Validation of Fuzzy Connectedness Segmentation for Jaw Tissues

  • Roberto Lloréns
  • Valery Naranjo
  • Miriam Clemente
  • Mariano Alcañiz
  • Salvador Albalat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

Most of the dental implant planning systems implement 3D reconstructions of the CT-data in order to achieve more intuitive interfaces. This way, the dentists or surgeons can handle the patient’s virtual jaw in the space and plan the location, orientation and some other features of the implant from the orography and density of the jaw. The segmentation of the jaw tissues (the cortical bone, the trabecular core and the mandibular channel) is critical for this process, because each one has different properties and in addition, because an injury of the channel in the surgery may cause lip numbness. Current programs don’t carry out the segmentation process or just do it by hard thresholding or by means of exhaustive human interaction. This paper deals with the validation of fuzzy connectedness theory for the automated, accurate and time efficient segmentation of jaw tissues.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Reiser, G.M., Manwaring, J.D., Damoulis, P.D.: Clinical significance of the structural integrity of the superior aspect of the mandibular canal. Journal of Periodontology 75(2), 322–326 (2004)CrossRefGoogle Scholar
  2. 2.
    Verstreken, K., Cleynenbreugel, J.V., Martens, K., Marchal, G., van Steenberghe, D., Suetens, P.: An image-guided planning system for endosseous oral implants. IEEE Transactions on Medical Imaging 17(5), 842–852 (1998)CrossRefGoogle Scholar
  3. 3.
    Galanis, C.C., Sfantsikopoulos, M.M., Koidis, P.T., Kafantaris, N.M., Mpikos, P.G.: Computer methods for automating preoperative dental implant planning: Implant positioning and size assignment. Computer Methods and Programs in Biomedicine 86(1), 30–38 (2007)CrossRefGoogle Scholar
  4. 4.
    Fütterling, S., Klein, R., Straßer, W., Weber, H.: Automated finite element modeling of a human mandible with dental implants (1998)Google Scholar
  5. 5.
    Stein, W., Hassfeld, S., Muhling, J.: Tracing of thin tubular structures in computer tomographic data. Computer Aided Surgery 3 (1998)Google Scholar
  6. 6.
    Kršek, P., Krupa, P., Cernochová, P.: Teeth and jaw 3D reconstruction in stomatology. In: Medical Information Visualisation - BioMedical Visualisation. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  7. 7.
    Kang, H., Pinti, A., Vermeiren, L., Taleb-Ahmed, A., Zeng, X.: An automatic FCM-based method for tissue classification. Bioinformatics and Biomedical EngineeringGoogle Scholar
  8. 8.
    DeBruijne, B., Ginneken, B.V., Niessen, W., Viergever, M., Wiro, J.: Adapting active shape models for 3D segmentation of tubular structures in medical images. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 136–147. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Rueda, S., Gil, J.A., Pichery, R., Niz, M.A.: Automatic segmentation of jaw tissues in CT using active appearance models and semi-automatic landmarking. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 167–174. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Udupa, J.K., Wei, L., Samaraeskera, S., Miki, Y., van Buchem, M.A., Grossman, R.I.: Multiple sclerosis lesion quantification using fuzzy connectedness principles. IEEE Trans. Medical Imaging 16Google Scholar
  11. 11.
    Udupa, J.K., Odhner, D., Tian, J., Holland, G., Axel, L.: Automatic clutter-free volume rendering for MR angiography using fuzzy connectedness. In: SPIE Proceedings Medical Imaging, vol. 3034Google Scholar
  12. 12.
    Udupa, J.K., Tian, J., Hemmy, D., Tessier, P.: A pentium PC-based craniofacial 3D imaging and analysis system. J. Craniofacial Surgery 8Google Scholar
  13. 13.
    Udupa, J., Samarasekera, S.: Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. Graphical Models and Image Processing 58(3), 246–261 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Roberto Lloréns
    • 1
  • Valery Naranjo
    • 1
  • Miriam Clemente
    • 1
  • Mariano Alcañiz
    • 1
  • Salvador Albalat
    • 1
  1. 1.LabHuman - Human Centered TechnologyUniversidad Politécnica de ValenciaSpain

Personalised recommendations