Detection Of Blood Vessels Research Articles

Ocular circulation change in optic disc melanocytoma – a case report and a review of the literature

BackgroundOcular circulation in optic disc melanocytoma (ODM) has not yet been well evaluated. We quantitatively evaluated longitudinal changes in the morphology and circulation hemodynamics of the disc and macula using optical coherence tomography angiography (OCTA) and laser speckle flowgraphy (LSFG) in a patient with optic disc melanocytoma.Case presentationA 50-year-old woman was referred to our hospital due to a dark pigmented tumor over the superior optic disc area of the left eye noted on physical examination. At the first visit, the patient’s best-corrected visual acuity (BCVA) was 20/20 in both eyes, and the intraocular pressure (IOP) was 17 and 18 mmHg in the left and right eyes, respectively. Fluorescein angiography (FA) showed blockage of fluorescence in the topography of the lesion, and indocyanine green angiography (ICGA) showed hypofluorescence at all times. On LSFG, a low mean blur rate (MBR) was noted in the optic disc all area (MBRa) and tissue (MBRt) compared to the contralateral eye at the first visit and at the 3-month follow-up. A relatively low MBR was also detected in the macular area of the affected eye and the tumor itself. OCTA detected blood vessel networks in the deep retinal layer of the tumor. The visual field showed no specific defects. During follow-up, there was no tumor enlargement or vision decrease.ConclusionsWe found that a lower MBR of the disc and macula area was noted on LSFG in this patient with optic disc melanocytoma, and it was continually observed at the 3-month follow-up. Although blood vessel networks in the deep retinal layer of the tumor were detected by OCTA, vascular compromise in the surrounding disc area and macula was found. Therefore, these results further increase our knowledge about the role that circulation impairment plays in the pathogenesis of the disease while vision is unaffected.

CD47 and CD68 expression in breast cancer is associated with tumor-infiltrating lymphocytes, blood vessel invasion, detection mode, and prognosis.

CD47 expressed on tumor cells binds to signal regulatory protein alpha on macrophages, initiating inhibition of phagocytosis. We investigated the relationships between tumor expression of CD47 and CD68 macrophage content, subsets of tumor-infiltrating lymphocytes (TILs), and vascular invasion in breast cancer. A population-based series of 282 cases (200 screen detected and 82 interval patients) from the Norwegian Breast Cancer Screening Program was examined. Immunohistochemical staining for CD47 and CD68 was evaluated on tissue microarray (TMA) slides. For CD47 evaluation, a staining index was used. CD68 tumor-associated macrophages were counted and dichotomized. TIL subsets (CD45, CD3, CD4, CD8, and FOXP3) were counted and dichotomized using immunohistochemistry on TMA slides. Vascular invasion (both lymphatic and blood vessel) was determined on whole tissue slides. High CD47 tumor cell expression or high counts of CD68 macrophages were significantly associated with elevated levels of all TIL subsets (p < 0.02), CD163 macrophages (p < 0.001), blood vessel invasion (CD31 positive) (p < 0.01), and high tumor cell Ki67 (p < 0.004). High CD47 expression was associated with ER negativity (p < 0.001), HER2 positive status (p=0.03), and interval-detected tumors (p=0.03). Combined high expression of CD47-CD68 was associated with a shorter recurrence-free survival (RFS) by multivariate analysis (hazard ratio [HR]: 2.37, p=0.018), adjusting for tumor diameter, histologic grade, lymph node status, and molecular subtype. Patients with luminal A tumors showed a shorter RFS for CD47-CD68 high cases by multivariate assessment (HR: 5.73, p=0.004). This study demonstrates an association of concurrent high CD47 tumor cell expression and high CD68 macrophage counts with various TIL subsets, blood vessel invasion (CD31 positive), other aggressive tumor features, and interval-presenting breast cancer. Our findings suggest a link between CD47, tumor immune response, and blood vessel invasion (CD31 positive). Combined high expression of CD47-CD68 was an independent prognostic factor associated with poor prognosis in all cases, as well as in the luminal A category.

Supervised learning software model for the diagnosis of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of eye diseases and vision loss for diabetic affected people. Due to the damage of retinal blood vessels, diabetic patients often suffer from DR. So the retinal blood vessel segmentation plays a crucial role in the diagnosis of DR. We can prevent vision loss or blindness problems if the diagnosis happens during the early stages. Early diagnosis and initial investigation would help lower the risk of vision loss by 50%. This article exploits the supervised classification approach to detect blood vessels by applying features such as grey level and invariant moments. The image pre-processing and blood vessel segmentation are the two essential steps are used in this study, along with the proposed classification framework using neural network models. Two publicly available retinal image datasets, such as DRIVE and STARE, are used to assess the proposed supervised classification framework. The suggested supervised classification methodology in this study attains the average retinal blood vessel segmentation accuracy of 93.94% in the DRIVE dataset and 95.00% in the STARE dataset.

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques

Automatic Method for Blood Vessel Diameter Measurement on Color Retinal Images

Automatic detection of retinal blood vessels and measurement of blood vessel diameter has become very useful in medical applications, including the diagnosis and monitoring of vascular therapy. This paper present a new method for the detection of retinal blood vessels from fundus images. In addition, it accurately determines the blood vessels diameter. The proposed system consists of several stages starting from the detection of a complete blood vessel network (retinal images), a segmentation process, an extraction of blood vessel centerlines, and a new approach for the measurement of the blood vessel diameters utilizing the circle diameter approach. The application of the proposed method is based on three publicly available databases (DRIVE, STARE, CHASE_DB and HRF), which has been conducted and compared to recently reported measurement techniques. Numerical results demonstrate the efficacy of the proposed method achieving a success rate of 100% with 2.2454×10−4 as mean and 4.5833×10−5 as standard deviation differences. This could be harnessed in the future blood vessel measurement techniques.

Photoacoustic non-human primate brain imaging

Non-human primates (NHPs) play significant roles in brain research because of the physiological similarities between humans and NHPs. Current functional brain imaging techniques for NHPs are difficult to meet the required high spatiotemporal resolution for behavior active NHPs. In this presentation, we review our initial study on evaluating the feasibility of photoacoustic imaging (PAI) for monitoring hemodynamic responses in the NHP brains due to various functional activities. PAI systems, including array-based photoacoustic computed tomography (PACT) system and photoacoustic microscopy (PAM), were used to detect hemodynamic responses in the NHP brains through a cranial window. The NHPs were subjected to different functional stimulations or actively performing behavioral tasks. Strong increases in PA signal amplitude during functional activities can be detected with single-blood-vessel spatial resolution in the cortex and subcortical regions of the NHP brains. Realtime-temporal-resolution hemodynamic response can also be obtained for the detected blood vessels in the brain. Our results demonstrate that PAI can reliably detect brain activations in NHPs with high spatiotemporal resolution.

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.

Assessing Skin Healing and Angiogenesis of Deep Burns in Vivo Using Two-Photon Microscopy in Mice

Prevalent methods for monitoring burn injuries and testing drug efficacies rely on fixed tissue sections. However, this may leave out cellular details in the living state. In vivo assessments of burn healing has been long sought after and are of scientific and clinical interest. Nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), collagen, and melanin are endogenous fluorescent molecules and their signals can be captured by two-photon microscopy (TPM), therefore providing information on epidermal histological features and collagen growth in real-time. In addition, TMP imaging on exogenous fluorescent substances provides a basis for detecting blood vessels. In this work, two-photon microscopy was used to capture the exogenous fluorescent substances and endogenous fluorescent molecules at different times to assess and track burn healing in vivo. Combining TPM imaging and morphological characteristics, proliferation and differentiation of the keratinocytes in different layers of skin, collagen contents, and angiogenesis were identified and quantified. The TPM monitoring method provides an effective tool to systemically evaluate skin healing of deep burns in vivo.

AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning

Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR and prevent vision loss caused by it. Researchers have proposed several systems to detect abnormalities in retinal images in the past few years. However, Diabetic Retinopathy automatic detection methods have traditionally been based on hand-crafted feature extraction from the retinal images and using a classifier to obtain the final classification. DNN (Deep neural networks) have made several changes in the previous few years to assist overcome the problem mentioned above. We suggested a two-stage novel approach for automated DR classification in this research. Due to the low fraction of positive instances in the asymmetric Optic Disk (OD) and blood vessels (BV) detection system, preprocessing and data augmentation techniques are used to enhance the image quality and quantity. The first step uses two independent U-Net models for OD (optic disc) and BV (blood vessel) segmentation. In the second stage, the symmetric hybrid CNN-SVD model was created after preprocessing to extract and choose the most discriminant features following OD and BV extraction using Inception-V3 based on transfer learning, and detects DR by recognizing retinal biomarkers such as MA (microaneurysms), HM (hemorrhages), and exudates (EX). On EyePACS-1, Messidor-2, and DIARETDB0, the proposed methodology demonstrated state-of-the-art performance, with an average accuracy of 97.92%, 94.59%, and 93.52%, respectively. Extensive testing and comparisons with baseline approaches indicate the efficacy of the suggested methodology.

Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile.

It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis.

Deteksi Pembuluh Darah pada Citra Fundus Retina Menggunakan Gabungan Metode Segementasi Pembuluh Darah Lebar dan Tipis

Organs in the human body provide a lot of knowledge about human body health. One of them is the organ in the human eye, more specifically the retina.Furthermoreeven some accute diseases can be detected through the retina image. One of the diseases that can be detected through retinal images is diabetic retinopathy. This disease can be identified through segmentation to find abnormalities in the retinal blood vessels. These abnormalities include enlarged blood vessels, abnormal branching, and so on. Manual detection of blood vessels on the retina is less accurate, so a comperhensive program is required in order to segment the blood vessels in eye fundus images, more accurately. In this research, some method and algorithms are approached by combining the segmentation process for wide vessels and the segmentation process for thin vessels. There are three stages in this research. First, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and 2D Gabor Wavelet are applied as the preprocessing stage. Then, active contour and region growing methods are required as the processing to segment the thin and wide vessels in fundus retina image. And the last stage is postprocessing using the logical operator method or. The trial in this research uses color eye fundus images on the DRIVE dataset consisting of 20 retinal photos. By using this dataset, the average accuracy is 94.32%, sensitivity is 77.09% and specificity is 96.04% in 20 trials.

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 deep learning approach to detect blood vessels in basal cell carcinoma.

Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual annotation nor with computerized techniques. In this study, we automate the segmentation of telangiectasia in dermoscopic images with a deep learning U-Net approach. We apply a combination of image processing techniques and a deep learning-based U-Net approach to detect telangiectasia in digital basal cell carcinoma skin cancer images. We compare loss functions and optimize the performance by using a combination loss function to manage class imbalance of skin versus vessel pixels. We establish a baseline method for pixel-based telangiectasia detection in skin cancer lesion images. An analysis and comparison for human observer variability in annotation is also presented. Our approach yields Jaccard score within the variation of human observers as it addresses a new aspect of the rapidly evolving field of deep learning: automatic identification of cancer-specific structures. Further application of DL techniques to detect dermoscopic structures and handle noisy labels is warranted.

A high accuracy segmentation method for retinal blood vessel detection based on hybrid filters and an adaptive thresholding

A high accuracy segmentation method for retinal blood vessel detection based on hybrid filters and an adaptive thresholding

U-shaped Retinal Vessel Segmentation Based on Adaptive Aggregation of Feature Information.

Detection and analysis of retinal blood vessels contribute to the clinical diagnosis of many ophthalmic diseases. In this paper, aiming on achieving more accurate segmentation of retinal vessels and enhance the ability of the algorithm to identify microvessels, we propose a U-shaped network based on adaptive aggregation of feature information. The introduced feature selection module, which could strengthen feature transmission and selectively emphasize feature information. To effectively capture the characteristics of vessels at different scales, generate richer and denser context information, and DenseASPP is embedded at the bottom of the network. Meanwhile, we propose an adaptive aggregation module to aggregate the semantic information in each layer of the encoder part and transmit it to subsequent layers, which is beneficial to the spatial reconstruction of retinal vessels. A joint loss function is also introduced to facilitate network training. The proposed network is evaluated on three public datasets. The sensitivity, accuracy, and area under curve(AUC) are 83.48%/83.16/85.86, 95.67%/96.67%/96.52%, and 98.11%/98.69%/98.60% on DRIVE, STARE and CHASE_DB1, respectively. In order to achieve more accurate retinal blood vessel segmentation and improve the ability of the algorithm to identify microvessels. We propose a U-shaped network based on adaptive aggregation of feature information. The introduction of the adaptive aggregation module aggregates the semantic information of each level of the encoder part, which improves the robustness of the model to segment blood vessels.

Novel Technique for Segmentation and Identification of Diabetic Retinopathy in Retinal Images Using GMM and Compare Accuracy with SVM

The main objective of this work is to detect blood vessel segmentation of diabetic retinopathy in low-resolution retinal images using two machine learning algorithms and perform accuracy comparison. Materials and methods: Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) are implemented to detect and segment the blood vessel in retinal images dataset with 40 samples (20 per group). Results: From the MATLAB simulation result, GMM classified the image with better accuracy of 95% compared to SVM classifier accuracy 89%, attained a significant accuracy ratio (p=0.020) in statistical analysis. Conclusion: GMM provides better accuracy compared to SVM classifiers in segmentation and detection.

Fovea localization by blood vessel vector in abnormal fundus images

In human eyes, macula is responsible for sharp central vision with fovea in the center. The location of fovea becomes an important landmark in diagnosing the retinal diseases. As macula doesn’t have the clear boundary and obvious shape, deep learning techniques to locate the fovea often fail in complicated lesions and insufficient training samples, and the unsupervised method is incapable for illumination variations. In this paper, a new unsupervised fovea localization method using the retinal raphe and region searching is proposed, and the blood vessel vector (BVV) model is developed. After detecting blood vessels and OD by U-net and probability bubbles, the BVVs are conceived and the retinal raphe is obtained by summating all the BVVs, then the fovea is estimated through the local region searching. Compared with the parabola model, the BVV model does not involve the coordinate transformation and reduces the complexity to the linear time cost O(N). Two other unsupervised techniques the parabola model and intensity searching and five supervised techniques cGAN, U-net, DRNet, MedTnet and EANet are included and compared. The global feature of retinal vessels is utilized which makes the proposed method more robust to the lesions than the other localization methods. The experiments on public datasets Kaggle, MESSIDOR and IDRiD validate the effectiveness of the proposed method by the student’s t-test, and our method obtains the least average Euclidean distance to the groundtruth on Kaggle and almost least on Base 33 of MESSIDOR.

Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology

Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator’s experience. In this study, we present “blood vessel detection—BVD”, an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.

Comparison of the vitality tests used in the dental clinical practice and histological analysis of the dental pulp.

In dentistry, indirect diagnostic methods such as electrical sensibility testing and pulse oximetry are used to assess the status of the pulp. Our study aimed to determine the correlation between hemoglobin oxygen saturation and vascular volume density (Vvasc). We also wanted to examine an electrical sensibility test and the volume density of myelinated nerve fibers (Vnerv). Twenty-six intact permanent premolars were included in the study. For histological analysis, the pulp tissue was stained with hematoxylin-eosin and immunohistochemically for von Willebrand factor and S100 to detect blood vessels and myelinated nerve fibers, respectively. The stereological analysis was used to determine the Vvasc and Vnerv. Statistical analysis was done using the Pearson correlation test and Welch’s ANOVA test. Histological analysis showed that the pulp tissue was strongly vascularized and innervated. A significant positive correlation was found between Vvasc and hemoglobin oxygen saturation levels (p = 0.030). A significant negative correlation was found between Vnerv and the lowest electrical voltage that patient felt (p = 0.033). According to the maturity of the dental apex, teeth were divided into a group with open (n = 6, OA group) and closed apex (n = 20, CA group). We found that pulps in the CA group had higher Vnerv than the OA group (p = 0.037). In contrast, there were no significant differences in Vvasc of the pulp tissue (p = 0.059), oxygen saturation (p = 0.907), or electrical voltage (p = 0.113) between both groups. We can conclude that the measurement of pulse oximetry and electrical sensibility test reflects the morphology of healthy pulp tissue independently of the maturity of the dental apex.

Review on Diabetic Retinopathy Detection Using Molecular Segmentation In Convolution Neural Network

Abstract: People who suffer from diabetes are at risk of developing blindness since their pancreas doesn't produce enough insulin. Diabetic retinopathy is a condition in which a patient's vision gradually worsens as the disease advances. To better understand how diabetes affects the eye, retinal images captured with a fundal camera can be helpful. This study aims to detect blood vessels, identify hemorrhages, and classify different phases of diabetic retinopathy into normal, mild, and non-proliferative diabetic retinopathy (NPDR). When it comes to classifying the various stages of diabetes-related retinopathy, the retinal picture is used as a starting point. Using the contrast between the blood vessels and the surrounding background, the retinal vascular is segmented.. Density analysis and bounding box approaches were used to identify potential hemorrhage sites. According to blood vessel and hemorrhage area and perimeter, Random Forests technique was used to finally classify various phases of eye disease. Keywords: Retina, blood vessel, hemorrhages, classification, diabetic retinopathy.