Lung Vessel Enhancement in Low-Dose CT Scans

The LANCELOT Method
  • Nico Merten
  • Kai Lawonn
  • Philipp Gensecke
  • Oliver Großer
  • Bernhard Preim
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

To reduce the patient’s radiation exposure from computed tomography scans (CT), low-dose CT scans can be recorded. Several image processing methods exist to segment or enhance the lung blood vessels from contrast-enhanced or high resolution CT scans, but the reduced contrast in low-dose CT scans leads to over- or under-segmentation. Our LANCELOT method combines maximum response and stick filters to enhance lung blood vessels in native, low-dose CT scans. We compare our method with the vessel segmentation and enhancing methods from Frangi and Sato et al. Our method has two advantages that were confirmed in an evaluation with two clinical experts: First, our method enhances small vessels and vessel branches more clearly and second, it connects vessels anatomically correct, while the others create discontinuities.

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Literatur

  1. 1.
    National Lung Screening Trial Research Team. The national lung screening trial: overview and study design. Radiology. 2011.Google Scholar
  2. 2.
    Sato, Y and Nakajima, S and Atsumi, H et al; Springer. 3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Proc CVRMed-MRCAS. 1997; p. 213–222.Google Scholar
  3. 3.
    Frangi, A F and Niessen, W J and Vincken, K et al; Springer. Multiscale vessel enhancement filtering. Proc MICCAI. 1998; p. 130–137.Google Scholar
  4. 4.
    Kuhnigk, J-M and Hahn, H and Hindennach, M et al . Lung lobe segmentation by anatomy-guided 3D watershed transform. In: Proc SPIE. vol. 5032; 2003. p. 1482–1490.Google Scholar
  5. 5.
    Rudyanto, R D and Kerkstra, S and Van Rikxoort, EMet al . Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med Image Anal. 2014;18(7):1217–1232.Google Scholar
  6. 6.
    Czerwinski, R N and Jones, D L and O’brien, W D. Line and boundary detection in speckle images. IEEE Trans Image Process. 1998;7(12):1700–1714.Google Scholar
  7. 7.
    Bresenham, J E. Algorithm for computer control of a digital plotter. IBM Systems Journal. 1965;4(1):25–30.Google Scholar
  8. 8.
    Ritter, F and Boskamp, T and Homeyer, A et al . Medical image analysis. IEEE Pulse. 2011;2(6):60–70.Google Scholar
  9. 9.
    Kuhnigk, J-M and Dicken, V and Bornemann, L et al . Morphological Segmentation and Partial Volume Analysis for Volumetry of Solid Pulmonary Lesions in Thoracic CT Scans. IEEE Trans Med Imaging. 2006;25(4):417–434.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Nico Merten
    • 1
    • 2
  • Kai Lawonn
    • 3
  • Philipp Gensecke
    • 4
  • Oliver Großer
    • 4
  • Bernhard Preim
    • 1
    • 2
  1. 1.Research Campus STIMULATEMagdeburgDeutschland
  2. 2.Department of Simulation and GraphicsOtto-von-Guericke UniversityMagdeburgDeutschland
  3. 3.Institute for Computational VisualisticsUniversity of Koblenz-LandauMainzDeutschland
  4. 4.Department of Radiology and Nuclear MedicineUniversity Hospital MagdeburgMagdeburgDeutschland

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