Blood Vessel Extraction Research Articles

Diabetic Retinopathy Detection using Image Processing

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM

Diabetic Retinopathy (DR) occurs due to Type-II diabetes. It causes damages to the retinal blood vessels and reason for visual impairment. The predicted center is around the probability of variation in the estimation of retinal veins, and the crisp enrolls vessel development inside the retina. To witness the changes segmentation of retinal blood vessels has to be made. A framework to upgrade the quality of the segmentation results over morbid retinal images is proposed. This framework utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) for eliminating the background from the source image and enhances the foreground blood vessel pixels, Tandem Pulse Coupled Neural Network (TPCNN) model is endorsed for automatic feature vectors generation, and Deep Learning Based Support Vector Machine (DLBSVM) is proposed for classification and extraction of blood vessels. The DLBSVM parameters are fine-tuned via Firefly algorithm. The STARE, DRIVE, HRF, REVIEW, and DRIONS fundus image datasets are deliberated to assess the recommended techniques. The results render that the proposed technologies improve the segmentation with 80.61% Sensitivity, 99.54% Specificity, and 99.49% Accuracy.

Enhancement of Coronary Blood Vessels based on Frangi’s Vesselness Filter and Morphological Operations

Cardiovascular diseases (CVDs) are the global cause of deaths and therefore research in modern medical image processing aims to develop a medical tools to assist the clinicians in vessel extraction, artery detection and 3D reconstruction. Vessel extraction is an important and trivial step which depends extremely on enhancement method. Extraction of coronary artery blood vessels from 3 Dimension (3D) Coronary Computed Tomography Angiography (CCTA) images is a demanding research objective to strengthen the diagnosis and therapy of coronary artery illness. This paper presents a vessel enhancement method of coronary artery blood vessels using Frangi’s vesselness measure and morphological operators. In the first stage of the proposed work, Preprocessing is performed to consider only the heart region. Next Frangi’s vesselness measure is calculated for the 3D CCTA images. While calculating the Frangi’s vesselness measure, four different types of gradient operators are used for calculating the Hessian matrix viz., Sobel, Prewitt, central difference and intermediate difference operators. In the second stage, the vessels are enhanced by morphological operations based on top hat and bottom hat operations. These morphological operations help in further enhancing the blood vessels. The proposed methodology was applied on 12 3D CCTA dataset and evaluated using quality measures such as MSE, PSNR, SSIM and FSIM. The results obtained based on the four gradient operators are compared. The statistical test viz., one way ANOVA was carried out on the results. The proposed method using Prewitt operator is able to extract even small vessels and the results seem to be promising.

Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution

The appearance and structure of blood vessels in retinal fundus image is a fundamental part of diagnosing different issues related with such as diabetes and hypertension. The proposed blood vessel segmentation in fundus image using Clifford Algebra approach is divided into three steps. Image vectorization as a first step helps to convert the image space into Clifford space. Next step introduces Clifford matched filter as a proposed mask which works for retinal blood vessel extraction. The third and final step of this method is Clifford convolution operation with the help of Clifford convolution. This mask generates edge points along the boundaries of the blood vessels. The edge points are represented as a Grade-0 vector or scalar unit. Discrete edge points along the boundary of blood vessels are the edge pixels instead of continuous edges. The output of this method differs in the representation of vessel tree compare to other existing methods. The output image can be defined as the edge point set. This method achieves blood vessel segmentation accuracy of 94.88% and 92.95% on two publicly available datasets STARE and DRIVE respectively in less than 0.5 s per image. The proposed matched filter and the segmentation technique opens many windows of reliable and faster processing for further image processing steps on retinal fundus images.

Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation.

Retinal blood vessel extraction is considered to be the indispensable action for the diagnostic purpose of many retinal diseases. In this work, a parallel fully convolved neural network-based architecture is proposed for the retinal blood vessel segmentation. Also, the network performance improvement is studied by applying different levels of preprocessed images. The proposed method is experimented on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the Retina) which are the widely accepted public database for this research area. The proposed work attains high accuracy, sensitivity, and specificity of about 96.37%, 86.53%, and 98.18% respectively. Data independence is also proved by testing abnormal STARE images with DRIVE trained model. The proposed architecture shows better result in the vessel extraction irrespective of vessel thickness. The obtained results show that the proposed work outperforms most of the existing segmentation methodologies, and it can be implemented as the real time application tool since the entire work is carried out on CPU. The proposed work is executed with low-cost computation; at the same time, it takes less than 2s per image for vessel extraction.

The Application of Deep Learning in Vessel Extraction with Fundus Image

Fundus image is a very prevalent Biomedical data, so it is very important to choose a method to extract blood vessel from fundus images. Extracting vessel from the fundus image can be used for personal identity verification and assist doctor to diagnose diseases [1] such as diabetes. However, traditional extracted methods such as manually annotation area waste of time and energy. There is some another method [2] based on the image recognition, extracting the feature with the filter and segment with mathematical morphology method. But in the fact of the application, this method has low stability and accuracy. Traditional machine learning methods such as SVM [3] are also used for vessel extraction, but this method can only apply to dealing with one-dimensional data while the images showed up as matrix form. So, we have to stretch a matrix line by line or column by column into one-dimensional vectors [4-6]. However, by doing so, we will be face that some important information can’t be reflected because the position relation of rows and rows in the original matrix will be lost. Deep learning can process image data naturally and extract features automatically with convolution, and it is also popular for its good learning ability and high accuracy. U-net [7] is a convolutional network based on deep learning and widely used in biological image extraction, which constructs a U-shaped network from input image and output image. In this paper, building a U-Net convolutional network, which takes fundus image as input and vessel image as output, to extract vessels in fundus image. We established a training set with 20 fundus images and a test set with 20 fundus images. Finally, the accuracy rate of our U-net model on the training set can reach 99 percent, while the accuracy rate on the test set was stable above 99.5 percent.

A computer-aided diagnosis system for plus disease in retinopathy of prematurity with structure adaptive segmentation and vessel based features

Retinopathy of Prematurity (ROP) is a blinding disease affecting the retina of low birth-weight preterm infants. Accurate diagnosis of ROP is essential to identify treatment-requiring ROP, which would help to prevent childhood blindness. Plus disease, which characterizes abnormal twisting, widening and branching of the blood vessels, is a significant symptom of treatment requiring ROP. In this paper, we have developed and evaluated a computer-based analysis system for objective assessment of plus disease in ROP, which best mimics the clinical method of disease diagnosis by identifying unique vessel based features. The proposed system consists of an initial segmentation stage, which will efficiently extract blood vessels of varying width and length by utilizing structure adaptive filtering, connectivity analysis and image fusion. The paper proposes the usage of additional retinal features namely leaf node count and vessel density, to portray the abnormal growth and branching of the blood vessels and to complement the commonly used features namely tortuosity and width. The test results show a better classification of plus disease in terms of sensitivity (95%) and specificity (93%), emphasizing the superiority of the proposed segmentation algorithm and vessel-based features. An additional advantage of the proposed system is that the process of selection of relevant vessels for feature extraction is fully automated, which makes the system highly useful to the non-physician graders, owing to the unavailability of a sufficient number of ROP specialists.

Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods.

In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized. The selected combination of the different types of individually efficient features results in a rich local information with better discrimination for vessel and non-vessel pixels. The proposed method first extracts the thick and clear vessels in an unsupervised manner, and then, it extracts the thin vessels in a supervised way. The goal of the combination of the supervised and unsupervised methods is to deal with the problem of intra-class high variance of image features calculated from various vessel pixels. The proposed method is evaluated on three publicly available databases DRIVE, STARE and CHASE_DB1. The obtained results (DRIVE: Acc = 0.9531, AUC = 0.9752; STARE: Acc = 0.9691, AUC = 0.9853; CHASE_DB1: Acc = 0.9623, AUC = 0.9789) demonstrate the better performance of the proposed method compared to the state-of-the-art methods.

Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

A hybrid segmentation algorithm is proposed is this paper to extract the blood vessels from the fundus image of retina. Fundus camera captures the posterior surface of the eye and the captured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinal haemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysis of vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the field of ophthalmology. It is derived from the literature review that no unique segmentation algorithm is suitable for images of different eye-related diseases and the degradation of the vessels differ from patient to patient. If the blood vessels are extracted from the fundus images, it will make the diagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithm exclusively for the extraction of blood vessels from the fundus image. The proposed algorithm is hybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement (MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, the area-based morphological operator is applied to highlight the blood vessels. To validate the proposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus (HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments the blood vessels with more accuracy than the existing algorithms.

Extraction of Retinal Blood Vessels on Fundus Images by Kirsch's Template and Fuzzy C-Means.

Purpose:Accurate segmentation of retinal blood vessel is an important task in computer-aided diagnosis and surgery planning of diabetic retinopathy. Despite the high-resolution of photographs in fundus photography, the contrast between the blood vessels and the retinal background tends to be poor.Materials and Methods:In this proposed method, contrast-limited adaptive histogram equalization is used for noise cancellation and improving the local contrast of the image. By uniform distribution of gray values, it enhances the image and makes the hidden features more visible. The extraction of the retinal blood vessel depends on two levels of optimization. The first level is the extraction of blood vessels from the retinal image using Kirsch's templates. The second level is used to find the coarse vessels with the assistance of the unsupervised method of Fuzzy C-Means clustering. After segmentation, to remove the optic disc, the region-based active contour method is used. The proposed system is evaluated using DRIVE dataset with 40 images.Results:The performance of the proposed approach is comparable with state of the art techniques. The proposed technique outperforms the existing techniques by achieving an accuracy of 99.55%, sensitivity of 71.83%, and specificity of 99.86% in the experimental setup.Conclusion:The results show that this approach is a suitable alternative technique for the supervised method and it is support for similar fundus images dataset.

Optimized maximum principal curvatures based segmentation of blood vessels from retinal images

In retinal image of the human eye, extracting tree shaped retinal vasculature is an important feature which helps eye care specialists or ophthalmologists to pursue proper diagnostic procedures. In this paper, an approach named Optimized Maximum Principal Curvatures Based (OPCB) segmentation is been proposed for efficient extraction of blood vessels from retinal fundus images. This algorithm proceeds into two stages. Firstly, pre-processing on input retinal images is done by Particle Swarm Optimization (PSO) technique which is an automatic process for computing the global optimum pixels of the image in order to avoid working with all or random pixels. Later, these optimal pixels are made to undergo further processing with Gaussian Filter to remove the noisy pixels among them. Secondly, the post-processing is carried out in four steps: (i)Maximum Principal Curvatures (maximum-eigenvalues) of the second order derivative matrix (Hessian) quantity of the pre-processed PSO image are computed by using ‘Lambda Function’, which then does region growing of the tree-shaped blood vessels by convolving Maximum Principal Curvatures with the mathematical erosion structuring element of. (ii)After extraction of blood vessels, section-wise contrast enhancement is performed by using Adaptive Histogram Equalization that work on 8x8 tiles of image being segmented for smoothing artificially introduced boundaries if any, and also for eliminating over amplified noise. (iii) ISODATA (Iterative Self-Organizing Data Analysis Technique) thresholding is used to classify the image globally where the image’s foreground vascular structure is segmented from the background. (iv) A ‘morphologically opened’ operation is performed to prune falsely segmented isolated regions, to achieve very accurate segmentation. This proposed technique tested online available colored retinal images of STARE and DRIVE databases. As an outcome, the proposed approach achieves the superior segmentation accuracy of 96% which outperformed many empirically proven segmentation methods which were proposed in the past.

Central Retinal Artery Occlusion: The Identification and Segmentation of Retinal Blood Vessels

The Central Retinal Artery Occlusion is a retinal disorder in which blood flow through the major artery of retina gets blocked resulting in sudden vision loss without any pain in the affected eye. This blockage generally comes from a blood clot or cholesterol deposit in the blood vessel. The segmentation of retinal blood vessels and optical disc is the most vital and challenging task to investigate the rigorousness of the various retinal diseases such as branch retinal vein occlusion. There is lot of methods and algorithms are developed to address this issue. The proposed method enlightens the central retinal artery occluded retinal blood vessels identification and extraction based on simple image processing techniques.

Digital Diagnosis of Diabetic Retinopathy using Fundus Images

Diabetic retinopathy is a condition where the retina of the eye is affected due to diabetes. It eventually leads to blindness. It is a systemic disease which is an ocular manifestation of diabetes. It affects almost 80 percent of people with prolonged and acute diabetes for 20 years or more. Inspite of these intimidating statistics, advanced researchers propose that monitoring of eyes enables earlier detection of symptoms that can help reduce blinding of eyes by more than 90%. United States, records 12% of all new cases of blindness due to diabetic retinopathy. It is also the leading cause of blindness for people aged 20 to 64 years. Therefore serious efforts are being taken by engineers to develop efficient ways of detecting this diabetic retinopathy through image processing of fundus images. Automated Blood Vessel Extraction algorithms save time, protects patient’s vision and reduces unwanted medical costly treatments. This paper analyzes on the image of human eye captured from the fundus camera and proposes a methodology for detection of Diabetic Retinopathy using Image Enhancement.

