-shaped network is proposed in this paper. This algorithm can extract multi-scale vascular features and perform segmentation in an end-to-end manner. First, in order to improve the low contrast of the original image, pre-processing methods are employed. Second, a multi-scale residual convolution module is employed to extract image features of different granularities, while residual learning improves feature utilization efficiency and reduces information loss. In addition, a selective kernel unit is incorporated into the skip connections to obtain multi-scale features with varying receptive field sizes achieved through soft attention. Subsequently, to further extract vascular features and improve processing speed, a residual attention module is constructed at the decoder stage. Finally, a weighted joint loss function is implemented to address the imbalance between positive and negative samples. The experimental results on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that MU-Net exhibits better sensitivity and a higher Matthew’s correlation coefficient (0.8197, 0.8051; STARE: 0.8264, 0.7987; CHASE_DB1: 0.8313, 0.7960) compared to several state-of-the-art methods.

Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement.

Quantitative monitoring of flow-altering interventions has been proposed using algorithms that quantify blood velocity from time-resolved two-dimensional angiograms. These algorithms track the movement of contrast oscillations along a vessel centerline. Vessel motion may occur relative to a statically defined vessel centerline, corrupting the blood velocity measurement. We provide a method for motion-compensated blood velocity quantification. The motion-compensation approach utilizes a vessel segmentation algorithm to perform frame-by-frame vessel registration and creates a dynamic vessel centerline that moves with the vasculature. Performance was evaluated in-vivo through comparison with manually annotated centerlines. The method was also compared to a previous uncompensated method using best- and worst-case static centerlines chosen to minimize and maximize centerline placement accuracy. Blood velocities determined through quantitative DSA (qDSA) analysis for each centerline type were compared through linear regression analysis. Centerline distance errors were relative to gold standard manual annotations. For the uncompensated approach, the best- and worst-case static centerlines had distance errors of and , respectively. Linear regression analysis found a high -squared between qDSA-derived blood velocities using gold standard centerlines and motion-compensated centerlines () with a slope of 1.15 and a small offset of . The use of static centerlines resulted in low coefficients of determination for the best case () and worst-case () scenarios, with slopes close to zero. In-vivo validation of motion-compensated qDSA analysis demonstrated improved velocity quantification accuracy in vessels with motion, addressing an important clinical limitation of the current qDSA algorithm.

ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

FSE-Net: Rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation

Automatic retinal vessel segmentation plays a crucial role in the diagnosis and assessment of various ophthalmologic diseases. Currently, the primary retinal vessel segmentation algorithms are based on the encoder-decoder structure. However, these U-Net analogs suffer from the loss of both spatial and semantic information, caused by continuous up-sampling operations in the decoder structure. In this paper, we rethink the above problem and build a novel deep neural network for retinal vessel segmentation, called FSE-Net. Specifically, to address the issue of feature information loss and enhance the performance of retinal vessel segmentation, we eliminate the decoder structure. In particular, we introduced a multi-head feature fusion block (MFF) as a substitute for the continuous up-sampling operation. Additionally, the encoder stage of FSE-Net incorporates a residual feature separable block (RFSB) to further refine and distill features, thereby enhancing the capability of feature extraction. Subsequently, we employ a residual atrous spatial feature aggregate module (RASF) to expand the network's receptive field by incorporating multi-scale feature information. We conducted experiments on five widely recognized databases for retinal vessel segmentation, namely DRIVE, CHASEDB1, STARE, IOSTAR, and LES-AV. The results demonstrate that our proposed FSE-Net outperforms state-of-the-art approaches in terms of segmentation performance. Moreover, we demonstrate the feasibility of achieving superior segmentation performance without employing the traditional U-Net analog network structure.

An improved supervised and attention mechanism-based U-Net algorithm for retinal vessel segmentation

The segmentation results of retinal blood vessels are crucial for automatically diagnosing ophthalmic diseases such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular diseases. To improve the accuracy of vessel segmentation and better extract information about small vessels and edges, we introduce the U-Net algorithm with a supervised attention mechanism for retinal vessel segmentation. We achieve this by introducing a decoder fusion module (DFM) in the encoding part, effectively combining different convolutional blocks to extract features comprehensively. Additionally, in the decoding part of U-Net, we propose the context squeeze and excitation (CSE) decoding module to enhance important contextual feature information and the detection of tiny blood vessels. For the final output, we introduce the supervised fusion mechanism (SFM), which combines multiple branches from shallow to deep layers, effectively fusing multi-scale features and capturing information from different levels, fully integrating low-level and high-level features to improve segmentation performance. Our experimental results on the public datasets of DRIVE, STARE, and CHASED_B1 demonstrate the excellent performance of our proposed network.

