Vessel Extraction Research Articles

Polynomial modeling of retinal vessels for tortuosity measurement

Tortuosity is one of the micro vascular change that is observed in many retinopathies. Its early detection can prevent the progression of various retinopathies to a critical stage at which a person may become blind. Here, we propose a novel method for the measurement of tortuosity by polynomial modeling of retinal vessels for the analysis of hypertensive retinopathy. The proposed method is tested on a set of 30 arteries and 30 veins vessel images collected from the Retinal Vessel Tortuosity Dataset (RET-TORT). Also, 90 vessel segments from Digital Retinal Images for Vessel Extraction (DRIVE) and 149 vessel segments from High Resolution Fundus (HRF) databases are used for tortuosity evaluation. The experimental results demonstrate that the order of the polynomial increases with the increase in the tortuosity of the blood vessels. Hence, the order of the polynomial can be used as a parameter to classify vessels as non-tortuous and tortuous. The results of the method are also evaluated subjectively and the inter-rater agreement analysis is made by using Fleiss Kappa index. The Spearman's rank order correlation coefficient is used to analyze the correlation between the ranking provided by the expert in the RET-TORT database and the ranking obtained by the proposed method. The results demonstrate that this method is capable of evaluating the tortuosity and classify vessel segments into non-tortuous or tortuous effectively.

A Discriminative Colour-to-Grayscale Representation for Retinal Vessel Segmentation

Structural changes in retinal blood vessels are important indicators for many ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. An automated vessel segmentation process almost always begins with acquired color fundus image, containing three layers of images (red, green and blue), and then quickly converted them to a single grayscale image. However, grayscale conversion is not unique and more than one grayscale representation can be obtained for a given color image. For the many currently existing automated vessel extraction methods, the green channel of the RGB color fundus image is routinely used as an input grey-scale representation to a pipeline of the segmentation process for the reason that it provides the best contrast among all three channels, namely red, green and blue. We hypothesize that vessel information contained in dropped channels, red and blue, will add to result in improved segmentation performance. In this paper, we propose a linear combination framework to utilize all three channels of the color fundus image based on their importance level rather than completely discarding some channels. We devised a Principal Component Analysis (PCA) method that provides appropriate weights for each color channel to realize a more discriminative grey-scale representation. The added information made available through PCA-based grayscale representation results in improved performance for Modautomated vessel segmentation algorithms. The performance of the framework is analyzed on two publically available databases (DRIVE, STARE) of fundus images quantifying improvements in all three aspects called accuracy, sensitivity, and specificity.

Correction to: Retinal vessel extraction using dynamic multi-scale matched filtering and dynamic threshold processing based on histogram fitting

In the affiliation of the first author, Shandong University was omitted by mistake.

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.

Abstract P6-01-02: Machine learning-based structural analysis and oxygen saturation measurement of tumor-associated vessels in breast cancer using a photoacoustic tomography system

Abstract Introduction Breast cancer induces angiogenesis, one of the primary factors responsible for tumor progression. Therefore, the ability to visualize angiogenesis at a higher resolution is crucial. Photoacoustic tomography is a noninvasive method of visualizing angiogenesis involving light absorption and ultrasonic wave emission. If the irradiation light wavelength is adjusted for hemoglobin, vascular imaging is possible. Furthermore, using two wavelengths for oxidized and reduced hemoglobin, “S-factor,” can be calculated, which nearly corresponds to oxygen saturation. Therefore, photoacoustic imaging allows the assessment of breast lesions from vascular structural and functional viewpoints. Objectives This study aimed to demonstrate the possible utility of photoacoustic tomography for clinical application focusing on the morphologic features and oxygen saturation status of breast tumor-related vessels. Methods For the morphological analysis, we applied a machine learning-based method for automatic vessel extraction, and for the functional analysis we evaluated hemoglobin oxygen saturation calculating signals obtained at two wavelengths. In our system, a 3D ultrasound image was simultaneously acquired as a volume image of a tumor, which helped analyze the positional relationship between the vessels and the tumor. Results On morphological analysis, the fine structure of tumor-related vessels was rendered in high resolution. In our system, the blood vessels branched toward the tumor 2-3 more times more frequently than observed on contrast-enhanced MRI, illustrating a finer level of blood vessels near the tumor on our system than on MRI. Next, we analyzed the six morphologic features of vessels (radius, volume, curvature, contraction, maximum angle and vessel branch number) that are associated with the pathologic condition in neuroscience. We determined that the feature distribution of vessels located close to the tumor differed from that located away from the tumor. For example, vessels near the tumor had higher curvature, which means they are more tortuous than healthy vessels. The difference in the distribution of all six features was statistically significant on the Kolmogorov-Smirnov test. On functional analysis, S-factor measurement of the healthy human breast demonstrated clearly demarcated arteries and veins. The S-factor of any artery was nearly 100%, while that of the veins inside the breast cancer tended to be a little higher (approximately 5%) compared to that in the healthy part. This tendency of veins was not recognized in benign tumors. This could show arteriovenous shunt in cancer microenvironment. We found low saturation signals emerging in the tumor tissue following bevacizumab-containing chemotherapy, indicating the possibility that our system reveals microenvironment changes. Discussion If our system can identify the structure or oxygen saturation characteristics unique to tumor-associated vasculature, it could contribute to the improved accuracy of breast cancer diagnosis and allow the observation of tumor vessel normalization because of the drug treatment. An earlier grasp of the therapeutic effect could lead to the provision of individualized medicine. Citation Format: Matsumoto Y, Gu L, Bise R, Asao Y, Sekiguchi H, Yoshikawa A, Ishii T, Takada M, Kataoka M, Sakurai T, Yagi T, Sato I, Togashi K, Shiina T, Toi M. Machine learning-based structural analysis and oxygen saturation measurement of tumor-associated vessels in breast cancer using a photoacoustic tomography system [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-01-02.

