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.

/pdf/a-review-of-vessel-extraction-techniques-and-algorithms-1qqzv9f4am.pdf

A review of vessel extraction techniques and algorithms

A method for automatically evaluating image data taking over a sequence of image frames to determine a contour of a left ventricle (LV). The sequence of image frames are converted to digital data that identify a gray scale value for each pixel in each image frame. Using probability, an initial LV region is estimated based upon previously determined a priori parameters that are derived from processing manually drawn contours of the LV in other hearts, for the sequence of image frames being analyzed. The initial estimate of the LV region is refined using motion constraints, which are also derived from training data determined from the manually-drawn contours of other hearts. An LV region is estimated and its coordinates are extracted for use in applying global shape constraints that provide a more accurate LV boundary estimate for the region around an apex of the left ventricle. Finally, the estimated LV boundary estimate is tested for consistency, yielding an accepted estimate, or warning that the estimate is unreliable. The resulting automatically determined contours of each image frame can be displayed almost immediately after the image data are produced so that a physician can more readily diagnose physiological defects of the heart.

Method for determining the contour of an in vivo organ using multiple image frames of the organ

Geometrical calibration of X-ray imaging chains for three-dimensional reconstruction.

A fully automated system for detecting the endocardial and epicardial boundaries in a two-dimensional echocardiography by using fuzzy reasoning techniques is proposed. The image is first enhanced by applying the Laplacian-of-Gaussian edge detector. Second, the center of the left ventricle is determined automatically by analyzing the original image. Next, a search process radiated from the estimated center is performed to locate the endocardial boundary by using the zero-crossing points. After this step, the estimation of the range of radius of a possible epicardial boundary is carried out by comparing the high-level knowledge of intensity changes along all directions with the actual image intensity changes. The high-level knowledge of global intensity change in the image is acquired from experts in advance, and is represented in the form of fuzzy linguistic descriptions and relations. Knowledge of local intensity change can therefore be deduced from the knowledge of global intensity change through fuzzy reasoning. >

Epicardial boundary detection using fuzzy reasoning

We propose a method for extracting the left ventricular (LV) contours from left ventriculograms by means of a neural edge detector (NED) in order to extract the contours which accord with those traced by a cardiologist. The NED is a supervised edge detector based on a modified multilayer neural network, and is trained by use of a modified back-propagation algorithm. The NED can acquire the function of a desired edge detector through training with a set of input images and the desired edges obtained from the contours traced by a cardiologist. The proposed contour-extraction method consists of 1) detection of "subjective edges" by use of the NED; 2) extraction of rough contours by use of low-pass filtering and edge enhancement; and 3) a contour-tracing method based on the contour candidates synthesized from the edges detected by the NED and the rough contours. Through experiments, it was shown that the proposed method was able to extract the contours in agreement with those traced by an experienced cardiologist, i.e., we achieved an average contour error of 6.2% for left ventriculograms at end-diastole and an average difference between the ejection fractions obtained from the manually traced contours and those obtained from the computer-extracted contours of 4.1%.

Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector

The authors present a method for automating the generation of search targets used in finding borders of the left ventricle (LV) from digital-subtraction angiography (DSA) images and thus further automating LV-border determination. This method uses a large clinical database of LV borders to generate a probability surface of expected border locations. A composite image of all observed border points is used to create a set of extraction lines that is overlaid on the LV of interest. Border points are found by using a dynamic search on the density matrix formed by extracting pixel values at the points defined by the extraction matrix. User interaction is only needed to define the valve plane, confirm the final border, and make corrections if needed. LV contours then be used to find end-diastole and end-systole volume and ejection fraction. >

https://ieeexplore.ieee.org/iel2/246/2467/00072580.pdf

Improving left ventricular border recognition using probability surfaces

In this paper we discuss three-dimensional reconstruction and flow measure­ments of the coronary artery vascular bed from multiple view coronary angiography. The reconstruction generates densitometric centerline and cross-sectional area measurements of the major branches of the arterial bed from two view ECG correlated digital angiograms for all images obtained throughout the cardiac cycle. The completeness of the reconstruction is well suited to allow computation of absolute flow through the major branches.

Three-dimensional reconstruction and flow measurements of coronary arteries using multi-view digital angiography

The three-dimensional reconstruction of vascular networks from X-ray projection images is reviewed in analogy to general three-dimensional computed tomography. Intrinsic vascular bed properties (connectivity, density (spareness), lumen dimensions, radio-opaque contrast uniformity, etc.) suggest a general data representation, provide a basis for the algorithmic nature of reconstruction, and can be used to predict tradeoffs between reconstruction accuracy, number of views, and view orientation. >

Three-dimensional vascular reconstruction from projections: a theoretical review

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Improving left ventricular border recognition using probability surfaces

Three-dimensional reconstruction and flow measurements of coronary arteries using multi-view digital angiography

Three-dimensional vascular reconstruction from projections: a theoretical review