Characterization of vascular tree architecture using the Tokunaga taxonomy

Miguel A. Galarreta-Valverde; Jihan M. Zoghbi; Fabricio Pereira; Jean-Paul Beregi; Choukri Mekkaoui; Marcel P. Jackowski II

doi:10.1117/12.2080854

20 March 2015 Characterization of vascular tree architecture using the Tokunaga taxonomy

Miguel A. Galarreta-Valverde, Jihan M. Zoghbi, Fabricio Pereira, Jean-Paul Beregi, Choukri Mekkaoui, Marcel P. Jackowski II

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 941438 (2015) https://doi.org/10.1117/12.2080854
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

The diagnosis of cardiovascular disease is usually assisted by resonance angiography (MRA) or computed tomography angiography (CTA) imaging. The identification of abnormal vascular architecture from angiographic three-dimensional images is therefore crucial to the diagnosis of cardiovascular disease. Automated detection and quantification of vascular structure and architecture thus holds significant clinical value. In this work, we employ a Lindenmayer system to represent vascular trees from angiographic images and describe a quantitative measure based on the Tokunaga taxonomy to differentiate vascular architectures. Synthetic vessel architectures with varying bifurcation patterns were compared and results showed that this architectural measure is proportional to the level of branching. In real MRA images, this measure was able to differentiate between normal and abnormal intracerebral vasculature containing an aneurysm. Hence, this methodology not only allows for compact representation of vascular architectures but also provides a quantitative metric of bifurcation complexity, which has the potential to characterize different types of vascular abnormalities.

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Miguel A. Galarreta-Valverde, Jihan M. Zoghbi, Fabricio Pereira, Jean-Paul Beregi, Choukri Mekkaoui, and Marcel P. Jackowski II "Characterization of vascular tree architecture using the Tokunaga taxonomy", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941438 (20 March 2015); https://doi.org/10.1117/12.2080854

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