Coronary Artery Segmentation in Angiograms with Pattern Recognition Techniques -- A Survey
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: Digital image acquisition and processing in medical x-ray imaging

paper_content:

This contribution discusses a selection of today’s techniques ::: and future concepts for digital x-ray imaging in medicine. ::: Advantages of digital imaging over conventional analog methods ::: include the possibility to archive and transmit images in digital information ::: systems as well as to digitally process pictures before display, ::: for example, to enhance low contrast details. After reviewing ::: two digital x-ray radiography systems for the capture of still x-ray ::: images, we examine the real time acquisition of dynamic x-ray images ::: (x-ray fluoroscopy). Here, particular attention is paid to the ::: implications of introducing charge-coupled device cameras. We ::: then present a new unified radiography/fluoroscopy solid-state detector ::: concept. As digital image quality is predominantly determined ::: by the relation of signal and noise, aspects of signal transfer, noise, ::: and noise-related quality measures like detective quantum efficiency ::: feature prominently in our discussions. Finally, we describe a ::: digital image processing algorithm for the reduction of noise in images ::: acquired with low x-ray dose.
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paper_title: Image Registration for Digital Subtraction Angiography

paper_content:

In clinical practice, Digital Subtraction Angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. The diagnostic relevance of the images is often reduced by artifacts which arise from the misalignment of successive images in the sequence, due to patient motion. In order to improve the quality of the subtraction images, several registration techniques have been proposed. However, because of the required computation times, it has never led to algorithms that are fast enough so as to be acceptable for integration in clinical applications. In this paper, a new approach to the registration of digital angiographic images is proposed. It involves an edge-based selection of control points for which the displacement is computed by means of template matching, and from which the complete displacement vector field is constructed by means of interpolation. The final warping of the images according to the calculated displacement vector field is performed real-time by graphics hardware. Experimental results with several clinical data sets show that the proposed algorithm is both effective and very fast.
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paper_title: DSA image enhancement via multi-resolution motion correction for interventional procedures: a robust strategy

paper_content:

Visualization of blood vessels during X-ray based interventional procedures is hindered by patient motion and noise in the X-ray imaging systems. This degrades the image quality in Operating Room (OR) procedures. ::: ::: This paper presents a two step strategy to improve the image quality: (a) compensates the motion and (b) enhances the vessels thereby suppressing the background. We minimize the motion artifacts by first registering the bolus images to mask images using B-spline based inverse-consistent image registration method. In step (b), we use two class-based strategies: piecewise and nonlinear, to gradually suppress background noise thereby enhancing the blood vessels and preserving its translucency. The two step enhancement method was tested on 73 different subjects depicting several anatomies such as: brain, carotid and renal. The presented method was compared with the existing methods using signal-to-noise ratio over all the datasets. Our experimental protocol demonstrated consistency between both methods and a significant improvement of both enhancement strategies (SNR improvement of 49.21% for piecewise normalization and 66.44% for nonlinear normalization) compared to conventional methods. We validated our system using GE's PMMA phantoms. The system works on PC using C++.
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paper_title: Linear and morphological digital image enhancement of peripheral angiography images

paper_content:

X-ray angiography images of the legs are enhanced using linear and morphological image processing tech- I1iques. Salient features of the leg images include high contrast bones relatively smooth soft tissue and arteries. Filter parameters are optimized to enhance the arteries while deemphasizing the bones and other structured noise. Morphological processing has several advantages compared to linear processing including improved retention of both arterial dimensions and x-ray density. 1© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Coronary tree extraction from X-ray angiograms using marked point processes

paper_content:

