Vessel Extraction Research Articles

An experimental methodology to validate the use of hydroethanolic mixtures as suspending medium / modifier for the supercritical CO2 extraction of suspensions

We developed a methodology to study the extraction of high-value solutes directly from suspensions of finely disrupted substrates. For that, we modelled the high-pressure phase equilibrium for the ternary (CO2 + ethanol + water) system using experimental literature data. Different compositions of hydroethanolic mixture and CO2 were loaded into an extraction vessel set at 30–35MPa and 40–50 °C during static extraction, and a gaseous mixture with the composition of the CO2-rich gaseous phase in the extraction vessel was continuously fed during dynamic extraction. Losses of the fed hydroethanolic mixture occurred mainly during dynamic extraction (10-30wt%) and were properly distributed to account for actual flows and compositions of experimental streams. Mostly, equilibrium conditions were reached following about 1h of the 2-h dynamic extraction, and good reproducibility was achieved. In conclusion, equilibrium is reached in which two phases coexist in equilibrium within the extraction vessel: a water-rich liquid phase and a CO2-rich gaseous phase.

Technical evaluation of supercritical fluid impregnation scaling-up of olive leaf extract: From lab to pilot scale

Supercritical fluid technology is an advanced field that offers remarkable versatility and it applies to a broad range of processes. However, the scaling-up of the supercritical fluid impregnation process is still scarcely explored. This research investigates the technical analysis of the supercritical fluid impregnation scaling-up of olive (Olea europaea L.) leaf extract into polypropylene (PP) films. The impregnation scale was increased up to 20-fold while maintaining constant ratios among the extract volume, film area, CO2 amount, and vessel geometry. The study compares homogeneity, total extract loading, and ABTS antioxidant activity of impregnated films at both scales. Homogeneity was assessed using CIELAB coordinates and desorption electrospray ionization mass spectroscopy (DESI-MS) imaging. Films impregnated at 250 bar and 55 °C for 1 h in 100 ml and 500 ml vessels were uniform in colour, whereas those from the larger vessel were less homogeneous. To evaluate the effect of time on impregnation at the largest scale, impregnation was conducted for 1, 6, 24, and 48 h. Homogeneity and ABTS antioxidant activity improved over time, and DESI-MS images showed a gradual decrease in oleuropein signal intensity over time. After 6 h, PP films achieved a total extract loading of 14.02 mg g−1 of polymer, 23 % ABTS antioxidant activity, with a global diffusion coefficient of the extract compounds in PP films of 3.42 × 10−13 m2 s−1. While this study offers valuable insights into scaling up the supercritical fluid impregnation of olive leaf extract into PP films, several limitations remain. Future research is required for a more comprehensive understanding and practical application of the process.

A 96-position platform for magnetic sorbent-based dispersive microextraction: Application to the determination of tetrahydrocannabinol and cannabidiol in saliva of cannabis smokers

BackgroundIn order to obtain sustainable analytical methods, it is essential to develop kinetically efficient sample preparation strategies in which equilibria are reached faster, as dispersive extraction techniques. In addition, the higher the reduction in size, the higher number of extraction vessels can be located and thus the higher number of samples can be simultaneously treated. All this increases sample throughput, and contributes to the reduction of chemical waste, and energy and sample consumption. In this sense, multiposition extraction platforms are smart strategies to achieve these goals, but they are scarcely developed for dispersive extraction techniques. ResultsTaking miniaturized stir bar sorptive dispersive microextraction (mSBSDME) as a starting point, a 96-position extraction platform has been developed using a 96-position stirring plate and a tailor-designed 3D-printed support for locating the miniaturized extraction vessels, achieving a high-throughput miniaturized sample preparation strategy. In order to show the applicability of this novel platform, the determination of Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in human saliva has been carried out and applied to samples collected after the consumption of marijuana and legal CBD-rich cannabis. Only 100 μL of saliva were needed for the analysis and good analytical features in terms of linearity (at least up to 500 ng mL−1), limits of detection (0.7 and 2.8 ng mL−1 for THC and CBD, respectively), and precision (RSD ≤ 14 %) were achieved. SignificanceThe miniaturization of the vessel allows the use of small volumes of sample (i.e., a few microliters) and the treatment of 96 samples in parallel, being the first proposal for carrying out dispersive sorbent-based microextraction under the concept of 96-well format. Additionally, this new workflow contributes to the development of analytical methods that meet the three pillars of sustainability, i.e., greenness and easily affordable in terms of economics and applicability.

