Abstract
Numerical blood flow simulations helps in understanding the flow behaviors of the blood inside the large human arteries which can help understand the chronological disorders of the blood vessels as a result of mechanical forces. The extractions of the patient specific geometries from the digital images are a building block for any numerical approach. However, it is difficult to achieve the topological models for the simulations as the overall manual process of extraction is error prone and it is time consuming. The work presented here is the approach towards constructing a semi-automated extraction based on the differential operator. The approach concentrates on how the intrinsic property of the medical images helps in guiding the processing and segmentation of an image. The result achieved via the differential operator based approach provides a distinctive arterial structures from it’s surrounding. Thus it becomes simpler to integrate the extracted geometric models in the cycle of numerical simulations as it reduces overall time in pre-processing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dove, E.: Physics of medical imaging - An Introduction (2004)
Bredenhöller, C., Feuerlein, U.: Somatom sensation 10/16 application guide. Siemens AG, Germany (2005)
Gander, W.: Algorithms for the qr decomposition. Res. Rep 80(02), 1251–1268 (1980)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. In: ACM Siggraph Computer Graphics, vol. 21, pp. 163–169. Association for Computing Machinery, New York (1987)
Patel, N., Küster, U.: Geometry dependent computational study of patient specific abdominal aortic aneurysm. In: Sustained Simulation Performance 2014, pp. 221–238. Springer, Berlin (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Patel, N., Küster, U. (2015). Semi-Automatic Segmentation and Analysis of Vascular Structures in CT Data. In: Resch, M., Bez, W., Focht, E., Kobayashi, H., Qi, J., Roller, S. (eds) Sustained Simulation Performance 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-20340-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-20340-9_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20339-3
Online ISBN: 978-3-319-20340-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)