Vessel Segmentation Algorithm Research Articles

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.

Abdominal vessel segmentation using vessel model embedded fuzzy C-means and similarity from CT angiography.

The accurate abdominal vessel segmentation of CT angiography (CTA) data is essential for diagnosis and surgical planning. However, accurate abdominal vessel segmentation is a difficult problem since the following challenges: (1) complex abdominal vessel structure containing a wide range size of vessel branches, (2) low contrast of small vessels, and (3) uneven distribution of vessel grayscale. With full consideration of the challenges, we propose an automatic vessel segmentation algorithm. For challenge 1, the algorithm's framework is divided into large and small vessel segmentation and has the following steps. Firstly, a vessel model embedded fuzzy c-means (VMEFCM) method with full consideration of challenge 2 is presented to obtain the initial vessel voxels. Then, considering challenge 3, a large vessel segmentation method based on the initial vessel voxels, similarity, and morphologic is proposed. Finally, a small vessel segmentation method based on spine is described. Extensive analysis is carried out on simulation datasets and 78 CTA datasets. The experimental results indicate that each step of the algorithm achieves the prospective results, and the proposed algorithm is effective and accurate with low computational cost. The dice, sensitivity, Jaccard coefficient, and precision rate were 93.7±2.8%, 93.7±2.8%, 88.2±4.8%, and 94.2±7.5% respectively.

Quantification of retinal artery-vein ratio for vascular disease diagnosis using spatial U-Net

The retinal <span>vascular morphological caliber changes reveal the signs of systemic health disorder and life threat diseases such as cardio and cerebral diseases. The quantitative vascular parameters like narrowed arteries, widened venules, reduced artery-vein ratio (AVR) have been associated with aforesaid disorders and diseases. Hence the quantitative biomarker AVR is important parameter in diagnosing variety of diseases. The accurate quantification of AVR be possible if and only if accurate classification of arteries and vein is done. In this paper, we proposed a deep learning based robust vessel segmentation and classification algorithm based on spatial <br /> U-Net and the accuracy of the algorithm is 97.8%. In the quantification process, this algorithm is applied on region of interest (RoI) of a fundus image and measured the AVR values using central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE). The experimentation is carried on the digital retinal images for vessel extraction (DRIVE) dataset. The outcome of this work is the AVR observed to be >0.4 in normal retinal case and AVR value <0.4 for unhealthy case.</span>

Hessian filter-assisted full diameter at half maximum (FDHM) segmentation and quantification method for optical-resolution photoacoustic microscopy.

Optical-resolution photoacoustic microscopy has been validated as an ideal tool for angiographic studies. Quantitative vascular analysis reveals critical information where vessel segmentation plays the key step. The comm-only used Hessian filter method suffers from varying accuracy due to the multi-kernel strategy. In this work, we developed a Hessian filter-assisted, adaptive thresholding vessel segmentation algorithm. Its performance is validated by a digital phantom and in vivo images which demonstrates a superior and consistent accuracy of 0.987 regardless of kernel selection. Subtle vessel change detection is further tested in two longitudinal studies on blood pressure agents. In the antihypotensive case, the proposed method detected a twice larger vasoconstriction over the Hessian filter method. In the antihypertensive case, the proposed method detected a vasodilation of 21.2%, while the Hessian filter method failed in change detection. The proposed algorithm may further push the limit of quantitative imaging on angiographic applications.