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, where thin vessels in the foreground are proportionally minimal. Secondly, they are susceptible to poor image quality and blurred vessel edges, resulting in discontinuities or breaks in vascular structures. In response, this paper proposes the Skeleton-guided Multi-scale Dual-coordinate Attention Aggregation (SMDAA) network for retinal vessel segmentation. SMDAA comprises three innovative modules: Dual-coordinate Attention (DCA), Unbalanced Pixel Amplifier (UPA), and Vessel Skeleton Guidance (VSG). DCA, integrating Multi-scale Coordinate Feature Aggregation (MCFA) and Scale Coordinate Attention Decoding (SCAD), meticulously analyzes vessel structures across various scales, adept at capturing intricate details, thereby significantly enhancing segmentation accuracy. To address class imbalance, we introduce UPA, dynamically allocating more attention to misclassified pixels, ensuring precise extraction of thin and small blood vessels. Moreover, to preserve vessel structure continuity, we integrate vessel anatomy and develop the VSG module to connect fragmented vessel segments. Additionally, a Feature-level Contrast (FCL) loss is introduced to capture subtle nuances within the same category, enhancing the fidelity of retinal blood vessel segmentation. Extensive experiments on three public datasets (DRIVE, STARE, and CHASE_DB1) demonstrate superior performance in comparison to current methods. The code is available at https://github.com/wangwxr/SMDAA_NET.

Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.

Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.

Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.

Accurate and efficient segmentation of coronary arteries from CTA images is crucial for diagnosing and treating cardiovascular diseases. This study proposes a structured approach that combines vesselness enhancement, heart region of interest (ROI) extraction, and the ResUNet deep learning method to accurately and efficiently extract coronary artery vessels. Vesselness enhancement and heart ROI extraction significantly improve the accuracy and efficiency of the segmentation process, while ResUNet enables the model to capture both local and global features. The proposed method outperformed other state-of-the-art methods, achieving a Dice similarity coefficient (DSC) of 0.867, a Recall of 0.881, and a Precision of 0.892. The exceptional results for segmenting coronary arteries from CTA images demonstrate the potential of this method to significantly contribute to accurate diagnosis and effective treatment of cardiovascular diseases.

Enhancing vascular network visualization in 3D photoacoustic imaging: in vivo experiments with a vasculature filter

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.

A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model.

Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet's generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations.

A Microvascular Segmentation Network Based on Pyramidal Attention Mechanism.

The precise segmentation of retinal vasculature is crucial for the early screening of various eye diseases, such as diabetic retinopathy and hypertensive retinopathy. Given the complex and variable overall structure of retinal vessels and their delicate, minute local features, the accurate extraction of fine vessels and edge pixels remains a technical challenge in the current research. To enhance the ability to extract thin vessels, this paper incorporates a pyramid channel attention module into a U-shaped network. This allows for more effective capture of information at different levels and increased attention to vessel-related channels, thereby improving model performance. Simultaneously, to prevent overfitting, this paper optimizes the standard convolutional block in the U-Net with the pre-activated residual discard convolution block, thus improving the model's generalization ability. The model is evaluated on three benchmark retinal datasets: DRIVE, CHASE_DB1, and STARE. Experimental results demonstrate that, compared to the baseline model, the proposed model achieves improvements in sensitivity (Sen) scores of 7.12%, 9.65%, and 5.36% on these three datasets, respectively, proving its strong ability to extract fine vessels.

Skip connection information enhancement network for retinal vessel segmentation.

Many major diseases of the retina often show symptoms of lesions in the fundus of the eye. The extraction of blood vessels from retinal fundus images is essential to assist doctors. Some of the existing methods do not fully extract the detailed features of retinal images or lose some information, making it difficult to accurately segment capillaries located at the edges of the images. In this paper, we propose a multi-scale retinal vessel segmentation network (SCIE_Net) based on skip connection information enhancement. Firstly, the network processes retinal images at multiple scales to achieve network capture of features at different scales. Secondly, the feature aggregation module is proposed to aggregate the rich information of the shallow network. Further, the skip connection information enhancement module is proposed to take into account the detailed features of the shallow layer and the advanced features of the deeper network to avoid the problem of incomplete information interaction between the layers of the network. Finally, SCIE_Net achieves better vessel segmentation performance and results on the publicly available retinal image standard datasets DRIVE, CHASE_DB1, and STARE.