In this paper, we use marked point processes to perform an unsupervised extraction of the coronary tree from 2D X-ray angiography. These processes provide a rigorous framework based on measure theory to describe a scene by an unordered set of objects. Firstly, the thick branches are detected at low resolution using a segment process. Secondly, a polygon tree is derived from this first result at high resolution to represent the main part of the coronary tree. Finally, new branches are extracted using a recursive algorithm based on the modeling of the descendants of a given branch by a polyline process in the neighborhood of this branch. Process optimization is done via simulated annealing using a reversible jump Markov chain Monte Carlo algorithm. Experimental results show the relevance of the object process models.
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: 3-D reconstruction of coronary arteries

paper_content:

Coronary arteriography is a technique used for evaluating the state of the coronary arteries. Matching of coronary arteries from multiple views is necessary for obtaining a 3-D (three dimensional) description of the arterial tree. Overlapping vessels and artifacts due to digital subtraction of the angiogram background make the matching process quite difficult. Simplex method based linear programming and relaxation based consistent labeling are applied to skeletons from three views in order to obtain a matching of branches between views. Results show low error for 3-D reconstruction, by overlapping its forward projection with the skeleton extracted from one view. >
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paper_title: Multi scale classification approach for coronary artery detection from X-ray angiography

paper_content:

X-ray angiography is currently the gold standard for navigation guidance during percutaneous coronary interventions. From X-ray angiography, robust automatic detection of coronary arteries would be of great interest during cardiac interventions. Multi scale Hessian-based filtering was proven successful to automatically detect vessels from X-ray angiography. However, other anatomical structures interfere greatly with the detection process and the result still contains many false positives. The goal of the project is to propose a novel machine learning-based method to improve Hessian-based coronary artery detection from X-ray angiography. The proposed method divides Hessian-filtered images in patches, uses feature extraction with a contour profiling algorithm, and classifies using Support Vector Machines. The method is applied recursively on the detected connected components using patches of different sizes to define the arteries. This scheme allows an improvement of robustness against noise and imaging artifacts.
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paper_title: A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

paper_content:

Nowadays, medical diagnostics using images has a considerable importance in many areas of medicine. It promotes and makes easier the acquisition, transmission and analysis of medical images. The use of digital images for diseases evaluations or diagnostics is still growing up and new application modalities are always appearing. This paper presents a methodology for a semi-automatic segmentation of the coronary artery tree in 2D X-Ray angiographies. It combines a region growing algorithm and a differential geometry approach. The proposed segmentation method identifies about 90% of the main coronary artery tree.
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paper_title: A Segmentation Method of Coronary Angiograms Based on Multi-scale Filtering and Region-Growing

paper_content:

In this paper, we propose a simple and powerful method for segmentation of the coronary artery in angiograms, which combines Hessian matrix multi-scale filtering and region-growing. In this method, Hessian matrix multi-scale filtering is used to enhance vessel in angiographic images. The multi-seed region-growing algorithm is used to extract the coronary artery tree from the enhanced images. As considering the characteristics of the X-ray angiographic images and the advantages from both, the proposed method is sensitive to the vessel-like structures and robust to noise in the angiograms. Using this method, both the coronary artery trees and most of smaller distal vessels could be extracted clearly. The results show that the proposed method is suitable for the segmentation of coronary angiograms, and is available for further accurate quantitative analysis to coronary angiograms.
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paper_title: A Novel Method of Vessel Segmentation for X-ray Coronary Angiography Images

paper_content:

This paper presents a new automatic region-growing method for vessel segmentation in two-dimensional X-ray coronary angiography images. The method consists of two parts: the feature map extraction based on a novel vesselness function; and the segmentation process which includes automatic seed-point selection, main branch segmentation and vessel detail repair. Both the greyscale and spatial information are extracted for segmentation based on region growing algorithm. The presented method is validated on several clinical X-ray coronary angiography images, and the experimental results show that the method can not only segment large vessels but also small vessels.
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paper_title: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

paper_content:

