Angiographic Images Segmentation Techniques

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)
DOI: 10.4018/978-1-60960-561-2.ch209
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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.
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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):

  • 1.

    Techniques for “pattern-matching” or pattern recognition

  • 2.

    Techniques based on models

  • 3.

    Techniques based on tracking

  • 4.

    Techniques based on artificial intelligence

  • 5.

    Main Focus

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.

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