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==
ure 5. Tumour, brain and skin
Fig
In the Figure 5, brain surface has been defined as translucent
and the tumour surface is opaque. Thus, it is possible to see
brain and tumour together. Here skin model had been obtained
from CT slices and the others from MR slices. Brain and
tumour surface models have been registered with 3D affine
mapping.
In Figure 6, photo realistic skin surface model after texture
mapping is shown from different angles.
Figure 6. Photo realistic skin surface model after texture
mapping
7. CONCLUSION
By using 3D models, it is easy to diagnose pathological
formations and preparing the treatment plans. With 3D models,
it will be possible to trace the progress of the diseases. Thus, for
many diseases such as Parkinson, new treatment methods may
be developed. On the other hand, a such system may be used in
plastic surgery or in dental diagnosis and treatments.
When the medical imaging and photogrammetry get closer, it
would be possible to produce new approaches and techniques
for medical purposes. By considering the photogrammetric
methods in medical imaging, some progressed changes would
be obtained on designing medical image acquisition and
evaluation systems.
8. REFERENCES
Altan, O., and Dogan, S., 2003. 3D modelling of human face
and brain by using CT, MR and digital images for finding
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
abnormalities. Optical 3D MeasurementTtechniques VI, Zurich,
Switzerland, Vol. I, pp. 148-155.
Betke, M., et al, 2001. “Automatic 3D registration of Lung
Surfaces in computed tomography scans”, http://
citeseer.nj.nec.com / betkeOlautomatic.html, (accessed March
2001).
D'apuzzo, N., 2001. Human face modeling from multi images.
Proc. Of Third International Image Sensing Seminar on New
Development in Digital Photogrammetry, Gifu, Japan, pp. 28-
20.
Dogan, S., 2003. 3D Reconstruction and evaluation of tissues
by using CT, MR slices and digital images, PH. D. Thesis, NO,
FBE, Istanbul, Turkey.
Elad, D., and Einav, S., 1996. 3D measurements of biological
surfaces, Photogrammetric Record, 45, pp. 248-265.
Fitzgibbon, A.W., 2001. Robust registration of 2D and 3D
point sets, http://citeseer.nji.nec.com/fitzgibbon01robust.html,
(accessed March 2001).
Gonzales, R., C., 1987. Digital Image Processing. Addison
Wesley Publ.Comp, USA.
Kaneko, S., et., al., 2003. Robust matching of 3D contours
using iterative closest point algorithm improved by M-
estimation, September 2003, pp. 2041-2047. http: //iris.usc.edu
/Vision-Notes/bibliography/match-pl521.html (accessed Oct.
2003).
Mitchell, H., L., Newton, 1., 2002. Medical photogrammetric
measurement: overview and prospects, ISPRS Journal of
Photogrammetry and Remote Sensing. 56, pp. 286-294.
Patias, P., 2002. Medical imaging challenges photogrammetry,
ISPRS Journal of Photogrammetry and Remote Sensing, 56, pp.
295-310.
Ruckert et., al., 1999. Nonrigid registration using free form
deformations: application to breast MR images, /EEE
Transactions on Medical Imaging, 18, pp. 712-721.
Rusinkievicz, S., 2001. Efficient variants of the ICP algorithm,
www.cs.princeton.edu/ —smr/ papers/ fasticp/ fasticp paper.pdf
(accesed March 2001).
Schewe, H., et al., 1999. PictranMed- an orthodontic
application of digital photogrammetry, Third Turkish German
Joint Geodetic Days, Istanbul, Turkey, pp. 257-261.
Teuber, J., 1993. Digital Image Processing, Prentice Hall, UK.
Watt, A., and Watt, M., 1992. Advanced Animation and
Rendering Techniques. Addison Wesley, USA.
West, J., et. al, 1997. Comparision and evaluation of
retrospective intermodality brain image registration techniques,
Journal of Computer Assisted Tomography. 21(4), pp. 554-566.
Xie, Z., and Farin, G., E., 2000. Deformation with hierarchical
B- splines, Mathematical Methods in CAGD, Oslo, Norway, pp.
1-8.