Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

  • Johannes Feulner
  • S. Kevin Zhou
  • Matthias Hammon
  • Joachim Hornegger
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


Lymph nodes routinely need to be considered in clinical practice in all kinds of oncological examinations. Automatic detection of lymph nodes from chest CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Features extracted from a segmentation that is initialized with a detection candidate can be used to train a classifier that decides whether the detection is a true or false positive. In this paper, the graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the candidate segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross validation on 54 CT datasets showed that the proposed system reaches a detection rate of 60.9% with only 6.1 false alarms per volume image, which is better than the current state of the art of mediastinal lymph node detection.


False Alarm Mediastinal Lymph Node Edge Capacity Detection Candidate Radial Weighting 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S.K., Comaniciu, D.: Automatic detection and segmentation of axillary lymph nodes. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 28–36. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. IJCV 70, 109–131 (2006), CrossRefGoogle Scholar
  3. 3.
    Dornheim, L., Dornheim, J.: Automatische detektion von lymphknoten in ct-datensätzen des halses. In: Bildverarbeitung für die Medizin, pp. 308–312 (2008)Google Scholar
  4. 4.
    Duwe, B.V., Sterman, D.H., Musani, A.I.: Tumors of the mediastinum. Chest 128(4), 2893–2909 (2005)CrossRefGoogle Scholar
  5. 5.
    Feuerstein, M., Deguchi, D., Kitasaka, T., Iwano, S., Imaizumi, K., Hasegawa, Y., Suenaga, Y., Mori, K.: Automatic mediastinal lymph node detection in chest ct. In: SPIE Medical Imaging, Orlando, Florida, USA (February 2009)Google Scholar
  6. 6.
    Feulner, J., Zhou, S.K., Huber, M., Hornegger, J., Comaniciu, D., Cavallaro, A.: Lymph node detection in 3-d chest ct using a spatial prior probability. In: CVPR (2010)Google Scholar
  7. 7.
    Funka-lea, G., Boykov, Y., Florin, C., Jolly, M.-p., Moreau-gobard, R., Ramaraj, R., Rinck, D.: Automatic heart isolation for ct coronary visualization using graph-cuts. In: IEEE International Symposium on Biomedical Imaging (2006)Google Scholar
  8. 8.
    Greig, D.M., Porteous, B.T., Seheult, A.H.: Exact maximum a posteriori estimation for binary images. JRSS Series B 51(2), 271–279 (1989)Google Scholar
  9. 9.
    Kitasaka, T., Tsujimura, Y., Nakamura, Y., Mori, K., Suenaga, Y., Ito, M., Nawano, S.: Automated extraction of lymph nodes from 3-D abdominal CT images using 3-D minimum directional difference filter. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 336–343. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    de Langen, A.J., Raijmakers, P., Riphagen, I., Paul, M.A., Hoekstra, O.S.: The size of mediastinal lymph nodes and its relation with metastatic involvement: a meta-analysis. Eur. J. Cardiothorac. Surg. 29(1), 26–29 (2006)CrossRefGoogle Scholar
  11. 11.
    Sinop, A., Grady, L.: A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8 (2007)Google Scholar
  12. 12.
    Slabaugh, G., Unal, G.: Graph cuts segmentation using an elliptical shape prior. In: ICIP, vol. 2, pp. II–1222–II–1225 (2005)Google Scholar
  13. 13.
    Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: ICCV, vol. 2, pp. 1589–1596 (2005)Google Scholar
  14. 14.
    Veksler, O.: Star shape prior for graph-cut image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 454–467. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Johannes Feulner
    • 1
    • 3
  • S. Kevin Zhou
    • 2
  • Matthias Hammon
    • 4
  • Joachim Hornegger
    • 1
  • Dorin Comaniciu
    • 2
  1. 1.Pattern Recognition LabUniversity of Erlangen-NurembergGermany
  2. 2.Siemens Corporate ResearchPrincetonUSA
  3. 3.Siemens Corporate TechnologyErlangenGermany
  4. 4.Radiology InstituteUniversity Hospital ErlangenGermany

Personalised recommendations