Angiographic image analysis

Abstract : The important rise of medical imaging during the 20th century, mainly induced by physics breakthroughs related to nuclear magnetic resonance and X-rays has led to the development of imaging modalities devoted to visualise vascular structures. The analysis of such angiographic images is of great interest for several clinical applications. Initially designed to generate 2D data, these imaging modalities progressively led to the acquisition of 3D images, enabling to visualise vascular volumes. However, such 3D data are generally huge, being composed of several millions of voxels, while the useful –vascular– information generally represents less than 5% of the whole volume. In addition to this sparseness, the frequent low signal-to-noise ratio and the potential presence of artifacts make the analysis of such images quite a challenging task. In order to assist radiologists and clinicians, it is therefore necessary to design software tools enabling them to extract as well as possible the relevant information embedded in 3D angiographic data. One of the main ways to perform such a task is to develop segmentation methods, i.e., tools which (automatically or interactively) extract the vessels as 3D volumes from the angiographic images. A survey of such segmentation methods is proposed in Section 6.2. In particular, it sheds light on recent advances devoted to merge different image processing methodologies to improve the segmentation accuracy. Another way to consider computer-aided analysis of 3D angiographic images is to provide human experts with a base of high-level anatomical knowledge which can possibly be involved in more specific analysis procedures such as vessel labelling. Such knowledge can in particular be embedded in vascular atlases which are devoted to model qualitative and/or quantitative information related to vessels. A survey of different existing vascular atlases, and ways they can be created is proposed in Section 6.3. The purpose of this chapter is to provide some general background notions on 3D angiographic image analysis to the reader. Due to space limitations, it is impossible to propose an exhaustive overview on vessel segmentation and vascular knowledge modelling. Consequently, Sections 6.4 and 6.5 propose partial, but hopefully relevant, states of the art on these topics. They present some of the most classical and/or recent related works, and some pointers on more complete surveys linked to the main topics of this chapter (or to connected research fields, for the sake of completeness). They also present, in a more accurate fashion, some recent contributions of some of the authors, especially related to vessel segmentation.
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Olena Tankyevych, Hugues Talbot, Nicolas Passat, Mariano Musacchio, Michel Lagneau. Angiographic image analysis. Medical Image Processing: Techniques and Applications, pp.115-144, 2011, 1441997695. ⟨10.1007/978-1-4419-9779-1_6⟩. ⟨hal-01694511⟩

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