The RETA Benchmark for Retinal Vascular Tree Analysis

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

Capillaries segmentation of NIR-II images and its application in ischemic stroke

Fluorescence imaging in the second near-infrared window (NIR-II) offers μm resolution blood vessel information noninvasively, which is crucial for the diagnosis and surgery treatment of some blood vessel-related diseases. However, only a few blood vessel segmentation algorithms have been done for the NIR-II images so far. Here, we proposed a vessel segmentation algorithm that used multi-scale enhancement and fractional differential to enhance capillaries, and then segmented vessels based on the blood vessels' tubular characteristics. Experimental results showed that this method could effectively suppress the point and lump tissue noise influence during vascular segmentation. The accuracy of vessel identification by other algorithms dropped below 30%, while our algorithm still achieved an accuracy of around 50% in deep vessel segmentation experiments with the 6.5 mm Intralipid. So it had the advantage of accurately detecting deep and dim blood capillaries. Meanwhile, the vascular density quantization algorithm had been successfully applied to the mice's ischemic stroke evaluations for the first time. In addition, this algorithm can provide the quantified vessel features under physiological or pathological conditions, which could be used to accurately evaluate the stroke drugs' therapeutic effect in the future.

MC-DMD: A data-driven method for blood vessel enhancement in retinal images using morphological closing and dynamic mode decomposition

Detection of blood vessels in retinal images is an important step in any computer-aided pathological system for the early screening and detection of eye-related ailments such as retinal detachment, diabetic retinopathy, and macular degeneration. Propriety of the vessel enhancement process, as a pre-processing step, has been well established in medical corpus, for enhanced accuracy of vessel segmentation. Algorithms for retinal image extraction are still not qualitatively sufficient to be appropriate for diagnostics. This paper presents an effective and robust approach for retinal vessel enhancement that overcome prevailing deficiencies, using Morphological Closing based Dynamic Mode Decomposition (MC-DMD). The proffered algorithm exploits the power of mathematical morphology for the generation of input channel to the Dynamic Mode Decompostion (DMD) system, decomposing the vessel and non-vessel features of retinal images. We confirmed the effectiveness of the proposed enhancement method on three publicly available retinal image datasets: DRIVE, STARE and HRF, across nine existing vessel enhancement methods in terms of Receiver Operating Characteristic (ROC) curve and Area Under the ROC Curve (AUC). In addition, segmentation on final vessel map is performed and validated by enhanced performance in comparison with eight conventional vessel segmentation algorithms in terms of Sensitivity (SEN), Specificity (SP), Accuracy (ACC).

A Metaphorical Interpretation on Vessel Segmentation Algorithms by using Local Connected Fractal Dimension

ABSTRACT Retinal vessel segmentation is a prime utensil in ophthalmology. From the last two decades, a massive number of studies have been carried out in the creation and development of automated vessel segmentation. A benefit of this procedure has been recognised in various retinal oddities. A keen mathematical tool that anticipates the degree of difficulty of disease is the fractal dimension. Typically, the fractal dimension depends on the complexity of the objects. In the case of vessel segmentation, we placed filters and contrast adjustments for obtaining a better configuration of the original images. As well as, precise visualisation, identification, prevention medication and surgery planning for particular diseases can be done in an effective manner by considering this segmentation procedure. This will enhance the feature of the skeletonized/segmented structure of the retinal vessels. Hence, focusing on the segmentation method is more important. As a consequence, determining an elite strategy for vessel segmentation remains a challenging task. Thus, this article mainly focuses on the segmentation procedures in four ways and it uses the local connected fractal dimension (for short, ). Also, a precise method for segmenting the source images has been fixed in such a way that manipulating the fractal dimension of the output images occurred in those four processes. Ultimately, it results in the best technique by focusing on the variation of the fractal dimension of the raw pictures, which yields that the U-Net segmentation is more effective.

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively.

