Vessel Detection

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A Comparative Study of Different Blood Vessel Detection on Retinal Images

A Comparative Study of Different Blood Vessel Detection on Retinal Images

Blood vessel detection is vital for the analysis and treatment of dissimilar diseases for example obesity, glaucoma, hypertension and diabetic retinopathy. Diabetic retinopathy is a diabetes complication that affects the eyes [1–5]. The statistic of this disease is increased in community health problem and it also causes loss of sight. In all cases, accurate recognition of retinal blood vessel is vital. Manual analysis is regularly accomplished through analyzing the images from a patient, as not all images display signs of diabetic retinopathy [6–8]. Manual analysis surges time and leads to an incorrect diagnostic decision for ophthalmologists. An automatic segmentation of the vasculature might save workload of the ophthalmologists plus could contribute to portray the discovered injuries [9 –10]. Moreover, blood vessels are used as benchmarks for registration of retinal images of the same patient that had been collected from different sources [11–15]. In addition, the segmentation of the vascular tree appears to be the best suitable demonstration of the image registration applications caused by this explanation which are mapping the entire retina [15–18].

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Water flow based vessel detection in retinal images

Water flow based vessel detection in retinal images

From equation (1) and (2), the flow velocity is inversely proportional to the resistance of water. In a physical model, the flow resistance is decided by the water, the flow channel and temperature etc. Since this is a physical analogy which offers great freedom in selection of parameter definitions, we can assign high resistance values for unwanted image attributes and low values to preferred ones. For instance, in iris vessel detection, if the vessels have relatively low intensity, we can define the resistance to be proportional to the intensity of the pixel. If we couple the resistance with the edge information, the process will become adaptive. That is, when the edge response is strong, resistance would be large and so the flow velocity would be weakened. According to equation (3), the movement decision will now be dominated by the force acting during the exterior movement. Thereby, even if the driving force set by users is too “strong”, the resistance would lower its influence at edge positions and the problem in balloon models [1], where strong driving forces may overwhelm “weak” edges, can be suppressed. We first write an equation of velocity by (1) and (2):

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Frangi’s Vessel Detection Approach for Coronary Angiogram Segmentation

Frangi’s Vessel Detection Approach for Coronary Angiogram Segmentation

The proposed Frangi’s vessel detection approach for coronary angiogram segmentation is able to provide an accurate segmentation of vascular tree. It effectively suppresses noise and can extract small and distant vessels. Noise Adaptive Fuzzy Switching Median filter avoids blurring of output by processing only noisy pixels. The adaptive behaviour of the filter enables to expand the filtering window size based on local noise density. Switching behaviour speeds up the filtering process and at the same time preserving image details by selecting only noise pixels. Fuzzy reasoning of the filter helps the system to produce an accurate correction term when restoring noise pixels.

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A novel method for blood vessel detection from retinal images

A novel method for blood vessel detection from retinal images

As concerns the thin vessels ignored in large vessel extraction, they can be regarded as the lines with width within 3 pixels. So some basic line detectors are used to identify the orientation of thin vessels, shown in Fig.2(a). We consider 8 line detectors of fixed length l passing through each residual pixel (x, y) at different 8 angles (22.5× of angular resolution) [7]. And the mean and standard deviation of gray level is evaluated along each line. As shown in Fig.2(b), the mean and standard deviation of gray level along the line aligned within the vessel is minimum for almost invariable gray level. And this direction is marked by D1(x, y). The line with largest mean and standard deviation is found and corresponding direction is marked by D2(x, y). Now, we denote the mean and standard deviation of gray level along Di(x, y), with M gi (x, y) and SD gi (x, y), where i = 1 or 2. According to the orien-

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Lesions and Blood Vessel Detection in Eye Retinal Image

