Automated Kidney Detection and Segmentation in 3D Ultrasound

  • Matthias Noll
  • Xin Li
  • Stefan Wesarg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8361)


Ultrasound provides the physical capabilities for a fast and save disease diagnosis in various medical scenarios including renal exams and patient trauma assessment. However, the experience of the ultrasound operator is the key element in performing ultrasound diagnosis. Thus, we like to introduce our automatic kidney detection and segmentation algorithm for 3D ultrasound. The approach utilizes basic kidney shape information to detect the kidney position. Following, the Level Set algorithm is applied to segment the detection result. In combination this method may help physicians and inexperienced trainees to achieve kidney detection and segmentation for diagnostic purposes.


Ultrasound Image analysis Kidney Shape prior  Detection Segmentation 


  1. 1.
    Aliakseyeu, D., Subramanian, S., Martens, J.B., Rauterberg, M.: Interaction techniques for navigation through and manipulation of 2d and 3d data. In: Proceedings of the Workshop on Virtual Environments, EGVE ’02, pp. 179–188. Eurographics Association, Aire-la-Ville, Switzerland, Switzerland (2002)Google Scholar
  2. 2.
    Chen, T.F.: Medical Image Segmentation Using Level Sets, pp. 1–8 (2008)Google Scholar
  3. 3.
    Contreras Ortiz, S.H., Chiu, T., Fox, M.D.: Ultrasound image enhancement: a review. Biomed. Signal Process. Control 7(5), 419–428 (2012)CrossRefGoogle Scholar
  4. 4.
    Forcadel, N., Guyader, C., Gout, C.: Generalized fast marching method: applications to image segmentation 48(1–3), 189–211 (2008)zbMATHGoogle Scholar
  5. 5.
    Hellier, P., Coupe, P., Meyer, P., Morandi, X., Collins, D.: Acoustic shadows detection, application to accurate reconstruction of 3d intraoperative ultrasound. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 1569–1572 (2008)Google Scholar
  6. 6.
    Hiransakolwong, N., Hua, K.A., Vu, K., Windyga, P.S.: Segmentation of ultrasound liver images: an automatic approach. In: Proceedings of the 2003 International Conference on Multimedia and Expo - ICME ’03, vol. 2, pp. 573–576. IEEE Computer Society, Washington, DC (2003)Google Scholar
  7. 7.
    Loizou, C., Pattichis, C., Istepanian, R., Pantziaris, M., Kyriakou, E., Tyllis, T., Nicolaides, A.: Ultrasound image quality evaluation. In: 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 138–141 (2003)Google Scholar
  8. 8.
    Peng, B., Wang, Y., Yang, X.: A multiscale morphological approach to local contrast enhancement for ultrasound images. In: Proceedings of the 2010 International Conference on Computational and Information Sciences, ICCIS ’10, pp. 1142–1145. IEEE Computer Society, Washington, DC (2010).Google Scholar
  9. 9.
    Xie, J., Jiang, Y., Tsui, H.T.: Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans. Med. Imag. 24(1), 45–57 (2005)Google Scholar
  10. 10.
    Zong, X., Laine, A.F., Geiser, E.A.: Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Trans. Med. Imag. 17(4), 532–540 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Cognitive Computing and Medical ImagingFraunhofer IGDDarmstadtGermany

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