About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 928469, 12 pages
http://dx.doi.org/10.1155/2013/928469
Research Article

The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation

1Biomedical Engineering Department, Middle East Technical University, 06800 Ankara, Turkey
2Electrical Engineering Department, Middle East Technical University, 06800 Ankara, Turkey

Received 7 March 2013; Accepted 5 June 2013

Academic Editor: Marco Perez-Cisneros

Copyright © 2013 Yousef Rezaei Tabar and Ilkay Ulusoy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. J. Xia, J. Gateno, J. F. Teichgraeber et al., “Accuracy of the computer-aided surgical simulation (CASS) system in the treatment of patients with complex craniomaxillofacial deformity: a pilot study,” Journal of Oral and Maxillofacial Surgery, vol. 65, no. 2, pp. 248–254, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Rohlfing, R. Brandt, R. Menzel, D. B. Russakoff, and C. R. Maurer, “Quo vadis, atlas-based segmentation?” in Handbook of Biomedical Image Analysis, vol. 3 of Registration models, pp. 435–486, Springer, 2005.
  3. K. Held, E. R. Kops, B. J. Krause, W. M. Wells, R. Kikinis, and H.-W. Müller-Gärtner, “Markov random field segmentation of brain MR images,” IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp. 878–886, 1997. View at Scopus
  4. Y. Rezaeitabar and I. Ulusoy, “Automatic 3D segmentation of individual facial muscles using unlabeled prior information,” International Journal of Computer Assisted Radiology and Surgery, vol. 7, no. 1, pp. 35–41, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. M. E. Farrugia, G. M. Bydder, J. M. Francis, and M. D. Robson, “Magnetic resonance imaging of facial muscles,” Clinical Radiology, vol. 62, no. 11, pp. 1078–1086, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Altaye, S. K. Holland, M. Wilke, and C. Gaser, “Infant brain probability templates for MRI segmentation and normalization,” NeuroImage, vol. 43, no. 4, pp. 721–730, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Aljabar, R. A. Heckemann, A. Hammers, J. V. Hajnal, and D. Rueckert, “Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy,” NeuroImage, vol. 46, no. 3, pp. 726–738, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Lorenzo-Valdés, G. I. Sanchez-Ortiz, A. G. Elkington, R. H. Mohiaddin, and D. Rueckert, “Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm,” Medical Image Analysis, vol. 8, no. 3, pp. 255–265, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. E. M. van Rikxoort, M. Prokop, B. de Hoop, M. A. Viergever, J. P. W. Pluim, and B. Van Ginneken, “Automatic segmentation of pulmonary lobes robust against incomplete fissures,” IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1286–1296, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Akselrod-Ballin, M. Galun, J. M. Gomori, A. Brandt, and R. Basri, “Prior knowledge driven multiscale segmentation of brain MRI,” Medical Image Computing and Computer-Assisted Intervention, vol. 10, no. 2, pp. 118–126, 2007. View at Scopus
  11. N. Ray, S. T. Acton, T. Altes, E. E. De Lange, and J. R. Brookeman, “Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation,” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 189–199, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. E. M. van Rikxoort, I. Isgum, Y. Arzhaeva et al., “Adaptive local multi-atlas segmentation: application to the heart and the caudate nucleus,” Medical Image Analysis, vol. 14, no. 1, pp. 39–49, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Riklin Raviv, K. van Leemput, W. M. Wells III, and P. Golland, “Joint segmentation of image ensembles via latent atlases,” Medical Image Computing and Computer-Assisted Intervention, vol. 12, no. 1, pp. 272–280, 2009. View at Scopus
  14. T. N. Pappas and N. S. Jayant, “Adaptive clustering algorithm for image segmentation,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP '89), vol. 3, pp. 1667–1670, May 1989. View at Scopus
