A 3D Matching Method for Organic Training Samples Alignment Based on Surface Curvature Distribution

DOI: 10.4236/ojmi.2011.12006   PDF   HTML     2,965 Downloads   5,840 Views   Citations


The fundamental step to get a Statistical Shape Model (SSM) is to align all the training samples to the same spatial modality. In this paper, we propose a new 3D alignment method for organic training samples matching, whose modalities are orientable and surface figures could be recognized. It is a feature based alignment method which matches two models depending on the distribution of surface curvature. According to the affine transformation on 2D Gaussian map, the distances between the corresponding parts on surface could be minimized. We applied our proposed method on 5 cases left lung training samples alignment and 4 cases liver training samples alignment. The experiment results were performed on the left lung training samples and the liver training samples. The availability of proposed method was confirmed.

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G. Li, H. Kim, J. Tan, S. Ishikawa and A. Yamamoto, "A 3D Matching Method for Organic Training Samples Alignment Based on Surface Curvature Distribution," Open Journal of Medical Imaging, Vol. 1 No. 2, 2011, pp. 43-47. doi: 10.4236/ojmi.2011.12006.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. T. Heimann and H. Meinzer, “Statistical Shape Models for 3D Medical Image Segmentation: A review,” Medical Image Analysis, Vol. 13, No. 4, 2009, pp. 543-563. doi:10.1016/j.media.2009.05.004
[2] T. F. Cootes and C. J. Taylor, “Statistical Models of Appearance for Medical Image Analysis and Computer Vision,” SPIE Medical Imaging, Vol. 4322, No. 3, 2001, pp. 236-248.
[3] J. B. A. Maintz and M. A. Viergever, “A Survey of Medical Image Registration,” Medical Image Analysis, Vol. 2, No. 1, 1998, pp. 1-36. doi:10.1016/S1361-8415(98)80001-7
[4] M. A. Audette, F. P. Ferrie and T. M. Peters, “An Algorithmic Overview of Surface Registration Techniques for Medical Imaging,” Medical Image Analysis, Vol. 4, No. 3, 2000, pp. 201-217. doi:10.1016/S1361-8415(00)00014-1
[5] W. E. Lorensen and H. E. Cline, “Marching Cubes: A High Resolution 3D Surface Construction Algorithm,” ACM SIGGRAPH Computer Graphics, Vol. 21, No. 4, 1987, pp.163-169. doi:10.1145/37402.37422
[6] D. S. Meek and D. J. Walton, “On Surface Normal and Gaussian Curvature Approximations Given Data Sampled from Smooth Surface,” Computer Aided Geometric Design, Vol. 17, No. 6, 2000, pp. 521-543. doi:10.1016/S0167-8396(00)00006-6
[7] X. F. D. Gu and S.-T. Yau, “Computational Conformal Geometry,” Higher Education Press, Beijing, 2008, pp. 90-95.
[8] T. Kanungo, N. S. Netanyahu and A. Y. Wu, “An Efficient K-Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, 2002, pp. 881-892. doi:10.1109/TPAMI.2002.1017616
[9] B. K. P. Horn, “Closed-Form Solution of Absolute Orientation Using Unit Quaternions,” Journal of the Optical Society of America A, Vol. 4, No. 4, 1987, pp. 629-642. doi:10.1364/JOSAA.4.000629
[10] T. Zrimec, S. Busayarat and P. Wilson, “A 3D Model of the Human Lung,” Medical Image Computing and Computer-Assisted Intervention, Vol. 3217, 2004, pp. 1074-1075. doi:10.1007/978-3-540-30136-3_143
[11] X. Gu, Y. Wang, T. F. Chan, P. M. Thompson and S. Yau, “Genus Zero Surface Conformal Mapping and Its Application to Brain Surface Mapping,” IEEE Transactions on Medical Imaging, Vol. 23, No. 8, 2004, pp. 949-958. doi:10.1109/TMI.2004.831226

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