Localisation of Anatomical Soft Tissue Landmarks of the Head in CT Images
In this paper, algorithms for the automatic localisation
of two anatomical soft tissue landmarks of the head the medial
canthus (inner corner of the eye) and the tragus (a small, pointed,
cartilaginous flap of the ear), in CT images are describet. These
landmarks are to be used as a basis for an automated image-to-patient
registration system we are developing. The landmarks are localised
on a surface model extracted from CT images, based on surface
curvature and a rule based system that incorporates prior knowledge
of the landmark characteristics. The approach was tested on a dataset
of near isotropic CT images of 95 patients. The position of the
automatically localised landmarks was compared to the position of
the manually localised landmarks. The average difference was 1.5
mm and 0.8 mm for the medial canthus and tragus, with a maximum
difference of 4.5 mm and 2.6 mm respectively.The medial canthus
and tragus can be automatically localised in CT images, with
performance comparable to manual localisation
[1] P. A. Woerdeman, P. W. Willems, H. J. Noordmans, C. A. Tulleken and
J. W. van der Sprenkel, "Application accuracy in frameless imageguided
neurosurgery: A comparison study of three patient-to-image
registration methods," J. Neurosurg., vol. 106, pp. 1012-1016, Jun, 2007.
[2] S. Wolfsberger, K. Rossler, R. Regatschnig and K. Ungersbock,
"Anatomical landmarks for image registration in frameless stereotactic
neuronavigation," Neurosurg. Rev., vol. 25, pp. 68-72, Mar, 2002.
[3] M. Gooroochurn, M. Ovinis, D. Kerr, K. Bouazza-Marouf and M.
Vloeberghs, "A Registration Framework for Preoperative CT to
Intraoperative White Light Images," Proc. Medical Image
Understanding and Analysis, pp. 184-188, 2009.
[4] M. Gooroochurn, D. Kerr, K. Bouazza-Marouf; M.Ovinis, " Facial
recognition techniques applied to the automated registration of patients
in the emergency treatment of head injuries," Proceedings of the
Institution of Mechanical Engineers, Part H: Journal of Engineering in
Medicine (in press).
[5] M. Gooroochurn, D. Kerr, K. Bouazza-Marouf and M. Vloeberghs,
"Extraction of Craniofacial Landmarks for Preoperative to Intraoperative
Registration," in Sixth International Conference on Signal and Image
Processing, .
[6] S. Frantz, K. Rohr and H. S. Stiehl, "Localization of 3D Anatomical
Point Landmarks in 3D Tomographic Images Using Deformable
Models," Lecture Notes In Computer Science, pp. 492-501, 2000.
[7] S. Frantz, K. Rohr and H. S. Stiehl, "Development and validation of a
multi-step approach to improved detection of 3D point landmarks in
tomographic images," Image Vision Comput., vol. 23, pp. 956-971,
2005.
[8] S. Wörz and K. Rohr, "Localization of anatomical point landmarks in
3D medical images by fitting 3D parametric intensity models," Med.
Image Anal., vol. 10, pp. 41-58, 2006.
[9] K. Rohr, H. Stiehl, R. Sprengel, W. Beil, T. Buzug, J. Weese and M.
Kuhn, "Point-based elastic registration of medical image data using
approximating thin-plate splines," in Visualization in Biomedical
Computing, 1996, pp. 297-306.
[10] M. Alker, S. Frantz, K. Rohr and H. Siegfried Stiehl, "Improving the
robustness in extracting 3D point landmarks from 3D medical images
using parametric deformable models," in Medical Image Computing and
Computer-Assisted Intervention-MICCAI 2001, 2001, pp. 582-590.
[11] K. Subburaj, B. Ravi and M. Agarwal, "Automated identification of
anatomical landmarks on 3D bone models reconstructed from CT scan
images," Comput. Med. Imaging Graphics, vol. 33, pp. 359-368, 2009.
[12] M. Ester, H. P. Kriegel, J. Sander and X. Xu, "A density-based
algorithm for discovering clusters in large spatial databases with noise,"
in Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining,
Portland, OR, AAAI Press, 1996, pp. 226-231.
[13] P. Li, B. D. Corner and S. Paquette, "Automatic landmark extraction
from three-dimensional head scan data," in Proceedings of SPIE, 2002,
pp. 169.
[14] T. D. Gatzke and C. M. Grimm, "Estimating curvature on triangular
meshes," Int. J. Shape Model., vol. 12, pp. 1, 2006.
[15] S. Rusinkiewicz, "Estimating curvatures and their derivatives on triangle
meshes," in 3D Data Processing, Visualization and Transmission, 2004.
3DPVT 2004. Proceedings. 2nd International Symposium on, 2004, pp.
486-493.
[16] D. Cohen-Steiner and J. M. Morvan, "Restricted delaunay triangulations
and normal cycle," in Proceedings of the Nineteenth Annual Symposium
on Computational Geometry, 2003, pp. 312-321.
[17] P. Alliez, D. Cohen-Steiner, O. Devillers, B. Lévy and M. Desbrun,
"Anisotropic polygonal remeshing," 2003.
[18] N. Otsu, "A threshold selection method from gray-level histograms,"
Automatica, vol. 11, pp. 285-296, 1975.
[19] The MathWorks, Source Code for the Matlab Isosurface Function. 2007.
[1] P. A. Woerdeman, P. W. Willems, H. J. Noordmans, C. A. Tulleken and
J. W. van der Sprenkel, "Application accuracy in frameless imageguided
neurosurgery: A comparison study of three patient-to-image
registration methods," J. Neurosurg., vol. 106, pp. 1012-1016, Jun, 2007.