  15. P. P. Wyatt and J. A. Noble, “MAP MRF joint segmentation and registration of medical images,” Medical Image Analysis, vol. 7, no. 4, pp. 539–552, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. N. Richard, M. Dojat, and C. Garbay, “Distributed Markovian segmentation: application to MR brain scans,” Pattern Recognition, vol. 40, no. 12, pp. 3467–3480, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Liu, D. L. Langer, M. A. Haider, Y. Yang, M. N. Wernick, and I. Š. Yetik, “Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class,” IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 906–915, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Zhang, M. Brady, and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Transactions on Medical Imaging, vol. 20, no. 1, pp. 45–57, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “Automated model-based tissue classification of MR images of the brain,” IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 897–908, 1999. View at Scopus
  20. S. P. Awate, T. Tasdizen, N. Foster, and R. T. Whitaker, “Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification,” Medical Image Analysis, vol. 10, no. 5, pp. 726–739, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Sotiras, N. Komodakis, G. Langs, and N. Paragios, “Atlas-based deformable mutual population segmentation,” in Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09), pp. 5–8, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Young, G. Tabor, T. Collins, J. Richterova, and E. Dejuniat, “Automating the generation of 3D finite element models based on medical imaging data Ultrasound,” Tech. Rep. 2006-01-2371, 2005. View at Publisher · View at Google Scholar
  23. G. G. Barbarino, M. Jabareen, J. Trzewik, A. Nkengne, G. Stamatas, and E. Mazza, “Development and validation of a three-dimensional finite element model of the face,” Journal of Biomechanical Engineering, vol. 131, no. 4, Article ID 041006, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. M. E. Farrugia, M. D. Robson, L. Clover et al., “MRI and clinical studies of facial and bulbar muscle involvement in MuSK antibody-associated myasthenia gravis,” Brain, vol. 129, no. 6, pp. 1481–1492, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Olszewski, Y. Liu, T. Duprez, T. M. Xu, and H. Reychler, “Three-dimensional appearance of the lips muscles with three-dimensional isotropic MRI: in vivo study,” International Journal of Computer Assisted Radiology and Surgery, vol. 4, no. 4, pp. 349–352, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. 3D Slicer Open-Source Software, Harvard Medical School, Boston, Mass, USA.
  27. H. P. Ng, S. H. Ong, S. Huang et al., “Salient features useful for the accurate segmentation of masticatory muscles from minimum slices subsets of magnetic resonance images,” Machine Vision and Applications, vol. 21, no. 4, pp. 449–467, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. H. P. Ng, J. Liu, S. Huang et al., “An improved shape determinative slice determination method for patient-specific modeling of facial anatomical structure,” International Journal of Computer Assisted Radiology and Surgery, vol. 3, no. 3-4, pp. 221–230, 2008. View at Scopus
  29. H. P. Ng, Q. M. Hu, S. H. Ong et al., “Segmentation of the temporalis muscle from MR data,” International Journal of Computer Assisted Radiology and Surgery, vol. 2, no. 1, pp. 19–30, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Majeed, K. Fundana, M. Luthi, S. Kiriyanthan, J. Beinemann, and P. C. Cattin, “Graph cut segmentation using a constrained statistical model with non-linear and sparse shape optimization,” in Proceedings of the Workshop on Medical Computer Vision (MICCAI '12, vol. 7766, pp. 38–47, January 2012. View at Scopus
  31. T. Majeed, K. Fundana, M. Luthi, S. Kiriyanthan, J. Beinemann, and P. C. Cattin, “Using a flexibility constrained 3D statistical shape model for robust MRF-based segmentation,” in Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '12), pp. 57–64, January 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. “Alzheimer’s Disease Neuroimaging Initiative (ADNI),” http://adni.loni.ucla.edu/.
  33. J.-P. Thirion, “Image matching as a diffusion process: an analogy with Maxwell's demons,” Medical Image Analysis, vol. 2, no. 3, pp. 243–260, 1998. View at Scopus
  34. Berlin Amira 3-D scientific visualization and data analysis package (ZIB, Germany, Indeed—Visual Concepts GmbH, Berlin, Germany, TGS Inc., San Diego, CA).
  35. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Longman, Boston, Mass, USA, 2001.
  36. J. M. Hammersley and P. Clifford, “Markov field on finite graphs and lattices,” 1971.
  37. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, pp. 297–302, 1945.