Evaluation of conditions to minimize mercury losses from leaching test eluates

A series of experiments are described that provide recommendations of modifications to leaching test specifications to minimize losses of mercury from eluates during leaching tests and management of analytical samples. EPA methods of the Leaching Environmental Assessment Framework (LEAF) were used as a reference point for what steps are most likely to see losses, including filtration, storage of unpreserved mercury contaminated material, and materials of construction for vessels. The potential sorption of mercury to borosilicate glass and PTFE lab materials was tested to determine what materials are suitable to replace HDPE and polypropylene. The experimental findings indicate that bottles constructed of PTFE and Type I (borosilicate) glass are recommended as extraction and containment vessels for mercury-containing liquids. Type I syringe filtration of aqueous mercury samples at pH 2 through 9 through 0.45 µm PTFE filters housed in polypropylene is acceptable; however, sorption losses were observed to occur at pH > 9. Storage of unpreserved liquids containing mercury over extended leaching test intervals did not lead to significant losses of mercury when coupled with minimized headspace. All mercury-containing eluates should include preservation with Optima HNO3 to a 1 % concentration and gold (III) standard to a concentration of 200 µg/L, followed by refrigeration at <6 °C prior to analysis.

RVS-FDSC: A retinal vessel segmentation method with four-directional strip convolution to enhance feature extraction

Retinal vessel segmentation is important to assist ophthalmologists in the diagnosis and treatment of ophthalmic diseases. However, traditional convolutional neural networks (CNNs) are constrained by using square convolution kernels for retinal vessel feature extraction, thereby overlooking the structural characteristics of retinal vessels, which to some extent limits model performance. Consequently, we propose a retinal vessel segmentation method with four-directional strip convolution to enhance feature extraction (RVS-FDSC). Firstly, due to the shape structure of retinal vessels which have branching paths and appear locally as strips, we have constructed a residual strip convolution (RSC) module by using strip convolutions in four different directions: horizontal, vertical, antidiagonal, and main diagonal. These four directions can roughly simulate the branching directions of vessels, making it possible to better extract vessel features. Furthermore, to more comprehensively understand and utilize both local details and global context, RVS-FDSC introduces a multi-scale pooling feature fusion (MSPF2) module. Finally, to better focus on important features and reduce the attention to unimportant or redundant information, RVS-FDSC introduces a residual parallel dual attention (RPDA) module. We conduct experiments on three retinal vessel segmentation datasets: DRIVE, CHASE-DB1, and STARE. RVS-FDSC achieves excellent results on these three public datasets, with AUC of 0.9856, 0.9867, and 0.9808, Acc of 0.9692, 0.9743, and 0.9718, and Spe of 0.9878, 0.9856 and 0.9882, respectively. These results indicate the superior performance of RVS-FDSC compared to other state-of-the-art methods.

Multimodality Fusion Strategies in Eye Disease Diagnosis.

Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis proves difficult, often missing crucial disease indicators. In response, researchers advocate multimodality-based approaches to enhance diagnostics. This study is a unique exploration, evaluating three multimodality fusion strategies-early, joint, and late-in conjunction with state-of-the-art convolutional neural network models for automated eye disease binary detection across three datasets: fundus fluorescein angiography, macula, and combination of digital retinal images for vessel extraction, structured analysis of the retina, and high-resolution fundus. Findings reveal the efficacy of each fusion strategy: type 0 early fusion with DenseNet121 achieves an impressive 99.45% average accuracy. InceptionResNetV2 emerges as the top-performing joint fusion architecture with an average accuracy of 99.58%. Late fusion ResNet50V2 achieves a perfect score of 100% across all metrics, surpassing both early and joint fusion. Comparative analysis demonstrates that late fusion ResNet50V2 matches the accuracy of state-of-the-art feature-level fusion model for multiview learning. In conclusion, this study substantiates late fusion as the optimal strategy for eye disease diagnosis compared to early and joint fusion, showcasing its superiority in leveraging multimodal information.

VGA‐Net: Vessel graph based attentional U‐Net for retinal vessel segmentation

AbstractSegmentation is crucial in diagnosing retinal diseases by accurately identifiying retinal vessels. This paper addresses the complexity of segmenting retinal vessels, highlighting the need for precise analysis of blood vessel structures. Despite the progress made by convolutional neural networsks (CNNs) in image segmentation, their limitations in capturing the global structure of retinal vsessels and maintaining segmentation continuity present challenges. To tackle these issues, our proposed network integrates graph convolutional networks (GCNs) and attention mechansims. This allows the model to consider pixel relationships and learn vessel graphical structures, significantly improving segmentation accuracy. Additionally, the attentional feature fusion module, including pixel‐wise and channel‐wise attention mechansims within the U‐Net architecture, refines the model's focus on relevant features. This paper emphasizes the importance of continuty preservation, ensuring an accurate representation of pixel‐level information and structural details during sefmentation. Therefore, our method performs as an effective solution to overcome challenges in retinal vessel segmentation. The proposed method outperformed the state‐of‐the‐art approaches on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structed Analysis of the Retina) datasets with accuracies of 0.12% and 0.14%, respecttively. Importantly, our proposed approach excelled in delineating slender and diminutive blood vessels, crucial for diagnosing vascular‐related diseases. Implementation is accessible on https://github.com/CVLab‐SHUT/VGA‐Net.