Lesions and Blood Vessel Detection in Eye Retinal Image

Abstract— Medical image analysis, applied to clinical diagnosis in ophthalmology is currently drawing intense interest of scientists and physicians. It enables the physician to measure important structures in an image, compare sequential images, aggregate images similar in content and finally obtain automated diagnosis from images. This involves many challenging steps like image processing, segmentation, classification, registration, recognition of objects from arbitrary viewpoints and inferencing. Information about blood vessels in the eye can be used in grading disease severity or as a part of automated diagnosis of disease with ocular manifestations. Systemic or local ocular diseases, causes some measurable abnormalities in diameter, colour and tortusity of the blood vessels in the retina. Disease like diabetes, as it progresses, generates new blood vessels (Neovascularization) in the retina causing loss of vision. This project work addresses issues in the development of automatic system for the analysis of retinal angiographic images, providing focus on the segmentation of the blood vessels and lesion detection. Kirsch Template Matching Algorithm is proposed for detecting the blood vessels in the retinal images and an effective approach to detect lesions in color retinal images. The proposed method uses two dimensional Kirsch Template Matching, which detect the blood vessel as the whole not only the edges and do the noise filtering in a single step and shows small vessels, capillaries to produce complete vessel map there by increasing the diagnostic ease of the ophthalmologist. The lesion detection algorithm, automatically take care of the non-uniform illumination using a power law transformation and classifies the lesion like regions in the retina image.

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Comparison of Morphological and Edge detection,  Line Operator based Retinal Blood Vessel Detection Algorithm

Comparison of Morphological and Edge detection, Line Operator based Retinal Blood Vessel Detection Algorithm

Abstract— Diabetic Retinopathy has turned out to be the most commonly occurring disease in patients with uncontrolled level of diabetes. So, it has become necessary to diagnose it at preliminary stage. Blood Vessels and optic disc are normal features of any retinal image. They are very helpful in detecting abnormal features of diabetic retinopathy. In detection of non-proliferative diabetic retinopathy (NPDR), sometimes, the abnormal features look like normal features. Hence, to avoid false positives, these features are removed before processing. Also, in the analysis of proliferative diabetic retinopathy (PDR), for neo-vascularization detection, the blood vessels are studied. So, after the blood vessels have been segmented, the features can be extracted and then the existence of neo-vascularization can be found out. There are various methods for blood vessel segmentation. This paper reviews two different methods for blood vessels segmentation: morphological and edge detection, line operator based methods. The algorithms were implemented on a set of images from different standard databases, ‘diaretdb’ and ‘High Resolution Fundus Images’; and non-standard database collected from ‘Bankers Retina Clinic and Laser Centre, Ahmedabad’.

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Robust and Efficient Segmentation of Blood Vessel in Retinal Images using Gray-Level Textures Features and Fuzzy SVM

Robust and Efficient Segmentation of Blood Vessel in Retinal Images using Gray-Level Textures Features and Fuzzy SVM

Proposed Method for Vessel classification This paper proposes a new supervised approach for blood vessel detection based on a NN for pixel classification. The essential feature vector is computed from preprocessed retinal images in the neighborhood of the pixel under consideration. The following process stages may be identified: 1) original fundus image pre- processing for gray-level homogenization and blood vessel enhancement, 2) feature extraction for pixel numerical representation, 3) application of a classifier to label the pixel as vessel or nonvessel, and 4) post-processing for filling pixel gaps in detected blood vessels and removing falsely- detected secluded vessel pixels. Input images are monochrome and obtained by extracting the green band from original RGB retinal images. The green channel provides the best vessel-background contrast of the RGB-representation, while the red channel is the brightest color channel and has low contrast, and the blue one offers poor dynamic range. Thus, blood containing elements in the retinal layer (such as vessels) are best represented and reach higher contrast in the green channel 11 .

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OPTIMIZED RETINAL BLOOD VESSEL SEGMENTATION TECHNIQUE FOR DETECTION OF DIABETIC RETINOPATHY

OPTIMIZED RETINAL BLOOD VESSEL SEGMENTATION TECHNIQUE FOR DETECTION OF DIABETIC RETINOPATHY

In automated retinopathy analysis system an important clinical role is played by blood vessel detection and segmentation technique. There is need of expertise and time to manually segment an image [15]. An image is divided into essential regions using various methods. The local or abrupt changes are considered for segmenting image using edge detector. The retinal analysis will become efficient using computational intelligence. In our paper, we have used MATLAB graphical user interface (GUI) tool to segment a blood vessels and then extracted the features using Principle component analysis (PCA). In paper [16], authors have used DRIVE database and achieves accuracy of 94.61% with 73.38% of sensitivity as compared to existing techniques of blood vessel segmentation. The image can be detected using various existing techniques which identify an image and then locate sharp discontinuities in it. The discontinuities are nothing it is just abrupt changes in pixel density that characterized boundaries [17]. In case of noisy images it is difficult to perform edge detection because there are a large frequency content which is available in both edges and noises. There are number of edge detection techniques available such as Sobel, Robert, Canny, Prewitt and Laplacian of Gaussian (LoG).

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Detection of Diabetic Retinopathy by Iterative Vessel Segmentation from Fundus Images

Detection of Diabetic Retinopathy by Iterative Vessel Segmentation from Fundus Images

Many automatic blood vessel detection algorithms have been projected in decades of study. A blood vessel chart is the basis of many applications. Many diseases such as diabetic retinopathy, arteriosclerosis and hypertension, are linked with irregularities of blood vascular. By observing the variation in diameter, position and tortuosities of blood vessels, strict diseases may be predicted in the early time and thus raise the prospect of a cure. Many techniques are preferred for segmentation of blood vessels, which can be separated into the following major categories: supervised and unsupervised methods.

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Comparative study of edge detection algorithm: vessel wall elasticity 
		measurement for deep vein thrombosis diagnosis

Comparative study of edge detection algorithm: vessel wall elasticity measurement for deep vein thrombosis diagnosis

In this paper, a comparative study of different edge detection methods had been done to determine the vessel wall elasticity for early diagnosis of the Deep Vein Thrombosis condition. Currently, in most research found that the measurement of the vessel detection conducted solely on the raw image obtains from the ultrasound. Thus, the precision of the measurement could be controvertible from time to time. As a matter of fact, the image consists of its individual characteristics or properties that cannot be verified distinctly. Therefore, various methods of edge detection techniques had been applied to the B-mode ultrasound image. There are several edge detection techniques available for pre-processing in computer vision. Though, Canny, Sobel and Roberts are some of the most applied methods. This paper compares each of the methods by the evaluation of the Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) of the output image. The evaluations are using Matlab software, the algorithms applied on the B-mode image of 10 subjects that had been volunteering for the purpose of the study. Both the MSE and the PSNR were in numeric values, that includes the vessel wall elasticity measurement of the popliteal vein, therefore, the performance of the algorithm is determined.

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An Inference of current techniques in retinal hemorrhage detection

An Inference of current techniques in retinal hemorrhage detection

Akhilesh Sharma et. al. [3] in their paper on dynamic thresholding technique for detection of hemorrhages in retinal images took ten samples of fundus images from the pub- lically available DIARETDB1 database. The green channel is extracted from RGB in order to extract the hemorrhage features with more accuracy and finally to gray channel with intensity range 0-255. The salt and pepper noise was removed using 3 x3 median filter and edges were preserved. The image was inverted to brighten the blood vessel and hemorrhage from background. For further contrast enhancement CLAHE algorithm was utilized.

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VSTP: vessel spatio temporal contact pattern detection based on MapReduce

VSTP: vessel spatio temporal contact pattern detection based on MapReduce

Due to lack of the coverage of 3G/4G network, satellite communication which costs excessively is the main approach used in ocean to provide network service. Ocean mobile delay tolerant network (OMDTN) can provide low-cost data transmission service in the network by utilizing the contact chances of moving vessels. Spatio-temporal contact pattern is one of the key metrics to improve the efficiency of the routing algorithm in OMDTN. Some researches have been carried out on human handheld device and vehicular ad hoc networks (VANETs). However, the vessel’s trajectory data is distributed and stored disorderly, which makes traditional contact pattern detection algorithm cannot be directly applied. In this paper, we design a parallel algorithm named VSTP based on MapReduce to detect spatio-temporal contact pattern from trajectories of over 2000 vessels. Studying the vessels’ trajectories and the contact records, we observe that the vessels’ contact pattern including inter-contact time distribution and contact times distribution is in sharp contrast to the study on human handheld device and VANETs. Our results can provide the guidelines for the design of data routing protocols on OMDTN and give a new solution to overcome the difficulty of ocean network coverage.

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Mental Stress Detection Based on Stereo Vision Fusion Measurement

Mental Stress Detection Based on Stereo Vision Fusion Measurement

This paper aims to present a novel methodology for real time monitoring of Internal Emotion State (Mental Stress). The method does not require any contact as contact measurement tend to effect emotions and burden physiologically. We have found out that user stress is correlated with the increased blood flow in three facial areas of sympathetic importance which is periorbital, supraorbital and maxillary. This increased blood flow dissipates convective heat which can be monitored through thermal imaging. In the stress experiment conducted, blood vessel is also detected via thermal imaging for several subjects in real time. Thermal infrared and visible cameras are being used in the stimulus experiment. Sample of several faces are also taken in real time in our experimental setup to measure the effectiveness of our method. Almost 98% of correct measurement of ROI and temperature was detected. The results of temperature before and after stress stimulus experiment are also compared and show promising results.

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Improved Detection of Anterior Circulation Occlusions: The “Delayed Vessel Sign” on Multiphase CT Angiography

Improved Detection of Anterior Circulation Occlusions: The “Delayed Vessel Sign” on Multiphase CT Angiography

Images were obtained with a 128-section multidetector CT scanner (Somatom Definition AS ⫹ ; Siemens, Erlangen, Ger- many) at 120 kV, 90 mAs (effective) with a collimation of 128 ⫻ 0.6 mm. NCCT was performed from the skull base to the vertex. The first phase of the MPCTA was performed from the aortic arch to the skull vertex with the second and third phases performed from the skull base to the vertex. The first phase is timed to occur during the peak arterial phase with bolus mon- itoring of the descending thoracic aorta and is commenced after a 6-second delay. Eighty milliliters of intravenous con- trast (iopamidol, Niopam; Bracco Imaging, Milan, Italy; 370 mg iodine per milliliter) is injected at a rate of 5 mL/s followed FIG 1. Multiphase CTA and follow-up MR imaging of an 83-year-old woman presenting with acute right upper limb weakness and dysphasia. A, Axial MIP of the first phase demonstrates subtle paucity of vessels in the distribution of the left MCA compared with the right side. B, Axial MIP of the second phase demonstrates the delayed vessel sign (long arrow). There is delayed enhancement of the distal left MCA via pial collateral vessels (short arrows). This vessel is not seen on the first phase due to the presence of an M2 vessel occlusion. C, Axial MIP of the third phase also demonstrates the “delayed” left MCA vessel (long arrow). D, DWI b ⫽ 1000 image 2 weeks postpresentation demonstrates a recent infarct (arrow) in the same left MCA territory.

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Review of Vessel Extraction Methods and Hemorrhage Detection Methods For Diabetic Retinopathy

Review of Vessel Extraction Methods and Hemorrhage Detection Methods For Diabetic Retinopathy

Neural networks are used to simulate biological learning and widely used in pattern recognition and are basically a classification approach. The network is a collection of elementary processors (nodes). Each node takes a number of inputs, performs elementary computations and generates a single output. Each node is assigned a weight and the output is a function of weighted sum of the inputs. These weights are learnt through training and then used in the recognition. The back-propagation algorithm is a widely used learning algorithm [10]. One problem associated with learning is that learning depends on the training data set and the size of the training data set also affects the learning process. When new training data set has been added to the training process should run again in each time and thus a supervised learning technique learning process. Medical imaging mainly used as neural networks trained systems specifications Lee is using the goal where the system image and medical images are trained with a set of a classification method blocks. Nekovei and the Sun blood vessels using a back propagation network to determine the system to remove vascular structure: pixels labeled as non-vessel or vessel to do the aim of ex-directly the system feature. Without pixels applies neural networks. Shiffman et al. combine an automated neural network-based segmentation approach with manual editing to extract vessel sections from image volumes. They aim to facilitate the visualization of vasculature by editing the target sections in the volume prior to 3D reconstruction. The first step of the method involves an automatic segmentation of an entire image sequence which produces a set of labeled image sections. The next step is to edit the resulting images view and user. In the last step, the user edited based on the remaining section and label recognition to remove sections of the final image. Vibhaj medical image that a neural network not attractive builds your network received during the training of the ability to use nonlinear classification boundaries.