We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eyecare specialists for purposes of patient screening, treatment evaluation, and clinical study. Our method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. A comparison of our method against hand-labeled ground truth segmentations of five images yielded 65% sensitivity and 81% specificity. A previously known technique yielded 69% sensitivity and 63% specificity. For a baseline, we also compared the ground truth against a second hand labeling, yielding 80% sensitivity and 90% specificity. These numbers indicate our method improves upon the previously known technique, but that further improvement is still possible.
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: Extraction of coronary artery trees in angiocardiography images

paper_content:

Dynamic variations of coronary arterial curvilinearity have been very difficult to study in the current angiograms. However, there is both experimental and clinical evidence showing that vessel extraction is very useful for the purposes of surgery treatment and clinical study. We propose in this paper an automated algorithm to locate the outline of the coronary artery tree blood vessels in angiograms. The proposed approach is a useful tool for physicians. The algorithm automatically segments the coronary arteries from the cineangiogram followed by an accurate extraction of the vessel features. Such a preprocessing in conjunction with the matched (Gaussian) filter can greatly improve the results. The proposed segmentation algorithm consists of two major processes: (1) Gaussian filter for blood vessels and (2) thresholding. We evaluated our algorithm by testing it on a raw data set of 100 angiogram images and the results were validated by two ways. First, hand-labelled annotations of ground truth segmentation of 20 images; and the results showed that our algorithm is better in detecting features of arteries even with poor contrast that bare eye could not recognize it in the hand-labelling. Secondly, by making a questionnaire to validate the efficient illustration output. The hand-labelling matched our results by 98% of the output while the validation rate of questionnaire was 90.84%. We conclude that our improved algorithm is efficient for extracting the coronary artery tree vessels including the tiny ones. In addition, the algorithm was fast to extract vessels that it takes between 14 to 15 s per image.
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paper_title: Automated coronary artery extraction using watersheds

paper_content:

An algorithm for the automated extraction of the skeletons and borders of coronary arteries in digitized angiograms is proposed. Initially, the approximate skeleton and borders of the coronary artery tree are extracted through an artery tracking method based on circular template analysis. The skeleton and borders of each artery segment are used for constructing its enclosing area where the defined skeleton and border curves are considered as markers. Using the marked artery segment enclosing area (ASEA), an artery gradient image is constructed where all pixels inside the ASEA, except skeleton ones, are assigned the gradient magnitude of the original image. The markers of the artery gradient image are imposed as its unique regional minima by the homotopy modification method. Then, the watershed transform is applied for extracting the artery segment borders. Experimental results using digitized coronary angiograms are presented.
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paper_title: Detection of coronary artery tree using morphological operator

paper_content:

Presents a technique of the automatic detection of the coronary artery tree on the coronary cineangiogram. The method uses several kinds of morphological operations. The morphological Top-Hat operator is used for enhancing the shape of the artery. The morphological erosion and the half-thresholding are used for removing points except the coronary area. Then one can extract the entire coronary artery tree on the angiogram after pointing at just one point on the coronary artery. The extracted coronary parts are skeletonized by the thinning operation. Finally the morphological watershed transformation is done by using the skeleton and the dilated binary image of the coronary. As a result, a clear boundary of the coronary artery is obtainable automatically. Some experimental studies confirm the ease and correctness of the detection of the coronary artery boundary by the method proposed.
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: Semi-automatic extraction of vascular networks in angiograms

paper_content:

A semi-automatic algorithm for the segmentation of angiographic images is proposed. First, the digital angiogram is smoothed by an image structure preserving technique which is based on homogeneity testing and anisotropic diffusion. Then, the watershed transform is applied to the smoothed image gradient magnitude and the resulting initial image partition is input to a fast hierarchical region merging process. Finally, the vessel regions are extracted from the segmented image by a simple point-and-click interactive procedure. Experimental results on Digital Subtracted Angiograms (DSA) are presented.
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paper_title: Segmentation of Coronary Artery on Angiogram by Combined Morphological Operations and Watershed

paper_content:

In this paper, a scheme using fuzzy mathematical morphology operations is proposed for extracting coronary ar- teries tree on angiogram. After the enhancement of image with the traditional morphological top-hat operator, the vessel image is filtered with the fuzzy morphological opening with a set of linear structuring elements at different orientation. At the same time, the enhanced image is also filtered with the dual of fuzzy opening with the same structuring elements set. The two filtered vessel image is then combined into a new image considering the local orientation consistency, detected by the morphological filter, within a linear neighborhood of each location. Threshold of the result image produces the binary vessel structure which is separate from background structure. The extracted vessel structure is lastly treated as prior of shape and location of vessel and used as marker in morphological watershed for detecting the accurate vessel boundaries. Experimentation shows the performance of proposed method on extraction vascular tree on clinical angiogram under x-ray. I. INTRODUCTION
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paper_title: Image Processing, Analysis and Machine Vision

paper_content:

List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
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paper_title: Automated coronary artery extraction using watersheds

paper_content:

An algorithm for the automated extraction of the skeletons and borders of coronary arteries in digitized angiograms is proposed. Initially, the approximate skeleton and borders of the coronary artery tree are extracted through an artery tracking method based on circular template analysis. The skeleton and borders of each artery segment are used for constructing its enclosing area where the defined skeleton and border curves are considered as markers. Using the marked artery segment enclosing area (ASEA), an artery gradient image is constructed where all pixels inside the ASEA, except skeleton ones, are assigned the gradient magnitude of the original image. The markers of the artery gradient image are imposed as its unique regional minima by the homotopy modification method. Then, the watershed transform is applied for extracting the artery segment borders. Experimental results using digitized coronary angiograms are presented.
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paper_title: Detection of coronary artery tree using morphological operator

paper_content:

Presents a technique of the automatic detection of the coronary artery tree on the coronary cineangiogram. The method uses several kinds of morphological operations. The morphological Top-Hat operator is used for enhancing the shape of the artery. The morphological erosion and the half-thresholding are used for removing points except the coronary area. Then one can extract the entire coronary artery tree on the angiogram after pointing at just one point on the coronary artery. The extracted coronary parts are skeletonized by the thinning operation. Finally the morphological watershed transformation is done by using the skeleton and the dilated binary image of the coronary. As a result, a clear boundary of the coronary artery is obtainable automatically. Some experimental studies confirm the ease and correctness of the detection of the coronary artery boundary by the method proposed.
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: Finding Edges and Lines in Images

paper_content:

Abstract : The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been been argued for. This thesis is an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of an edge operator. Variational techniques are used to find a solution over the space of all linear shift invariant operators. The first criterion is that the detector have low probability of error i.e. failing to mark edges or falsely marking non-edges. The second is that the marked points should b The third criterion is that there should be low probability of more than one response to a single edge. The technique is used to find optimal operators for step edges and for extended impulse profiles (ridges or valleys in two dimensions). The extension of the one dimensional operators to two dimensions is then discussed. The result is a set of operators of varying width, length and orientation. The problem of combining these outputs into a single description is discussed, and a set of heuristics for the integration are given. (Author)
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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paper_title: Automatic vessel extraction from angiogram images

paper_content:

Vessel extraction from angiogram images is useful for blood vessel measurement and computer visualization of the coronary artery tree. A digitized angiogram can be treated as a height map and ridges in this map correspond to the centerlines of the blood vessels. The authors propose a ridge extraction algorithm based on the calculation of the local maximum ascent direction to extract blood vessel axes automatically from all angiogram image. The output of the ridge detection algorithm is a set of ridge pixels assembled in a natural hierarchy order, aligned with the centerlines of the blood vessel, plus a set of ridge pixels induced by image noise, which require a post processing stage to remove. To extract vessel axes from the angiogram images. First, the digitized angiogram is balanced by a median filter it is then smoothed by a projective non-linear diffusion method. Next, an ROI, which is crossed by the shadow of a vessel, is detected automatically by adaptive thresholding, both to cut the cost of the ridge extraction process and to reduce the number of the spurious ridges induced by image noise. The authors then apply the ridge detection algorithm to the ROI to find candidate vessel centerlines. Finally the detected ridges are piped into a curve relaxation process and the candidate vessel centerlines are assembled together; spurious lines induced by noise are removed.
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paper_title: Intensity ridge and widths for tubular object segmentation and description

paper_content:

Introduces a technique for the automated description of tubular objects in 3D medical images. The goal of automated 3D object description is to extract a representation which consistently details the location, size, and structure of objects in 3D images using minimal user interaction. Such a representation provides a means by which objects can be classified, quantifiably evaluated, and registered. It also serves as a region of interest specification for visualization processes. The technique presented in this paper is suited for generating representations of 3D objects with nearly circular cross sections which have, possibly as a result of a global operation (e.g., blurring), intensity extrema near their centers. Such tubular objects commonly occur within human anatomy (e.g., vessels and selected bones). The medial axis of each of these objects is well approximated by its intensity ridge. The scales of the local maxima in medialness at all points along the ridge can be mapped to local width estimates. Together these measures capture the location, size, and structure of tubular objects. This paper covers the mathematical basis, the implementation issues, and the application of this technique to the extraction of vessels from 3D magnetic resonance angiographic images and bones from 3D X-ray computed tomographic images.
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paper_title: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction

paper_content:

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale measures that minimize these effects. Monte Carlo experiments using simulated and clinical data are used to quantify how these "dynamic-scale" enhancements address clinical needs regarding speed, accuracy, and automation. In particular, we show that dynamic-scale ridge traversal is insensitive to its initial parameter settings, operates with little additional computational overhead, tracks centerlines with subvoxel accuracy, passes branch points, and handles significant image noise. We also illustrate the capabilities of the method for medical applications involving a variety of tubular structures in clinical data from different organs, patients, and imaging modalities.
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paper_title: A review of vessel extraction techniques and algorithms

paper_content:

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. 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) 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, preprocessing, user interaction, and result type.
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Title: Coronary Artery Segmentation in Angiograms with Pattern Recognition Techniques -- A Survey

Section 1: INTRODUCTION
Description 1: This section introduces the importance of coronary artery segmentation, the imaging modalities used, and the focus and aim of the survey.

Section 2: CORONARY ARTERY IMAGE ENHANCEMENT AND ANALYSIS IN ANGIOGRAMS
Description 2: This section discusses the techniques and challenges associated with enhancing and analyzing coronary artery images in angiograms.

Section 3: MULTI SCALE APPROACHES (MSA)
Description 3: This section provides a brief description of multi-scale approaches used for coronary artery segmentation in angiograms.

Section 4: REGION GROWING APPROACHES (RGA)
Description 4: This section explains the region growing approaches and their use in coronary artery segmentation from angiograms.

Section 5: MATCHING FILTERS APPROACHES (MFA)
Description 5: This section discusses the matching filters approaches for extracting coronary arteries in angiograms.

Section 6: MATHEMATICAL MORPHOLOGY APPROACHES (MMA)
Description 6: This section covers mathematical morphology approaches applied to coronary artery segmentation in angiograms.

Section 7: SKELETON BASED (CENTERLINE DETECTION) APPROACHES (SBA)
Description 7: This section describes skeleton-based approaches used for centerline detection in coronary artery segmentation.

Section 8: RIDGE BASED APPROACHES (RBA)
Description 8: This section elaborates on ridge-based approaches for coronary artery segmentation.

Section 9: SUMMARIZATION
Description 9: This section summarizes the surveyed papers based on pattern recognition techniques, input types, enhancement methods, output types, user interaction, and 3D reconstruction capabilities.

Section 10: CONCLUSION
Description 10: This section concludes the paper by summarizing the findings, discussing the importance of accurate coronary artery segmentation, and indicating future research directions.