Angiographic Images Segmentation Techniques
Francisco J. Nóvoa (University of A Coruña, Spain), Alberto Curra (University of A Coruña, Spain), M. Gloria López (University of A Coruña, Spain) and Virginia Mato (University of A Coruña, Spain)
Copyright: © 2009
Heart-related pathologies are among the most frequent health problems in western society. Symptoms that point towards cardiovascular diseases are usually diagnosed with angiographies, which allow the medical expert to observe the bloodflow in the coronary arteries and detect severe narrowing (stenosis). According to the severity, extension, and location of these narrowings, the expert pronounces a diagnosis, defines a treatment, and establishes a prognosis. The current modus operandi is for clinical experts to observe the image sequences and take decisions on the basis of their empirical knowledge. Various techniques and segmentation strategies now aim at objectivizing this process by extracting quantitative and qualitative information from the angiographies.
Segmentation is the process that divides an image in its constituting parts or objects. In the present context, it consists in separating the pixels that compose the coronary tree from the remaining “background” pixels.
None of the currently applied segmentation methods is able to completely and perfectly extract the vasculature of the heart, because the images present complex morphologies and their background is inhomogeneous due to the presence of other anatomic elements and artifacts such as catheters.
The literature presents a wide array of coronary tree extraction methods: some apply pattern recognition techniques based on pure intensity, such as thresholding followed by an analysis of connected components, whereas others apply explicit vessel models to extract the vessel contours.
Depending on the quality and noise of the image, some segmentation methods may require image preprocessing prior to the segmentation algorithm; others may need postprocessing operations to eliminate the effects of a possible oversegmentation.
The techniques and algorithms for vascular segmentation could be categorized as follows (Kirbas, Quek, 2004):
Techniques for “pattern-matching” or pattern recognition
Techniques based on models
Techniques based on tracking
Techniques based on artificial intelligence
This section describes the main features of the most commonly accepted coronary tree segmentation techniques. These techniques automatically detect objects and their characteristics, which is an easy and immediate task for humans, but an extremely complex process for artificial computational systems.
Key Terms in this Chapter
Artery: Each of the vessels that take the blood from the heart to the other bodyparts.
Expert System: Computer or computer program that can give responses that are similar to those of an expert.
Segmentation: In computer vision, segmentation refers to the process of partitioning a digital image into multiple regions. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (structures) in images, in this case, the coronary tree in digital angiography frames.
Stenosis: A stenosis is an abnormal narrowing in a blood vessel or other tubular organ or structure. A coronary artery that’s constricted or narrowed is called stenosed. Buildup of fat, cholesterol and other substances over time may clog the artery. Many heart attacks are caused by a complete blockage of a vessel in the heart, called a coronary artery.
Computerized Tomography: Exploration of X-rays that produces detailed images of axial cuts of the body. A CT obtains many images by rotating around the body. A computer combines all these images into a final image that represents the bodycut like a slice.
Angiography: Image of blood vessels obtained by any possible procedure.
Thresholding: A technique for the processing of digital images that consists in applying a certain property or operation to those pixels whose intensity value exceeds a defined threshold.