Automated lung vessel segmentation reveals blood vessel volume redistribution in viral pneumonia

PurposeIt is known from histology studies that lung vessels are affected in viral pneumonia. However, their diagnostic potential as a chest CT imaging parameter has only rarely been exploited. The purpose of this study is to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset. MethodsIn total, 509 non-enhanced chest CTs (NECTs) and 563 CT pulmonary angiograms (CTPAs) were included. Sub-groups were patients with healthy lungs (group_NORM, n = 634) and those RT-PCR-positive for Influenza A/B (group_INF, n = 159) and SARS-CoV-2 (group_COV, n = 279). A lung vessel segmentation algorithm (LVSA) based on traditional image processing was developed, validated with a point-of-interest approach, and applied to a large clinical dataset. Total blood vessel volume in lung (TBV) and the blood vessel volume percentage (BV%) of three blood vessel size types were calculated and compared between groups: small (BV5%, cross-sectional area < 5 mm2), medium (BV5-10%, 5–10 mm2) and large (BV10%, >10 mm2). ResultsSensitivity of the LVSA was 84.6% (95 %CI: 73.9–95.3) for NECTs and 92.8% (95 %CI: 90.8–94.7) for CTPAs. In viral pneumonia, besides an increased TBV, the main finding was a significantly decreased BV5% in group_COV (n = 14%) and group_INF (n = 15%) compared to group_NORM (n = 18%) [p < 0.001]. At the same time, BV10% was increased (group_COV n = 15% and group_INF n = 14% vs. group_NORM n = 11%; p < 0.001). ConclusionIn COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. Automated LSVA allows researchers and clinicians to derive imaging parameters for large amounts of CTs. This can enhance the understanding of vascular changes, particularly in infectious lung diseases.

Design and Evaluation of a Handheld Robotic Device for Peripheral Catheterization.

Medical robots provide enhanced dexterity, vision, and safety for a broad range of procedures. In this article, we present a handheld, robotic device capable of performing peripheral catheter insertions with high accuracy and repeatability. The device utilizes a combination of ultrasound imaging, miniaturized robotics, and machine learning to safely and efficiently introduce a catheter sheath into a peripheral blood vessel. Here, we present the mechanical design and experimental validation of the device, known as VeniBot. Additionally, we present results on our ultrasound deep learning algorithm for vessel segmentation, and performance on tissue-mimicking phantom models that simulate difficult peripheral catheter placement. Overall, the device achieved first-attempt success rates of 97 ± 4% for vessel punctures and 89 ± 7% for sheath cannulations on the tissue mimicking models (n = 240). The results from these studies demonstrate the viability of a handheld device for performing semi-automated peripheral catheterization. In the future, the use of this device has the potential to improve clinical workflow and reduce patient discomfort by assuring a safe and efficient procedure.

Performance Comparison of Most Recently Proposed Evolutionary, Swarm Intelligence, and Physics-Based Metaheuristic Algorithms for Retinal Vessel Segmentation

Biomedical image analysis based on metaheuristic algorithms is one of the most important research areas encountered in recent years. Due to the low contrast differences between the diseased areas and the image background in high-contrast biomedical images, effective methods are required to diagnose diseases with high accuracy. To overcome the difficulties encountered in this field, metaheuristic approaches may offer effective solutions due to their advantages such as the ability of converging to the global optimum, higher convergence rate, and having few control parameters. In this work, Jellyfish Search (JS), Marine Predators (MPA), Tunicate Swarm (TSA), Mayfly Optimization (MA), Chimp Optimization (ChOA), Slime Mould Optimization (SMA), Archimedes Optimization (AOA), and Equilibrium Optimizer (EO) algorithms, which are the most recently proposed metaheuristic algorithms in the literature, have been improved as clustering based in order to achieve vessel segmentation with high precision. Also, a detailed performance comparison of these algorithms has been realized for the rate of convergences, error values reached, CPU time, standard deviation, sensitivity, specificity, accuracy, F-score, and Wilcoxon rank sum-test. In order to present the compatibility of the results obtained with the literature, the performances of these novel algorithms have also been compared to that of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Differential Evolution (DE) algorithms. The simulation results represent that each algorithm produces similar convergence and error performance. Also, it can be emphasized from the statistical analyses that the stability and robustness of each metaheuristic approach are quite adequate in separating the vessel pixels and the background pixels of a retinal image. In general, this paper proves that although having fewer number of control parameters, the JS, MPA, TSA, MA, ChOA, SMA, AOA, and EO algorithms produce similar but a bit better results in terms of image segmentation when compared to PSO, GWO, and DE algorithms.