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.

Dispersive liquid-liquid microextraction of chloroacetic acids from water samples using a syringe-like glass extraction vessel

A syringe-like glass extraction vessel was used for dispersive liquid-liquid microextraction using low-density solvents for the extraction of chloroacetic acids (CAAs, monochloroacetic acid, dichloroacetic acid, and trichloroacetic acid) from water samples. The CAAs were derivatized using n-octanol (extraction solvent) in the presence of sulfuric acid during and after extraction. Gas chromatography–electron capture detection was applied for the detection of n-octyl derivatize of CAAs. The effective parameters on the extraction efficiency, including the volume of extraction solvent, type, and volume of dispersive solvent, sample acidity, salt addition and extraction time were investigated. Also, the time and temperature of the derivatization reaction were optimized. The limits of detection and quantification were in the range of 0.02–6 and 0.05–10 μg L−1, respectively. Tap, river and swimming pool water samples were analyzed by the method. The relative recoveries were from 90 to 119%, with the RSDs being in the range of 2.1–4.7% (n = 3). The satisfactory results revealed the suitability and reliability of the method in determining the CAAs in environmental water samples.

A novel diagnostic information based framework for super-resolution of retinal fundus images

Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images. Clinical information of the selected zone is captured using the Shannon entropy, the contrast sensitivity index (CSI), the multi-resolution (MR) intra-band energy and the MR inter-band eigen features. The support vector machine (SVM) classifier is used to decide the clinical significance of the zone. Highly accurate learning based SR method or the bicubic interpolation is applied to the selected zone based on the classification output. The method is tested on the Standard Diabetic Retinopathy Database Calibration level 1 (DIARETDB1) and the Digital Retinal Images for Vessel Extraction (DRIVE) databases. Classification accuracy of 85.22% and 85.77% is achieved for the DIARETDB1 and DRIVE databases respectively. The SR performance of the algorithm is quantified in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and computational time. The proposed diagnostic information based SR achieves computational time efficiency without compromising with the high resolution (HR) reconstruction accuracy of the fundus image zones.

Automated techniques for blood vessels segmentation through fundus retinal images: A review.

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.

Vessel segmentation using centerline constrained level set method

Vascular related diseases have become one of the most common diseases with high mortality, high morbidity and high medical risk in the world. Level set is a kind of active contour model, and can be used to extract vessel structures. However, the applications of level set methods in vessel segmentation suffer from two problems. The first problem is the error caused by the false inclusion of some non-vessel structures. The second one is the sensitivity of the level set evolution to the initialization condition. In this paper, we propose an algorithm termed Centerline constrained level set (CC-LS) for vessel segmentation which utilizes centerline information to improve the evolution of level set. Using centerline information as the initial level set condition leads to improved evolution efficiency and extraction accuracy. Additionally, a new centerline modulated velocity term can be used in the level set evolution function to avoid the wrong inclusion of non-vessel structures. Performance of the proposed CC-LS algorithm is well validated using both 2D and 3D coronary images in different types. The proposed method is able to attain satisfactory results on both 2D and 3D coronary data.

Automatic Retinal Lesions Detection of Diabetic Retinopathy Using Curvelet Based Enhancement

Diabetic Retinopathy is the eye disease caused in patient suffering from diabetes. Once the sugar level becomes high, it deteriorates retina leading to loss of vision. Therefore, lesion detection gained importance in treating diabetes. The main aim of this work is to propose an automatic detection of lesions using four steps such as pre-processing, optic disc removal and vessel extraction, post processing and candidate lesion detection. It starts with optic disc removal and vessel extraction followed by curvelet based enhancement to isolate dark lesions from retina. The optical disc is necessitous for this system. To segment microaneurysms from the retinal image we proposed morphological filtering techniques and transformation. Curvelet based enhancement is used to improve the parameters such as specificity, accuracy and sensitivity in the proposed system. Performance analysis is carried out for specificity, accuracy and sensitivity of the proposed method. Additionally red lesion defect and glaucoma defect is also analyzed in this work.

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.

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.

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR)... | Find, read and cite all the research you need on Tech Science Press

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.

Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression

Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy.

Retraction: Vesselness-guided Active Contour: A coronary vessel extraction method

Retraction: Vesselness-guided Active Contour: A coronary vessel extraction method

A Fundus Retinal Vessels Segmentation Scheme Based on the Improved Deep Learning U-Net Model

Retinal vascular segmentation is very important for diagnosing fundus diseases. However, the existing methods of retinal vascular segmentation have some problems, such as low microvascular segmentation and wrong segmentation of pathological information. To solve these problems, we developed a fundus retinal vessels segmentation based on the improved deep learning U-Net model. First, enhance the retinal images. Second, add the residual module in the process of designing the network structure, which solved the problem of the traditional deep learning U-Net model is not deep enough. By using the improved deep learning U-Net model, it can connect the output of the convolutional layer with the output of the deconvolution layer to avoid low-level information sharing, and solved the problem of performance degradation of deep convolutional neural networks in residual networks under extreme depth conditions. By verifying on the DRIVE (digital retinal images for vessel extraction) dataset, the segmentation accuracy, sensitivity, and specificity of the proposed method are 96.50%, 93.1%, and 98.63% respectively.

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.

Tensor Voting Based Vascular Bifurcation Detection in CT Images of Lung

The accurate detection of vascular bifurcations is not helpful for pulmonary vascular disease diagnosis, but vital in (Computed Tomography) CT image analysis and processing of lung. We propose a tensor voting based method for vascular bifurcation detection in CT image of lung, which a vessel enhancement method is firstly proceeding to initially extract vessel structure, on which we perform ball voting with the pixels.