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Medical Image Processing pp 115–144Cite as

Angiographic Image Analysis

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Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

In the last 20 years, progress in 3D medical imaging (such as MRI and CT) has led to the development of modalities devoted to visualise vascular structures. These angiographic images progressively proved their usefulness in the context of various clinical applications. However, such data are generally complex to analyse due to their size and low amount of relevant (vascular) information versus noise, artifacts and other anatomical structures. Therefore, there is an ongoing necessity to provide tools facilitating image visualisation and analysis. In this chapter, we first focus on vascular image analysis. In particular, we present a survey on both standard and recent vessel segmentation methodologies. We then discuss the existing ways to model anatomical knowledge via the computation of vascular atlases. Such atlases can notably be embedded in computer-aided radiology tools.

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Notes

  1. 1.

    Part I of this survey [93] also describes MRA acquisition techniques.

  2. 2.

    Due to limited space we heave omitted those methods which have resulted led to fewer publications, such as neural network-based methods [52].

  3. 3.

    Note that, by duality, region-growing also provides solutions to segmenting vessels by skeletonization. In such a case, the growing process starts from a seed being a subset of the background (which can then be automatically defined), and generally includes topological constraints in the propagation criterion [27, 76].

  4. 4.

    From a structural (and more especially from a topological) point of view, the terminology of “tree” is generally incorrect for most of vascular networks, despite its frequent use in the literature devoted to angiographic imaging. In the sequel of the chapter we will generally distinguish vascular trees from vascular networks. This distinction will be clarified in the next sections.

  5. 5.

    The notion of “vascular areas”, which actually does not match the anatomical notion of vascular territories, was introduced in [78] in order to propose a partition of the cerebral volume to facilitate the segmentation of vessels from PC-MRA data.

  6. 6.

    The notion of an atlas has been the subject of a quite intensive research activity during the last 15 years, especially in the field of brain imaging. The emergence of computational anatomy [39, 50, 99] is, in particular, a direct expression of such research activities.

  7. 7.

    The reader interested in registration – which is an issue strongly linked to (vascular) atlas generation – may complete the study of this chapter by reading the following surveys [48, 116].

  8. 8.

    However, this fact does not represent a crippling drawback, since the relevance of interindividual variability handling is essentially modulated by the applications requiring the designed atlas. In particular, deterministic atlases must not be considered as less (or more) relevant than statistical ones.

  9. 9.

    Note that the information on coronary arteries gathered in [26] corresponds to a sample of patients with “normal-sized hearts”. This example illustrates the general necessity to constraint some anatomical hypotheses if we hope to finally obtain a useful model from a finite (and generally restricted) set of patients. Such a consideration remains valid when considering statistical models: a classical example is the restriction to either healthy or non-healthy people in the considered pool of patients.

  10. 10.

    See, e.g., [22] for a robust medial axis computation method.

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Authors and Affiliations

  1. Laboratoire d’Informatique Gaspard-Monge – UMR CNRS 8049, Université Paris-Est, Paris, France

    Olena Tankyevych

  2. Université Paris-Est, Paris, France

    Hugues Talbot

  3. Université de Strasbourg, Strasbourg, France

    Nicolas Passat

  4. Hôpital Louis-Pasteur, Colmar, France

    Mariano Musacchio & Michel Lagneau

Authors
  1. Olena Tankyevych
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  2. Hugues Talbot
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  3. Nicolas Passat
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  4. Mariano Musacchio
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  5. Michel Lagneau
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Correspondence to Olena Tankyevych .

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  1. , Applied Physics and Medical Imaging, California State University Channel Isla, One University Drive, Camarillo, 93012, USA

    Geoff Dougherty

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Tankyevych, O., Talbot, H., Passat, N., Musacchio, M., Lagneau, M. (2011). Angiographic Image Analysis. In: Dougherty, G. (eds) Medical Image Processing. Biological and Medical Physics, Biomedical Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9779-1_6

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