Age‐ and Sex‐Related Morphological Changes in Cerebral Blood Vessels: a 7T TOF MRA Study

AbstractBackgroundIn this study we evaluated age‐ and sex‐associated morphological changes of cerebral blood vessels using 7T Time‐of‐flight (TOF) magnetic resonance angiography (MRA).MethodOur study consisted of 25 cognitively normal older adults (20 female, mean age 68.2 years). Whole‐Brain TOF MRA images were collected on a 7T Siemens scanner at the University of Pittsburgh (slice number = 354, voxel size = 0.38*0.38*0.38 mm, 12 mins). An iterative vessel segmentation algorithm, VesselMapper, was used to segment blood vessels on TOF MRA images (Li et al., 2023). For each subject, vessels are further divided into large and small vessels by median split based on vessel diameters. Mean tortuosity of large vessels, small vessels and overall vessels was computed. Linear regression analyses were performed to evaluate the main effects of age and sex, and the age*sex interaction on vessel tortuosity and diameter.ResultAs shown in Figure 1, we found age‐related increase in mean vessel tortuosity (greater age is associated with greater tortuosity, p<0.02). This age‐related difference is driven by small vessels (p = 0.002) rather than large vessels (p = 0.7). We also found that age is positively associated with mean voxel tortuosity (p<0.001, black line in Figure 2). Furthermore, women showed greater age‐related increase in mean arterial tortuosity, compared to men (age*sex interaction p = 0.04, Figure 2).ConclusionOur findings validated that morphological markers of cerebral blood vessels are sensitive to aging. Small vessels in the brain are particularly vulnerable. Further, compared to men, older women are at a higher risk for cerebral small vessel disease, which may contribute to elevated SVD‐related AD risk in women.Limitation: The study has an unbalanced sample (more women than men) and a relatively modest sample size (N = 25). A cross‐sectional instead of longitudinal design was used in this analysis. Future study with a large balanced sample and a longitudinal design are required to confirm our findings.Reference: Li, Jiatai, et al. “VesselMapper ‐ A Robust Vessel Segmentation Algorithm for 3D Images.” Annual Meeting of the Organization for Human Brain Mapping. 2023

Age‐ and Sex‐Related Morphological Changes in Cerebral Blood Vessels: a 7T TOF MRA Study

AbstractBackgroundIn this study we evaluated age‐ and sex‐associated morphological changes of cerebral blood vessels using 7T Time‐of‐flight (TOF) magnetic resonance angiography (MRA).MethodOur study consisted of 25 cognitively normal older adults (20 female, mean age 68.2 years). Whole‐Brain TOF MRA images were collected on a 7T Siemens scanner at the University of Pittsburgh (slice number = 354, voxel size = 0.38*0.38*0.38 mm, 12 mins). An iterative vessel segmentation algorithm, VesselMapper, was used to segment blood vessels on TOF MRA images (Li et al., 2023). For each subject, vessels are further divided into large and small vessels by median split based on vessel diameters. Mean tortuosity of large vessels, small vessels and overall vessels was computed. Linear regression analyses were performed to evaluate the main effects of age and sex, and the age*sex interaction on vessel tortuosity and diameter.ResultAs shown in Figure 1, we found age‐related increase in mean vessel tortuosity (greater age is associated with greater tortuosity, p<0.02). This age‐related difference is driven by small vessels (p = 0.002) rather than large vessels (p = 0.7). We also found that age is positively associated with mean voxel tortuosity (p<0.001, black line in Figure 2). Furthermore, women showed greater age‐related increase in mean arterial tortuosity, compared to men (age*sex interaction p = 0.04, Figure 2).ConclusionOur findings validated that morphological markers of cerebral blood vessels are sensitive to aging. Small vessels in the brain are particularly vulnerable. Further, compared to men, older women are at a higher risk for cerebral small vessel disease, which may contribute to elevated SVD‐related AD risk in women. Limitation: The study has an unbalanced sample (more women than men) and a relatively modest sample size (N = 25). A cross‐sectional instead of longitudinal design was used in this analysis. Future study with a large balanced sample and a longitudinal design are required to confirm our findings. Reference: Li, Jiatai, et al. “VesselMapper ‐ A Robust Vessel Segmentation Algorithm for 3D Images.” Annual Meeting of the Organization for Human Brain Mapping. 2023

Abstract 16513: Decreased Pulmonary Blood Flow and Airway Volumes in Patients With Long COVID Syndrome Assessed by Functional Respiratory Imaging

Introduction: In contrast to normal chest X-ray, lung computed tomography (CT), and physiological lung and cardiac functions, many patients with long COVID syndrome suffer from shortness of breath. Hypothesis: The aim of this study was to quantify the pulmonary blood and airway volumes of long COVID patients compared with that of healthy controls. Methods: Patients with long COVID syndromes were included if they had PCR-verified previous (≥3 months) SARS-CoV-2 infection, had normal laboratory (e.g. inflammation, coagulation, cardiac or other organ) parameter, normal pulmonary morphology (chest X-ray and CT) and function (spirometry and body plethysmography). The lung CT images were postprocessed by Functional Respiratory imaging analysis by using 3D reconstruction with automated lung vessel segmentation algorithm. Data of the quantitative images were compared with age, gender, and BMI-matched healthy controls. Results: Thirty patients (45±13 years, 37% male, 25.9±4.3 kg/m^2) at a median time of 256 (118-574) days after a confirmed COVID infection and 30 healthy controls (55±7y, 37% male, 26.3±2.7 kg/m^2) were included. All long COVID patients complained of dyspnoea and 14 (48.3%) patients reported thoracic pain. The total pulmonary blood volume was significantly lower in the long COVID patients compared to controls (190±24.3 mL/m^2 vs230.6±26.2 ml/m^2, p<0.001). Similarly, the capillary-small vessel blood flow (vessel cross sectional area <5 mm^2) was reduced in the long COVID population (118±19 mL vs 132±23 mL, p=0.011). (Figure). The specific image-based airway volume of the distal lung regions was lower than that of the healthy population (11.1±6.74 mL vs 17.33±7.7 mL, p<0.05). Conclusions: Both the reduced global and capillary pulmonary blood flow, and distal airway volumes indicate impaired gas exchange and might explain the pulmonary complaints of patient with long COVID syndromes even severe months after Coronavirus infection.

Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community.

Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence

The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image sequence has important clinical significance for intuitively diagnosis and therapy. In this study, the XCA image sequence is regarded as a 3D tensor input, the vessel layer is regarded as a sparse tensor, and the background layer is regarded as a low-rank tensor. Using tensor nuclear norm (TNN) minimization, a novel method for vessel layer extraction based on tensor robust principal component analysis (TRPCA) is proposed. Furthermore, considering the irregular movement of vessels and the low-frequency dynamic disturbance of surrounding irrelevant tissues, the total variation (TV) regularized spatial–temporal constraint is introduced to smooth the foreground layer. Subsequently, for vessel layer images with uneven contrast distribution, a two-stage region growing (TSRG) method is utilized for vessel enhancement and segmentation. A global threshold method is used as the preprocessing to obtain main branches, and the Radon-Like features (RLF) filter is used to enhance and connect broken minor segments. The final binary vessel mask is constructed by combining the two intermediate results. The visibility of TV-TRPCA algorithm for foreground extraction is evaluated on clinical XCA image sequences and third-party dataset, which can effectively improve the performance of commonly used vessel segmentation algorithms. Based on TV-TRPCA, the accuracy of TSRG algorithm for vessel segmentation is further evaluated. Both qualitative and quantitative results validate the superiority of the proposed method over existing state-of-the-art approaches.

CAS-UNet: A Retinal Segmentation Method Based on Attention

Retinal vessel segmentation is an important task in medical image analysis that can aid doctors in diagnosing various eye diseases. However, due to the complexity and blurred boundaries of retinal vessel structures, existing methods face many challenges in practical applications. To overcome these challenges, this paper proposes a retina vessel segmentation algorithm based on an attention mechanism, called CAS-UNet. Firstly, the Cross-Fusion Channel Attention mechanism is introduced, and the Structured Convolutional Attention block is used to replace the original convolutional block of U-Net to achieve channel enhancement for retinal blood vessels. Secondly, an Additive Attention Gate is added to the skip-connection layer of the network to achieve spatial enhancement for retinal blood vessels. Finally, the SoftPool pooling method is used to reduce information loss. Experimental results using the CHASEDB1 and DRIVE datasets show that the proposed algorithm achieves an accuracy of 96.68% and 95.86%, and a sensitivity of 83.21% and 83.75%, respectively. The proposed CAS-UNet thus outperforms the existing U-Net-based classic algorithms.

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.

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.

A novel vessel segmentation algorithm for pathological en-face images based on matched filter

The vascular information in fundus images can provide important basis for detection and prediction of retina-related diseases. However, the presence of lesions such as Coroidal Neovascularization can seriously interfere with normal vascular areas in optical coherence tomography (OCT) fundus images. In this paper, a novel method is proposed for detecting blood vessels in pathological OCT fundus images. First of all, an automatic localization and filling method is used in preprocessing step to reduce pathological interference. Afterwards, in terms of vessel extraction, a pore ablation method based on capillary bundle model is applied. The ablation method processes the image after matched filter feature extraction, which can eliminate the interference caused by diseased blood vessels to a great extent. At the end of the proposed method, morphological operations are used to obtain the main vascular features. Experimental results on the dataset show that the proposed method achieves 0.88 ± 0.03, 0.79 ± 0.05, 0.66 ± 0.04, results in DICE, PRECISION and TPR, respectively. Effective extraction of vascular information from OCT fundus images is of great significance for the diagnosis and treatment of retinal related diseases.

A modified Coye algorithm for retinal vessel segmentation

According to a scientific study, eyes are the best predictors of numerous disorders including glaucoma, diabetic retinopathy, hypertension, and stroke. An ophthalmologist can learn about the problems by looking at the segmented retinal blood vessel network. The goal of this study is to offer ophthalmologists with reliable segmented retinal blood vessels to help them pinpoint the issue. This work put forwards an automated method of vessel extraction by incorporating curvelet-based enhancement with the Coye algorithm. Further, the segmentation performance is fine-tuned by embodying a pair of complementary gamma functions (PCGF) for contrast improvement. The suggested approach is evaluated on DRIVE and STARE databases and shows outstanding results as compared to state-of-the-art algorithms.

Hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques

Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies.