A Review Of Vessel Extraction Techniques And Algorithms Pdf

a review of vessel extraction techniques and algorithms pdf

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A Review of Vessel Extraction Techniques and Algorithms

We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research.

While we have mainly targeted the extraction of blood vessels, neurosvascular structure in particular, we have also reviewed some of the segmentation methods for the tubular objects that show similar characteristics to vessels.

We have divided vessel segmentation algorithms and techniques into six main categories: 1 pattern recognition techniques, 2 model-based approaches, 3 tracking-based approaches, 4 artificial intelligence-based approaches, 5 neural network-based approaches, and 6 miscellaneous tube-like object detection approaches.

Some of these categories are further divided into subcategories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, pre-processing, user interaction, and result type. Keywords: Vessel extraction, medical imaging, X-ray angiography XRA , magnetic resonance angiography MRA 1 Introduction Blood vessel delineation on medical images forms an essential step in solving several practical applications such as diagnosis of the vessels e.

Vessel segmentation algorithms are the key components of automated radiological diagnostic systems. Segmentation methods vary depending on the imaging modality, application domain, method being automatic or semi-automatic, and other specific factors.

There is no single segmentation method that can extract vasculature from every medical image modality. While some methods employ pure intensity-based pattern recognition techniques. Depending on the image quality and the general image artifacts such as noise, some segmentation methods may require image preprocessing prior to the segmentation algorithm [6, 7].

On the other hand, some methods apply post-processing to overcome the problems arising from over segmentation. We survey current vessel segmentation methods, covering both early and recent literature related to vessel segmentation algorithms and techniques. We introduce each segmentation method category and briefly summarize papers by category. We aim to give a quick summary of the papers and refer interested readers to references for additional information. At the end of each section, we provide a table and compare the methods reviewed in that section.

Interested readers are referred to several surveys on medical image segmentation and analysis in general for further reading [8, 9, 10, 11, 12]. This paper is organized as follows. In Section 1, the classification of the extraction methods is given. In Section 2, pattern recognition techniques are reviewed.

Model-based approaches are discussed in Section 3. In Section 4, tracking-based approaches are reviewed. Methods based on artificial intelligence are discussed in Section 5. In Section 6, neural network-based methods are reviewed.

In Section 7, algorithms that are not particularly designed to extract vessels but deal with extraction of tubular objects are discussed. We conclude with discussion on the issues related to vessel extraction and its applications in Section 8. Instead, we put papers that use similar approaches into same group while we review them. During the categorization, we tried to be as specific as possible. For this reason we divided some categories into subcagetories as necessary.

We also created a separate category for some methods that are used significantly. For example we created a separate category for generalized cylinders model approach even it is a parametric model because of the amount of work done using this model.

Pattern recognition techniques A. Multi-scale approaches B. Skeleton-based approaches C. Region growing approaches D. Ridge-based approaches E. Differential geometry-based approaches F. Matching filters approaches G. Mathematical morphology schemes. Model-based approaches A. Deformable models a. Parametric deformable models - Active contours Snakes b. Geometric deformable models and front propagation methods B.

Parametric models C. Template matching approaches D. Generalized cylinders approaches III. Tracking-based approaches IV. Artificial Intelligence-based approaches V. Neural Network-based approaches VI.

Miscellaneous tube-like object detection approaches Although we divide segmentation methods in different categories, sometimes multiple techniques are used together to solve different segmentation problems. We, therefore, cross-listed the methods that fall into multiple segmentation categories. Such methods are reviewed in one section and mentioned in the other section with a pointer referencing to the section in which it is reviewed.

Humans are very well adapted to carry out PR tasks. Some of the PR techniques are the adaption of human PR ability to the computer systems. In the vessel extraction domain, PR techniques is concerned with the detection of vessel structures and the vessel features automatically. We divide PR techniques into seven categories: 1 multi-scale approaches, 2 skeleton-based centerline detection approaches, 3 region growing approaches, 4 ridge-based approaches, 5 differential Geometry-based approaches, 6 matching filters approaches, and 7 mathematical morphology schemes.

In the next sections, each category is discussed and the literature related to each category is reviewed. The main advantage of this technique is increased processing speed.

Major structures large vessels in our application domain are extracted from low resolution images while fine structures are extracted at high resolution. Another advantage is increased robustness. After segmenting the strong structures at the low resolution, weak structures, such as branches, in the neighborhood of the strong structures can be segmented at higher resolution.

Their method is based on simplex method-based linear programming and relaxation-based consistent labeling. To improve the robustness of the matcher, matching process is performed at three different resolutions. The stronger vessel tree branches are extracted at lower resolution while the weaker branches are extracted at higher scale. The extracted vessel tree is used for 3D reconstruction. Chwialkowski et al [14] employ multiresolution analysis based on wavelet transform.

Their work aims at automated qualitative analysis of arterial flow using velocity-sensitive, phase contrast MR images. The segmentation process is applied to the magnitude image and the velocity information from the phase difference image is integrated on the resulting vessel area to get the blood flow measurement.

Vessel boundaries are localized by employing a multivariate scoring criterion to minimize the effect of imaging artifacts such as partial volume averaging and flow turbulence. This method can also be classified as a contour detection approach. The works of Summers and Bhalerao [15] described in section 3. The vessel tree is created by connecting these centerlines.

Different approaches are used to extract the centerline structure. Variously these methods apply thresholding and then object connectivity, thresholding followed by thinning procedure, and extraction based on graph description. The resulting centerline structure is used for 3D reconstruction. Niki et al [2] describe a 3D blood vessel reconstruction and analysis method. Vessel reconstruction is achieved on short scan cone-beam filtered backpropagation reconstruction algorithm based on Gulberg and Zeng s work [18].

A 3D thresholding and 3D object connectivity procedure are applied to the resulting reconstructed images for the visualization and analysis process. A 3D graph description of blood vessels is used to represent the vessel anatomical structure. Tozaki et al [19] extract bronchus and blood vessels from thin slice CT images of the lung for 3D visualization and analysis.

First, a threshold is used to segment the images. Then, blood vessels and bronchus are differentiated by using their anatomical character e. Finally, a 3D thinning algorithm is applied to extract the vessel centerlines. The resulting centerline structure is used to analyze and classify the blood vessels. Their work helps in early detection of tumors of lung cancer patients. Kawata et al [20] analyze blood vessel structures and detect blood vessel diseases from conebeam CT images.

X-ray digital angiograms are collected using rotational angiography. First, a graph description procedure extracts the curvilinear centerline structures of the vessel tree using thresholding, elimination of the small connected components, and 3D fusion processes. Then, a 3D surface representation procedure extracts the characteristics of convex and concave shapes on blood vessel surface. The algorithm is. Kawata et al [21] detect blood vessel diseases on high resolution 3D cone-beam CT images.

This method has two major components: 1 A graph description procedure extracts a graph description of vessel centerlines from the vessel image; 2 A surface representation procedure extracts concave and convex shapes on vessels using curvature.

These shapes are used to represent aneurysms and stenoses on the vessels. Vessel surfaces are represented by curvatures which are invariant to arbitrary translations and rotations.

Surface characteristics such as Gaussian K and mean H curvatures, principal directions, surface normal direction, curvature magnitude, and surface types using signs of K and H can be obtained easily from the surface representation using curvatures. Since blood vessels surfaces are represented using curvatures, this work can also be classified as a differential geometry-based approach listed in section 2.

Parker et al [22] gives a theoretical review of 3D reconstruction algorithm of vascular networks from X-ray projection images. The algorithm has two steps: 1 Segmenting the centerline positions and densimetric profiles of artery candidates from each projection image; and 2 Combining multiple view information gathered in step one into one 3D artery representation in an iterative fashion.

A Review of Vessel Extraction Techniques and Algorithms

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Vessel extraction techniques and algorithms: a survey Abstract: Vessel segmentation algorithms are critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms, putting the various approaches and techniques in perspective by means of a classification of the existing research. While we target mainly the extraction of blood vessels, neurovascular structure in particular we also review some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We divide vessel segmentation algorithms and techniques into six main categories: 1 pattern recognition techniques, 2 model-based approaches, 3 tracking-based approaches, 4 artificial intelligence-based approaches, 5 neural network-based approaches, and 6 miscellaneous tube-like object detection approaches.

Show all documents A Robust Algorithm for Retinal Blood Vessel Extraction The 2-D Gabor filter is a linear filter, that has been widely used for low level oriented edge detection and extraction of texture features for discrimination purposes in image processing and computer vision fields. Frequency representation and orientation representation of the Gabor filter are identical to the human vision system. In the spatial domain, a 2-D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave [9]. Enhancement of the pixels of the blood vessels oriented along the various dimensions can be done due to the factor of directional selectivity of the Gabor filter.

Use of this Web site signifies your agreement to the terms and conditions. Special Issues. Contact Us. Change code. International Journal of Medical Imaging. The automatic extraction of brain vessels from Magnetic Resonance Angiography MRA has found its application in vascular disease diagnosis, endovascular operation and neurosurgical planning. A systematic survey of latest development in the area of vessel extraction by using region growing algorithms is present.


PDF | Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel.


Top PDF A Robust Algorithm for Retinal Blood Vessel Extraction

We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the extraction of blood vessels, neurosvascular structure in particular, we have also reviewed some of the segmentation methods for the tubular objects that show similar characteristics to vessels.

Continuous DSA images projections , obtained by X-ray fluoroscopy with contrast-media, are normally used as road maps in vessel catheterization. A more efficient technique would consist in the use of a 3D model reconstruction of the vascular tree, instead of continuous X-ray scans, as a map. By separating vessel information from the undesired background noise and signals coming from other organs and motion artefacts , efficient segmentation can play a key role in reducing the number of projections X-ray scans necessary to reconstruct a 3D vascular model. In what follows, the proposed method is described and some experimental results are reported, thus illustrating the behaviour of the algorithm when compared to other segmentation methods, ideated for the same application. The automatic calculation methods for the parameters used are also reported and discussed.

Show all documents A Systematic Survey and Evaluation of Blood Vessel Extraction Techniques Abstract: The automatic extraction of brain vessels from Magnetic Resonance Angiography MRA has found its application in vascular disease diagnosis, endovascular operation and neurosurgical planning. A systematic survey of latest development in the area of vessel extraction by using region growing algorithms is present. Then we detail the main challenges of vessel extraction and segmentation area.

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This article has been retracted. Retraction in: J Med Signals Sens. Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image.

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