Journal of International Society for Simulation Surgery

International Society for Simulation Surgery (국제컴퓨터가상수술학회)
권호 표지
DOI QR코드

DOI QR Code

Generation and Detection of Cranial Landmark

  • Heo, Suwoong (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Kang, Jiwoo (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Kim, Yong Oock (Department of Plastic & Reconstructive Surgery, Yonsei University College of Medicine) ;
  • Lee, Sanghoon (Department of Electrical & Electronic Engineering, Yonsei University)
  • Received : 2015.05.12
  • Accepted : 2015.05.25
  • Published : 2015.06.10
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose When a surgeon examines the morphology of skull of patient, locations of craniometric landmarks of 3D computed tomography(CT) volume are one of the most important information for surgical purpose. The locations of craniometric landmarks can be found manually by surgeon from the 3D rendered volume or 2D sagittal, axial, and coronal slices which are taken by CT. Since there are many landmarks on the skull, finding these manually is time-consuming, exhaustive, and occasionally inexact. These inefficiencies raise a demand for a automatic localization technique for craniometric landmark points. So in this paper, we propose a novel method through which we can automatically find these landmark points, which are useful for surgical purpose. Materials and Methods At first, we align the experimental data (CT volumes) using Frankfurt Horizontal Plane (FHP) and Mid Sagittal Plane(MSP) which are defined by 3 and 2 cranial landmark points each. The target landmark of our experiment is the anterior nasal spine. Prior to constructing a statistical cubic model which would be used for detecting the location of the landmark from a given CT volume, reference points for the anterior nasal spine were manually chosen by a surgeon from several CT volume sets. The statistical cubic model is constructed by calculating weighted intensity means of these CT sets around the reference points. By finding the location where similarity function (squared difference function) has the minimal value with this model, the location of the landmark can be found from any given CT volume. Results In this paper, we used 5 CT volumes to construct the statistical cubic model. The 20 CT volumes including the volumes, which were used to construct the model, were used for testing. The range of age of subjects is up to 2 years (24 months) old. The found points of each data are almost close to the reference point which were manually chosen by surgeon. Also it has been seen that the similarity function always has the global minimum at the detection point. Conclusion Through the experiment, we have seen the proposed method shows the outstanding performance in searching the landmark point. This algorithm would make surgeons efficiently work with morphological informations of skull. We also expect the potential of our algorithm for searching the anatomic landmarks not only cranial landmarks.

Keywords

References

  1. Subburaj K, Ravi B, Agarwal M. "Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images." Computerized Medical Imaging and Graphics 2009;33:359-68, https://doi.org/10.1016/j.compmedimag.2009.03.001
  2. Yue W, Yin D, Li C, Wang G, Xu T. "Automated 2-D cephalometric analysis on X-ray images by a model-based approach." IEEE Transactions on Biomedical Engineering 2006;53(8):1615-1623 https://doi.org/10.1109/TBME.2006.876638
  3. Cheng E, Chen J, Yang J, Deng H, Wu Y, et al. "Automatic Dentlandmark detection in 3-D CBCT dental volumes." In: EMBC 2011; 2011. p.6204-6207
  4. Kaur A, Singh C. "Automatic cephalometric landmark detection using zernike moments and template matching." Signal, Image and Video Processing;2013. p.1-16
  5. Ibragimov B, Likar B, Pernus F, Vrtovec T. "Automatic cephalometric X-ray landmark detection by applying game theory and random forests." In Proceedings ISBI 2014 Grand Challenge: Automatic Cephalometric X-Ray Landmark Detection Challenge;2014. p.1-8
  6. Grau V, Alcaniz M, Juan MC, Monserrat C, Knoll C. "Automatic localization of cephalometric landmarks" Journal of Biomedical Informations 2001;34:146-156 https://doi.org/10.1006/jbin.2001.1014
  7. Saad AA, El-Bialy A, Kandil AH, Sayed AA. "Automatic cephalometric analysis using active appearance model and simulated annealing." Proceedings of the International Conference on Graphics. Vision and Image Processing (GVIP05) 2005:51-67
  8. Cavalcanti MGP, Rocha SS, Vannier MW. "Craniofacial measurements based on 3D-CT volume rendering: implications for clinical applications." Dentomaxillofacial Radiology 2004;33:170-176 https://doi.org/10.1259/dmfr/13603271
  9. Breiman L. "Random forests." Machine learning 2001;45(1):5-32 https://doi.org/10.1023/A:1010933404324
  10. Lin YH, Chen CH. "Template matching using the parametric template vector with translation, rotation and scale invariance." Pattern Recognition 2008;41(7):2413-2421 https://doi.org/10.1016/j.patcog.2008.01.017
  11. Anderson D, Anderson L, Glanze W. Mosby's Medical Dictionary. Mosby St. Louis;2009. p.233-234
  12. Cheng Y, Leow WK, Lim TC. "Automatic identification of Frankfurt plane and mid-sagittal plane of skull," in 2012 IEEE Workshop on the Applications of Computer Vision;2012. p.233-238
  13. Kim HY, Araujo SA, Sidnei Alves. "Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast." Advances in Image and Video Technology. Springer Berlin Heidelberg; 2007. p.100-113