A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images.

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy is integrated into the image matting model for blood vessel segmentation. Normally the matting models require a user specified trimap, which separates the input image into three regions: the foreground, background and unknown regions. However, creating a user specified trimap is laborious for vessel segmentation tasks. In this paper, we propose a method that first generates trimap automatically by utilizing region features of blood vessels, then applies a hierarchical image matting model to extract the vessel pixels from the unknown regions. The proposed method has low calculation time and outperforms many other state-of-art supervised and unsupervised methods. It achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE DB1, respectively.

Segmentation of retinal blood vessels from ophthalmologic Diabetic Retinopathy images

The most prominent ophthalmic cause of blindness is Diabetic Retinopathy (DR). This retinal disease is characterized by variation in diameter of the retinal blood vessel and the new blood vessel growth inside the retina. A system to enhance the quality of the segmentation result over the pathological retinal images has been proposed. The proposed method uses Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and Tandem Pulse Coupled Neural Network (TPCNN) model for automatic feature vectors generation then classification and extraction of the retinal blood vessels via Deep Learning Based Support Vector Machine (DLBSVM). The proposed approach is assessed over the standard public fundus image databases to evaluate the performance. The results render that these techniques improve the segmentation results with an average value of 74.45% sensitivity, 99.40% specificity, and 99.16% accuracy. The results evoke that the proposed method is a suitable alternative for supervised techniques.

BAT algorithm inspired retinal blood vessel segmentation

The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40-dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors’ proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm-based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.

A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends

The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular ailment, high blood pressure, arteriosclerosis, and age-related macular degeneration. The manual inspection of retinal vasculature is an extremely challenging and tedious task for medical experts due to the complex structure of an eye, tiny blood vessels, and variation in vessels width. Several automatic retinal vessels extraction techniques have been proposed in contemporary literature, which assist ophthalmologists in the timely identification of an eye disorders. However, due to the fast evolution of such techniques, a comprehensive survey is needed. This survey presents a comprehensive review of such techniques, strategies, and algorithms presented to date. The techniques are classified into logical groups based on the underlying methodology employed for retinal vessel extraction. The performance of existing techniques is reported on the publicly accessible datasets in term of various performance measures, providing a valuable comparison among the techniques. Thus, this survey presents a valuable resource for researchers working toward automatic extraction of retinal vessels.

Extraction of Blood Vessels in Retina

This article presents a novel method to extract retinal vascular tree automatically. The proposed method consists of four steps; smoothing image using low pass spatial filter to reduce spurious noise in the image; extracting candidate borders of the vessels based on a local window property; tracking process, starting with a candidate pixel and following in the optimum direction with monitoring the connectivity of the vessel twin border; constructing the whole tree of retinal blood vessels by connecting the vessel segments based on their spatial locations, widths and directions. The algorithm was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images.

An Intelligent Model for Blood Vessel Segmentation in Diagnosing DR Using CNN.

Diabetic retinopathy (DR) is an eye disease, which affects the people who are all having the diabetes for more than 10years. The ophthalmologist identifies when the dilated eye exam causes severe in any one of the following deviations in the retina: changes in blood vessels, leaking blood vessels, newly grown blood vessels, swelling of the macula, changes in the lens, and damages to the nerve tissue. It can eventually lead to vision loss. The early detection of DR prevents the cause of blindness. In this paper, we propose the retinal image segmentation and extraction of blood vessels by morphological processing, thresholding, edge detection, and adaptive histogram equalization. For the automatic diagnosis of DR from the fundus image, we also developed a network with the convolutional neural network architecture for accurately classifying its severity. By using high-end graphical processor unit (GPU), we trained this network on the publicly available dataset such as DRIVE, DIARETDB0, and DIARETDB1_v1, and the images collected from the Aravind Eye Hospital, Coimbatore, India. Our proposed CNN achieves a sensitivity of 98%, a specificity of 93%, and an accuracy of 96.9% containing a database of 854 images.

Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy

Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s Thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). The developed segmentation method accuracies are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma.