scholarly journals Image processing-based model for tortuosity measurement of retinal blood vessels

2019 ◽  
Vol 2 (3) ◽  
pp. 43-67
Author(s):  
Sanyukta Chetia ◽  
SR Nirmala
Keyword(s):  
Image Processing ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Hypertensive Retinopathy ◽  
Retinal Diseases ◽  
Retinal Blood Vessel ◽  
Vessel Segment ◽  
Retinal Blood Vessels ◽  
Vessel Tortuosity

Purpose: The study of retinal blood vessel morphology is of great importance in retinal image analysis. The retinal blood vessels have a number of distinct features such as width, diameter, tortuosity, etc. In this paper, a method is proposed to measure the tortuosity of retinal blood vessels obtained from retinal fundus images. Tortuosity is a situation in which blood vessels become tortuous, that is, curved or non-smooth. It is one of the earliest changes that occur in blood vessels in some retinal diseases. Hence, its detection at an early stage can prevent the progression of retinal diseases such as diabetic retinopathy, hypertensive retinopathy, retinopathy of prematurity, etc. The present study focuses on the measurement of retinal blood vessel tortuosity for the analysis of hypertensive retinopathy. Hypertensive retinopathy is a condition in which the retinal vessels undergo changes and become tortuous due to long term high blood pressure. Early recognition of hypertensive retinopathy signs remains an important step in determining the target-organ damage and risk assessment of hypertensive patients. Hence, this paper attempts to estimate tortuosity using image-processing techniques that have been tested on artery and vein segments of retinal images. Design: Image processing-based model designed to measure blood vessel tortuosity. Methods: In this paper, a novel image processing-based model is proposed for tortuosity measurement. This parameter will be helpful for analyzing hypertensive retinopathy. To test the eff ectiveness of the system in determining tortuosity, the method is first applied on a set of synthetically generated blood vessels. Then, the method is repeated on blood vessel (both artery and vein) segments extracted from retinal images collected from publicly available databases and on images collected from a local eye hospital. The blood vessel segment images that are used in the method are binary images where blood vessels are represented by white pixels (foreground), while black pixels represent the background. Vessels are then classified into normal, moderately tortuous, and severely tortuous by following the analysis performed on the images in the Retinal Vessel Tortuosity Data Set (RET-TORT) obtained from BioIm Lab, Laboratory of Biomedical Imaging (Padova, Italy). This database consists of 30 artery segments and 30 vein segments, which were manually ordered on the basis of increasing tortuosity by Dr. S. Piermarocchi, a retinal specialist belonging to the Department of Ophthalmology of the University of Padova (Italy). The artery and vein segments with the fewest number of turns are given a low tortuosity ranking, while those with the greatest number of turns are given a high tortuosity ranking by the expert. Based on this concept, our proposed method defines retinal image segments as normal when they present the fewest number of twists/turns, moderately tortuous when they present more twists/turns than normal but fewer than severely tortuous vessels, and severely tortuous when they present a greater number of twists/turns than moderately tortuous vessels. On implementing our image processing-based method on binary blood vessel segment images that are represented by white pixels, it is found that the vessel pixel (white pixels) count increases with increasing vessel tortuosity. That is, for normal blood vessels, the white pixel count is less compared to moderately tortuous and severely tortuous vessels. It should be noted that the images obtained from the different databases and from the local hospital for this experiment are not hypertensive retinopathy images. Instead, they are mostly normal eye images and very few of them show tortuous blood vessels. Results: The results of the synthetically generated vessel segment images from the baseline for the evaluation of retinal blood vessel tortuosity. The proposed method is then applied on the retinal vessel segments that are obtained from the DRIVE and HRF databases, respectively. Finally, to evaluate the capability of the proposed method in determining the tortuosity level of the blood vessels, the method is tested with a standard tortuous database, namely, the RET-TORT database. The results are then compared with the tortuosity level mentioned in the database. It was found that our method is able to classify blood vessel images as normal, moderately tortuous, and severely tortuous, with results closely matching the clinical ordering provided by the expert in the RET-TORT database. Subjective evaluation was also performed by research scholars and postgraduate students to cross-validate the results. Conclusion: The close correlation between the tortuosity evaluation using the proposed method and the clinical ordering of retinal vessels as provided by the retinal specialist in the RET-TORT database shows that, although simple, this method can evaluate the tortuosity of vessel segments effectively.  

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

scholarly journals Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

2021 ◽  
Author(s):  
Sanjeewani NA ◽  
arun kumar yadav ◽  
Mohd Akbar ◽  
mohit kumar ◽  
Divakar Yadav
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Vessel Segmentation ◽  
Great Success ◽  
Retinal Blood Vessel ◽  
Data Set ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

Retinal Blood Vessel Segmentation with Improved Convolutional Neural Networks

2019 ◽  
Vol 9 (6) ◽  
pp. 1112-1118 ◽  
Author(s):  
Dan Yang ◽  
Mengcheng Ren ◽  
Bin Xu
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Data Augmentation ◽  
Training Image ◽  
Retinal Blood Vessel ◽  
Image Preprocessing ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Deep Cnn ◽  
Model Training

Retinal blood vessel feature is one of crucial biomarkers for ophthalmologic and cardiovascular diseases, efficiency image segmentation technologies will help doctors diagnose these related diseases. We propose an improved deep CNN model to segment retinal blood vessels. Our method includes three steps: Data augmentation, Image preprocessing methods and Model training. The data augmentation uses the rotation and image mirroring to make the training image better generalization. The CLAHE algorithm is used for image preprocessing, which can reduce the image noise and enhance tiny retinal blood vessels features. Finally, we used a deep CNN model combined with U-Net and Dense-Net structure to train retinal blood vessel image. The result of proposed model was tested on public available dataset DRIVE, achieving an average accuracy 0.951, specificity 0.973, sensitivity 0.797 and the average AUC is 0.885. The results show its potential for clinical application.

Retinal Blood Vessel Caliber Estimation for Optical Coherence Tomography Angiography Images Based on 3D Superellipsoid Modeling

2019 ◽  
Vol 19 (02) ◽  
pp. 1950011 ◽  
Author(s):  
Myounghee Han ◽  
Yongjoo Kim ◽  
Jang Ryul Park ◽  
Benjamin J. Vakoc ◽  
Wang-Yuhl Oh ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Blood Vessel ◽  
Optical Coherence Tomography Angiography ◽  
3D Visualization ◽  
Retinal Vessel ◽  
Flow Speed ◽  
Retinal Diseases ◽  
Optical Coherence ◽  
Retinal Blood Vessel

Changes of retinal blood vessel calibers may reflect various retinal diseases and even several non-retinal diseases. We propose a new method to estimate retinal vessel calibers from 3D optical coherence tomography angiography (OCTA) images based on 3D modeling using superellipsoids. Taking advantage of 3D visualization of the retinal tissue microstructures in vivo provided by OCTA, our method can detect retinal blood vessels precisely, estimate their calibers reliably, and show the relative flow speed visually.

A New Deeply Convolutional Neural Network Architecture for Retinal Blood Vessel Segmentation

2020 ◽  
pp. 2157001
Author(s):  
Mali Mohammedhasan ◽  
Harun Uğuz
Keyword(s):  
Neural Network ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Blood Vessels ◽  
Network Architecture ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Low Level ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

This paper proposes an incoming Deep Convolutional Neural Network (CNN) architecture for segmenting retinal blood vessels automatically from fundus images. Automatic segmentation performs a substantial role in computer-aided diagnosis of retinal diseases; it is of considerable significance as eye diseases as well as some other systemic diseases give rise to perceivable pathologic changes. Retinal blood vessel segmentation is challenging because of the excessive changes in the morphology of the vessels on a noisy background. Previous deep learning-based supervised methods suffer from the insufficient use of low-level features which is advantageous in semantic segmentation tasks. The proposed architecture makes use of both high-level features and low-level features to segment retinal blood vessels. The major contribution of the proposed architecture concentrates on two important factors; the first in its supplying of extremely modularized network architecture of aggregated residual connections which enable us to copy the learned layers from the shallower model and developing additional layers to identity mapping. The second is to improve the utilization of computing resources within the network. This is achieved through a skillfully crafted design that allows for increased depth and width of the network while maintaining the stability of its computational budget. Experimental results show the effectiveness of using aggregated residual connections in segmenting retinal vessels more accurately and clearly. Compared to the best existing methods, the proposed method outperformed other existing methods in different measures, comprised less false positives at fine vessels, and caressed more clear lines with sufficient details like the human annotator.