[2] S. Wolfsberger, K. Rossler, R. Regatschnig and K. Ungersbock,
"Anatomical landmarks for image registration in frameless stereotactic
neuronavigation," Neurosurg. Rev., vol. 25, pp. 68-72, Mar, 2002.
[3] M. Gooroochurn, M. Ovinis, D. Kerr, K. Bouazza-Marouf and M.
Vloeberghs, "A Registration Framework for Preoperative CT to
Intraoperative White Light Images," Proc. Medical Image
Understanding and Analysis, pp. 184-188, 2009.
[4] M. Gooroochurn, D. Kerr, K. Bouazza-Marouf; M.Ovinis, " Facial
recognition techniques applied to the automated registration of patients
in the emergency treatment of head injuries," Proceedings of the
Institution of Mechanical Engineers, Part H: Journal of Engineering in
Medicine (in press).
[5] M. Gooroochurn, D. Kerr, K. Bouazza-Marouf and M. Vloeberghs,
"Extraction of Craniofacial Landmarks for Preoperative to Intraoperative
Registration," in Sixth International Conference on Signal and Image
Processing, .
[6] S. Frantz, K. Rohr and H. S. Stiehl, "Localization of 3D Anatomical
Point Landmarks in 3D Tomographic Images Using Deformable
Models," Lecture Notes In Computer Science, pp. 492-501, 2000.
[7] S. Frantz, K. Rohr and H. S. Stiehl, "Development and validation of a
multi-step approach to improved detection of 3D point landmarks in
tomographic images," Image Vision Comput., vol. 23, pp. 956-971,
2005.
[8] S. Wörz and K. Rohr, "Localization of anatomical point landmarks in
3D medical images by fitting 3D parametric intensity models," Med.
Image Anal., vol. 10, pp. 41-58, 2006.
[9] K. Rohr, H. Stiehl, R. Sprengel, W. Beil, T. Buzug, J. Weese and M.
Kuhn, "Point-based elastic registration of medical image data using
approximating thin-plate splines," in Visualization in Biomedical
Computing, 1996, pp. 297-306.
[10] M. Alker, S. Frantz, K. Rohr and H. Siegfried Stiehl, "Improving the
robustness in extracting 3D point landmarks from 3D medical images
using parametric deformable models," in Medical Image Computing and
Computer-Assisted Intervention-MICCAI 2001, 2001, pp. 582-590.
[11] K. Subburaj, B. Ravi and M. Agarwal, "Automated identification of
anatomical landmarks on 3D bone models reconstructed from CT scan
images," Comput. Med. Imaging Graphics, vol. 33, pp. 359-368, 2009.
[12] M. Ester, H. P. Kriegel, J. Sander and X. Xu, "A density-based
algorithm for discovering clusters in large spatial databases with noise,"
in Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining,
Portland, OR, AAAI Press, 1996, pp. 226-231.
[13] P. Li, B. D. Corner and S. Paquette, "Automatic landmark extraction
from three-dimensional head scan data," in Proceedings of SPIE, 2002,
pp. 169.
[14] T. D. Gatzke and C. M. Grimm, "Estimating curvature on triangular
meshes," Int. J. Shape Model., vol. 12, pp. 1, 2006.
[15] S. Rusinkiewicz, "Estimating curvatures and their derivatives on triangle
meshes," in 3D Data Processing, Visualization and Transmission, 2004.
3DPVT 2004. Proceedings. 2nd International Symposium on, 2004, pp.
486-493.
[16] D. Cohen-Steiner and J. M. Morvan, "Restricted delaunay triangulations
and normal cycle," in Proceedings of the Nineteenth Annual Symposium
on Computational Geometry, 2003, pp. 312-321.
[17] P. Alliez, D. Cohen-Steiner, O. Devillers, B. Lévy and M. Desbrun,
"Anisotropic polygonal remeshing," 2003.
[18] N. Otsu, "A threshold selection method from gray-level histograms,"
Automatica, vol. 11, pp. 285-296, 1975.
[19] The MathWorks, Source Code for the Matlab Isosurface Function. 2007.
@article{"International Journal of Medical, Medicine and Health Sciences:60927", author = "M. Ovinis and D. Kerr and K. Bouazza-Marouf and M. Vloeberghs", title = "Localisation of Anatomical Soft Tissue Landmarks of the Head in CT Images", abstract = "In this paper, algorithms for the automatic localisation
of two anatomical soft tissue landmarks of the head the medial
canthus (inner corner of the eye) and the tragus (a small, pointed,
cartilaginous flap of the ear), in CT images are describet. These
landmarks are to be used as a basis for an automated image-to-patient
registration system we are developing. The landmarks are localised
on a surface model extracted from CT images, based on surface
curvature and a rule based system that incorporates prior knowledge
of the landmark characteristics. The approach was tested on a dataset
of near isotropic CT images of 95 patients. The position of the
automatically localised landmarks was compared to the position of
the manually localised landmarks. The average difference was 1.5
mm and 0.8 mm for the medial canthus and tragus, with a maximum
difference of 4.5 mm and 2.6 mm respectively.The medial canthus
and tragus can be automatically localised in CT images, with
performance comparable to manual localisation
", keywords = "Anatomical soft tissue landmarks, automatic localisation, Computed Tomography (CT)", volume = "4", number = "9", pages = "481-7", }
