Paper
13 March 2013 Three-dimensional synthetic blood vessel generation using stochastic L-systems
Miguel A. Galarreta-Valverde, Maysa M. G. Macedo, Choukri Mekkaoui, Marcel P. Jackowski
Author Affiliations +
Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691I (2013) https://doi.org/10.1117/12.2007532
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Segmentation of blood vessels from magnetic resonance angiography (MRA) or computed tomography angiography (CTA) images is a complex process that usually takes a lot of computational resources. Also, most vascular segmentation and detection algorithms do not work properly due to the wide architectural variability of the blood vessels. Thus, the construction of convincing synthetic vascular trees makes it possible to validate new segmentation methodologies. In this work, an extension to the traditional Lindenmayer system (L-system) that generates synthetic 3D blood vessels by adding stochastic rules and parameters to the grammar is proposed. Towards this aim, we implement a parser and a generator of L-systems whose grammars simulate natural features of real vessels such as the bifurcation angle, average length and diameter, as well as vascular anomalies, such as aneurysms and stenoses. The resulting expressions are then used to create synthetic vessel images that mimic MRA and CTA images. In addition, this methodology allows for vessel growth to be limited by arbitrary 3D surfaces, and the vessel intensity profille can be tailored to match real angiographic intensities.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miguel A. Galarreta-Valverde, Maysa M. G. Macedo, Choukri Mekkaoui, and Marcel P. Jackowski "Three-dimensional synthetic blood vessel generation using stochastic L-systems", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691I (13 March 2013); https://doi.org/10.1117/12.2007532
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CITATIONS
Cited by 17 scholarly publications and 2 patents.
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KEYWORDS
Image segmentation

Blood vessels

Angiography

Stochastic processes

Computed tomography

3D image processing

3D modeling

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