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation.

Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

A modified Coye algorithm for retinal vessel segmentation

A modified Coye algorithm for retinal vessel segmentation

Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network.

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

Sexual dimorphism in the cerebrovascular network: Brain MRI shows lower arterial density in women.

Accumulating evidence suggests that there is a sexual dimorphism in brain health, with women exhibiting greater disability following strokes of comparable size and having a higher prevalence of cognitive impairment later in life. Despite the critical implication of the cerebrovascular architecture in brain perfusion and brain health, it remains unclear whether structural differences in vessel density exist across the sexes. In this study, we used high-density MRI imaging to characterize the intracerebral arterial and venous density of 28 (14 women) sex-matched healthy young volunteers in vivo. Using an in-house vessel segmentation algorithm, we quantified and compared these vascular features across the cortical and subcortical deep gray matter, white matter, and periventricular white matter. We found that, on average, women have reduced intracerebral arterial density in comparison to men (F 2.34 ± 0.48%, M 2.67 ± 0.39%; p<.05). This difference was most pronounced in the subcortical deep gray matter (F 1.78 ± 0.53%, M 2.38 ± 0.82%; p<.05) and periventricular white matter (F 0.68 ± 0.15%, M 1.14 ± 0.33%; p<.0005), indicating a potential sex-specific vulnerability to hypoperfusion in areas critical to core cerebral functions. In contrast, venous density did not exhibit a significant difference between sexes. While this research remains exploratory, it raises important pathophysiological considerations for brain health, adverse cerebrovascular events, and dementia across the sexes. Our findings also highlight the need to take into account sex differences when investigating cerebral characteristics in humans.

Fundus Vessel Segmentation Algorithm Based on Multi-feature Fusion Neural Network

Fundus Vessel Segmentation Algorithm Based on Multi-feature Fusion Neural Network

Improved retinal vessel segmentation using the enhanced pre-processing method for high resolution fundus images

Background: By diagnosing using fundus images, ophthalmologists can possibly detect symptoms of retinal diseases such as diabetic retinopathy, age-related macular degeneration, and retinal detachment. A number of studies have also found some links between fundus image analysis data and other underlying systemic diseases such as cardiovascular diseases, including hypertension and kidney dysfunction. Now that imaging technology is advancing further, more fundus cameras are currently equipped with the capability to produce high resolution fundus images. One of the public databases for high-resolution fundus images called High-Resolution Fundus (HRF) is consistently used for validating vessel segmentation algorithms. However, it is noticed that the segmentation outputs from the HRF database normally include noisy pixels near the upper and lower edges of the image. In this study, we propose an enhanced method of pre-processing the images so that these noisy pixels can be eliminated, and thus the overall segmentation performance can be increased. Without eliminating the noisy pixels, the visual segmentation output shows a large number of false positive pixels near the top and bottom edges. Methods: The proposed method involves adding additional padding to the image before the segmentation procedure is applied. In this study, the Bar-Combination Of Shifted FIlter REsponses (B-COSFIRE) filter is used for retinal vessel segmentation. Results: Qualitative assessment of the segmentation results when using the proposed method showed improvement in terms of noisy pixel removal from near the edges. Quantitatively, the additional padding step improves all considered metrics for vessel segmentation, namely Sensitivity (73.76%), Specificity (97.53%), and Matthew’s Correlation Coefficient (MCC) value (71.57%) for the HRF database. Conclusions: Findings from this study indicate improvement in the overall segmentation performance when using the proposed double-padding method of pre-processing the fundus image prior to segmentation. In the future, more databases with various resolutions and modalities can be included for further validation.

Hybrid UNet Architecture based on Residual Learning of Fundus Images for Retinal Vessel Segmentation

This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.