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Comparative study of edge detection algorithm: vessel wall elasticity measurement for deep vein thrombosis diagnosis

Comparative study of edge detection algorithm: vessel wall elasticity measurement for deep vein thrombosis diagnosis

Doppler is useful in diagnosing DVT at risk patient and provide a noninvasive method of investigation. In addition, it is also contributes in evaluating the site, extent and stage of thrombus. As proven, ultrasound imaging had been widely implemented as it is noninvasive method and its low cost that has allowed the use of this technique for more clinical studies. The B-mode image obtain had been analysed to determine the condition of the patient. In this study, the purpose in analysing the image using the edge detection is to measure the vessel wall elasticity of the popliteal vein. Moreover, several methods of edge detection had been applied to compare the vessel wall elasticity measurement of the popliteal vein. Generally, edge is the plot of high intensity pixel and its immediate neighbourhood in the images. Therefore, the shape of the image object decided by its edges. The edges are used in the image analysis to discover its region boundaries. Edge detection is a significant process in computer vision and image processing, thus, it is essential to consider the edge detection operators (Jie Yanga, 2008, P. P. Acharjya, 2012). Rapid change in the pixel intensity of the image is the common meaning of edge. It includes the important features and critical characteristics of an image. Those rapid changes in the pixel intensity, detected by using the first and second order derivatives. The edge is the boundary between the object and its background. The edge detector aimed to avoid the false edges and correctly detecting the true edges ( Pushpajit A. Khaire, 2012).

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Detection of Failure mode of Pressure Vessel Agitator due to variable load

Detection of Failure mode of Pressure Vessel Agitator due to variable load

Detailing of complete procedure of problem which has to be sorted out, let us take a case study of Emulsion Polymer Vertical Reactor Pressure vessel, with a complete design & drawing based on the same being discussed below. The materials to be used in pressure vessels must be selected from Code-approved material specifications. This requirement is normally not a problem since a large catalogue of tables listing acceptable materials is available. Factors that need to beconsidered in picking a suitable table are:

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Detection of blood vessel Segmentation in retinal images using Adaptive filters

Detection of blood vessel Segmentation in retinal images using Adaptive filters

The proposed system allows processing of retinal image for identification of true blood vessels and cross overs in blood vessels. The method is based on vessel tracking technique. The key idea of the method is that first a set of cross over points (center of vessel cross sections) is extracted. Then, by using graph tracer algorithm optimal forest in retina is found and crossovers are identified. The major contribution of this work is to identify correct cross over point in blood vessel. The proposed method can be divided into 3 consecutive stages referred to as pre processing, identify crossovers, and identify blood vessel. Experiment results are analysed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision .The system proposed does not provide any information on leakage of blood in retina. Future work deals with identification of damaged blood vessels in retina.

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Generation of Vessel Tree Structure from Retinal Fundus Image

Generation of Vessel Tree Structure from Retinal Fundus Image

A class of popular approaches for vessel segmentation is based filtering methods [2], which work by maximizing response as ship-structures. Mathematical morphology [3] is another approach by applying morphological operators. Trace-based methods [1] to map out the global network of blood vessels after edge detection by tracing the centerlines of vessels. Such methods are highly dependent on the result of edge detection. Machine learning based methods [1, 4-5] have also been proposed and can be divided into two groups: supervised methods [1, 5] and unsupervised methods [4]. Supervised methods exploit some labeling information before deciding whether a pixel belongs to a ship or not, while unsupervised methods do vessel segmentation without any prior knowledge of labeling. In previous transform based edge detection and Segmentation like simple and global thresholding algorithm was used and there were some drawbacks in the existing methods like

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Ridgeness Based Vessel Feature Detection for Endoscopic Images

Ridgeness Based Vessel Feature Detection for Endoscopic Images

Vesselness-Based Circle Test (VBCT) and Vesselness-Based Branching Segment Detection (VBSD) to extract the two types of vessel features based on Frangi vesselness[4]. The goal of this method is to develop a feature detector based on Frangi‟s vesselness that will be able to robustly and repeatedly produce feature points and curve segments across different viewpoints and different lighting conditions in real time. Two types of blood vessel features are defined: branching points and branching segments[2]. Bifurcations and crossing points are defined as branching points. Branching segment is defined as a blood vessel segment which has both of its two endpoints as branching points[1]. Compared with feature points, branching segments are more powerful and distinctive visual cues whose locations, tangential directions and curvatures can all be exploited. The methods proposed in this paper to detect branching points and branching segments are named as Vesselness Based Circle Test and Vesselness Based Branching Segment Detection respectively.

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