scholarly journals An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jianqing Gao ◽  
Guannan Chen ◽  
Wenru Lin
Keyword(s):  
Random Walk ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Morphological Characteristics ◽  
Maximum Intensity Projection ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Structured Analysis

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

Nitric oxide dilates rat retinal blood vessels by cyclooxygenase-dependent mechanisms

2009 ◽  
Vol 297 (4) ◽  
pp. R968-R977 ◽  
Author(s):  
Naoto Ogawa ◽  
Asami Mori ◽  
Masami Hasebe ◽  
Maya Hoshino ◽  
Maki Saito ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Guanylyl Cyclase ◽  
Soluble Guanylyl Cyclase ◽  
Retinal Blood Vessel ◽  
No Donors ◽  
Retinal Blood Vessels ◽  
Retinal Arterioles ◽  
Cox 1

It has been suggested that nitric oxide (NO) stimulates the cyclooxygenase (COX)-dependent mechanisms in the ocular vasculature; however, the importance of the pathway in regulating retinal circulation in vivo remains to be elucidated. Therefore, we investigated the role of COX-dependent mechanisms in NO-induced vasodilation of retinal blood vessels in thiobutabarbital-anesthetized rats with and without neuronal blockade (tetrodotoxin treatment). Fundus images were captured with a digital camera that was equipped with a special objective lens. The retinal vascular response was assessed by measuring changes in diameter of the retinal blood vessel. The localization of COX and soluble guanylyl cyclase in rat retina was examined using immunohistochemistry. The NO donors (sodium nitroprusside and NOR3) increased the diameter of the retinal blood vessels but decreased systemic blood pressure in a dose-dependent manner. Treatment of rats with indomethacin, a nonselective COX inhibitor, or SC-560, a selective COX-1 inhibitor, markedly attenuated the vasodilation of retinal arterioles, but not the depressor response, to the NO donors. However, both the vascular responses to NO donors were unaffected by the selective COX-2 inhibitors NS-398 and nimesulide. Indomethacin did not change the retinal vascular and depressor responses to hydralazine, 8-(4-chlorophenylthio)-guanosine-3′, 5′-cyclic monophosphate (a membrane-permeable cGMP analog) and 8-(4-chlorophenylthio)-adenosine-3′, 5′-cyclic monophosphate (a membrane-permeable cAMP analog). Treatment with SQ 22536, an adenylyl cyclase inhibitor, but not ODQ, a soluble guanylyl cyclase inhibitor, significantly attenuated the NOR3-induced vasodilation of retinal arterioles. The COX-1 immunoreactivity was found in retinal blood vessels. The retinal blood vessel was faintly stained for soluble guanylyl cyclase, although the apparent immunoreactivities on mesenteric and choroidal blood vessels were observed. These results suggest that NO exerts a substantial part of its dilatory effect via a mechanism that involves COX-1-dependent pathway in rat retinal vasculature.

scholarly journals Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yuliang Ma ◽  
Zhenbin Zhu ◽  
Zhekang Dong ◽  
Tao Shen ◽  
Mingxu Sun ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Current Problem ◽  
Matched Filter ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Vessel Enhancement ◽  
Better Than

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

scholarly journals Retinal blood vessel segmentation based on heuristic image analysis

2019 ◽  
Vol 16 (1) ◽  
pp. 227-245 ◽  
Author(s):  
Maja Braovic ◽  
Darko Stipanicev ◽  
Ljiljana Seric
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Vessel Segmentation ◽  
Optic Disk ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

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