Advertisement

Algorithm for Blood-Vessel Segmentation in 3D Images Based on a Right Generalized Cylinder Model: Application to Carotid Arteries

  • Leonardo Flórez Valencia
  • Jacques Azencot
  • Maciej Orkisz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6374)

Abstract

The arterial lumen is modeled by a spatially continuous right generalized cylinder with piece-wise constant parameters. The method is the identifies the parameters of each cylinder piece from a series of planar contours extracted along an approximate axis of the artery. This curve is defined by a minimal path between the artery end-points. The contours are extracted by use of a 2D Fast Marching algorithm. The identification of the axial parameters is based on a geometrical analogy with piece-wise helical curves, while the identification of the surface parameters uses the Fourier series decomposition of the contours. Thus identified parameters are used as observations in a Kalman optimal estimation scheme that manages the spatial consistency from each piece to another. The method was evaluated on 15 datasets from the MICCAI 3D Segmentation in the Clinic Grand Challenge: Carotid Bifurcation Lumen Segmentation and Stenosis Grading ( http://cls2009.bigr.nl/ ). The average Dice similarity score was 71.4.

Keywords

Compute Tomography Angiography Contour Extraction Stenosis Grade Generalize Cylinder Fast March 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Azencot, J., Orkisz, M.: Deterministic and stochastic state model of right generalized cylinder (RGC-sm): application in computer phantoms synthesis. Graph. Models 65, 323–350 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Kalman, R.: A New Approach to Linear Filtering and Prediction Problems. Trans ASME–J. Basic Engineering 82, 35–45 (1960)Google Scholar
  3. 3.
    Flórez Valencia, L., Azencot, J., Vincent, F., Orkisz, M., Magnin, I.: Segmentation and Quantification of Blood Vessels in 3D Images using a Right Generalized Cylinder State Model. In: Proc. IEEE Int. Conf. Image Process., pp. 2441–2444 (2006)Google Scholar
  4. 4.
    Kittler, J., Illingworth, J., Fglein, J.: Threshold selection based on a simple image statistic. Comput. Vision Graphics Image Process. 30, 125–147 (1985)CrossRefGoogle Scholar
  5. 5.
    Flórez Valencia, L., Vincent, F., Orkisz, M.: Fast 3D pre-segmentation of arteries in computed tomography angiograms. In: Int. Conf. Comput. Vision & Graphics, Warsaw, Poland, pp. 87–88 (2004)Google Scholar
  6. 6.
    Maurer Jr., C.R., Qi, R., Raghavan, V.: A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans. Pattern Anal. Mach. Intell. 25, 265–270 (2003)CrossRefGoogle Scholar
  7. 7.
    Wink, O., Niessen, W., Frangi, A., Verdonck, B., Viergever, M.: 3D MRA coronary axis determination using a minimum cost path approach. Magnetic Resonance in Medicine 47, 1169–1175 (2002)CrossRefGoogle Scholar
  8. 8.
    Sethian, J.: A Fast Marching Level Set Method for Monotonically Advancing Fronts. Proc. Nat. Acad. Sci. 93, 1591–1595 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Baltaxe Milwer, M., Flórez Valencia, L., Hernández Hoyos, M., Magnin, I., Orkisz, M.: Fast marching contours for the segmentation of vessel lumen in CTA cross-sections. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., Lyon, France, pp. 791–794. IEEE, Los Alamitos (2007)Google Scholar
  10. 10.
    Hameeteman, K., Zuluaga, M., Joskowicz, L., Freiman, M., van Walsum, T.: Carotid Lumen Segmentation and Stenosis Grading Challenge. In: MICCAI Workshop 3D Segmentation in the Clinic: a Grand Challenge, MIDAS Journal London, UK (2009), http://hdl.handle.net/10380/3128

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Leonardo Flórez Valencia
    • 1
  • Jacques Azencot
    • 2
  • Maciej Orkisz
    • 2
  1. 1.Departamento de Ingeniería de SistemasPontificia Universidad JaverianaBogotáColombia
  2. 2.Université de Lyon; Université Lyon 1; INSA-Lyon CNRS UMR5220; INSERM U630; CREATISVilleurbanneFrance

Personalised recommendations