Semi-Automatic Segmentation and Analysis of Vascular Structures in CT Data

Nisarg Patel () and Uwe Küster ()
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Nisarg Patel: High Performance Computing Center Stuttgart (HLRS)
Uwe Küster: High Performance Computing Center Stuttgart (HLRS)

A chapter in Sustained Simulation Performance 2015, 2015, pp 205-218 from Springer

Abstract: 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.

Keywords: Adaptive Approach; Computer Tomography Image; Patient Specific Geometry; Computer Tomography Data; Registration Toolkit (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-20340-9_17

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DOI: 10.1007/978-3-319-20340-9_17

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