Impact of vessel volume on thermodilution measurements in patients with coronary microvascular dysfunction.

Two invasive methods are available to estimate microvascular resistance: bolus and continuous thermodilution. Comparative studies have revealed a lack of concordance between measurements of microvascular resistance obtained through these techniques. This study aimed to examine the influence of vessel volume on bolus thermodilution measurements. We prospectively included patients with angina with non-obstructive coronary arteries (ANOCA) undergoing bolus and continuous thermodilution assessments. All patients underwent coronary CT angiographyto extract vessel volume. Coronary microvascular dysfunction was defined as coronary flow reserve (CFR) < 2.0. Measurements of absolute microvascular resistance (in Woods units) and index of microvascular resistance (IMR) were compared before and after volumetric adjustment. Overall, 94 patients with ANOCA were included in this study. The mean age was 64.7 ± 10.8 years, 48% were female, and 19% had diabetes. The prevalence of CMD was 16% based on bolus thermodilution, while continuous thermodilution yielded a prevalence of 27% (Cohen's Kappa 0.44, 95% CI 0.23-0.65). There was no correlation in microvascular resistance between techniques (r = 0.17, 95% CI -0.04 to 0.36, p = 0.104). The adjustment of IMR by vessel volume significantly increased the agreement with absolute microvascular resistance derived from continuous thermodilution (r = 0.48, 95% CI 0.31-0.63, p < 0.001). In patients with ANOCA, invasive methods based on coronary thermodilution yielded conflicting results for the assessment of CMD. Adjusting IMR with vessel volume improved the agreement with continuous thermodilution for the assessment of microvascular resistance. These findings strongly suggest the importance of considering vessel volume when interpreting bolus thermodilution assessment.

Automatic 3D coronary artery segmentation based on local region active contour model.

Segmentation of coronary arteries in computed tomography angiography (CTA) images plays a key role in the diagnosis and treatment of coronary-related diseases. However, manually analyzing the large amount of data is time-consuming, and interpreting this data requires the prior knowledge and expertise of radiologists. Therefore, an automatic method is needed to separate coronary arteries from a given CTA dataset. Firstly, an anisotropic diffusion filter was employed to smooth the noise while preserving the vessel boundaries. The coronary skeleton was then extracted using a two-step process based on the intensity of the coronary. In the first step, the thick vessel skeleton was extracted by clustering, improved vesselness filtering and region growing, while in the second step, the thin vessel skeleton was extracted by the height ridge traversal method guided by the cylindrical model. Next, the vesselness measure, representing vessel a priori information, was incorporated into the local region active contour model based on the vessel geometry. Finally, the initial contour of the active contour model was generated using the coronary artery skeleton for effective segmentation of the three-dimensional (3D) coronary arteries. Experimental results on chest CTA images show that the method is able to segment coronary arteries effectively with an average precision, recall and dice similarity coefficient (DSC) of 86.64%, 91.26% and 79.13%, respectively, and has a good performance in thin vessel extraction. The method does not require manual selection of vessel seeds or setting of initial contours, and allows for the extraction of a successful coronary artery skeleton and eventual effective segmentation of the coronary arteries.

Multiscale attention network via topology learning for cerebral vessel segmentation in angiography images

Cerebrovascular diseases are a widespread threat to human health. The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases. However, the complexity of cerebral vessel structures and the low imaging contrast present significant challenges for vessel segmentation. Therefore, we propose a Multiscale Attention Network based on topological learning to extract vessel structures from angiographic images. This method employs a Multiscale Squeeze Attention (MSA) module for channel-wise attention learning, extracting multiscale attention feature maps from angiographic images. To maintain the topological connectivity of vessel segmentation, we introduced the clDice loss function to enforce skeleton connectivity of vessel segmentation. We conducted an experimental analysis of the proposed method using a publicly available cerebral vessel dataset. The results demonstrated that the proposed method achieved a sensitivity score of 0.8507 and a dice score of 0.8669 for cerebrovascular segmentation, enabling accurate and complete extraction of vascular structures. The proposed method was extended to coronary angiography images. The results show that the proposed method can accurately extract coronary structures, proving its broad applicability to other vascular segmentation tasks.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images

Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.

A novel vessel extraction technique for a three-way classification of diabetic retinopathy using cascaded classifier

A novel vessel extraction technique for a three-way classification of diabetic retinopathy using cascaded classifier