An Approach to Lumbar Vertebra CT Image Segmentation Using Contourlet Transform and ANNs

Abstract:

In this paper, we proposed a mathod for lumbar vertebra CT image segmentation based on the contourlet transform and artificial neural networks(ANNs). The proposed method consists of three portions. In the first part, contourlet transform is used to decompose the CT image to obtain the contourlet coefficients. In the second part, the self-organizing competitive artificial neural network is employed to optimize and extract the low frequency coefficients coefficients of contourlet transformation, reduce the number of coefficients greatly. The last part, the optimized coefficients are inverse contourlet transformed with the original coefficients,the segmented image is reconstructed. The experimental results show the accuracy of human lumbar vertebra CT image segmentation based on the proposed method is encouraged.

• Periodical: Advanced Materials Research (Volumes 468-471)
• Pages: 613-618
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.468-471.613
• Online since: February 2012
• Authors: Jun Min Deng, Hai Yun Li, Hao Wu
• Keywords: Contourlet Transform, Medical Image Segmentation, Self-Organizing Competitive Artificial Neural Network (ANNs)

References

[2] S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp.674-693, 1989.Shuqian Luo and Guohong Zhou, Medical image processing and analysis, Beijing: Science Press, 2003,65—70.

DOI: 10.1109/34.192463

[3] D. Donoho, Ridge functions and orthonormal ridgelets, J. Approx. Theory 111 (2) (2001) 143--179.

DOI: 10.1006/jath.2001.3568

[4] M. Do and M.Vetterli, Orthonormal Finite Ridgelet Transform for Image Compression, Proceedings. 2000 International Conference on Image Processing, vol. 2, pp.367-370, Sep 2000.

DOI: 10.1109/icip.2000.899394

[5] E. Cand'es and D. Donoho, A Surprisingly Effective Non adaptive Representation for Objects With Edges, Curves and Surfaces, Vanderbilt University Press, pp.105-120, 2000.

[6] E. Candes, Ridgelets: Theory and Application, Ph.D. thesis. Dept. of Stats, Stanford Univ., 1998.

[7] S. Alzu'bi, A. Amira, "3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches," Advances in Artificial Intelligence, vol. 2010, 2010. [Accessed Online on: Oct. 8, 2010]. http://www.hindawi.com/journals/aai/2010/520427.html.

DOI: 10.1155/2010/520427

[8] E.J. Candes D.L. Donoho,Curvelets:a surprisingly effectiven on adaptive representation for objects with edges, in:Saint-Malo Proceedings,VanderbilUniversity,Nashville,TN,2000,pp.1-10

[9] L.Semler,L.Dettori,Curvelet-based texture classication of tissues in computed tomography,in:IEEE International Conference on Image Proces-sing,2006.

DOI: 10.1109/icip.2006.312873

[10] M.N.Do,M.Vetterli,The contourlet transform:an efcient directional multi-resolution image representation, IEEE Transactions on Image Processing 14 (12)(2005)2091-2106

DOI: 10.1109/tip.2005.859376

[11] M.N.Do and M.Vetterli, "The contourlet transform: An efficient directional multi resolution image representation",IEEE Trans.Image Process.,vol.14,no.12,pp.2091-2106,Dec.2005.-

DOI: 10.1109/tip.2005.859376

[12] P.J. Burt and E.H. Adelson, "The Laplacian pyramid as a compact image code",IEEE Trans.Commun.,vol.31,no.4,pp.532-540,Apr.(1983)

DOI: 10.1109/tcom.1983.1095851

[13] R.H. Bamberger and M.J.T. Smith, "A filter bank for the directional decomposition of images:Theory and design", IEEE Trans. Signal Process.,vol.40,no.4,pp.882-893,Apr.1992.

DOI: 10.1109/78.127960

[14] Duncan D.-Y. Po,Minh N.Do,Member. "Directional Multiscale Modeling of Images Using the Contourlet Transform".IEEE Trans on Image Proc,2006.

DOI: 10.1109/tip.2006.873450

[15] Xin Wen and Lu Zhou, MATLAB neural network applications.Beijing.Science and Technology Press, 2000.271 ~ 275.

[16] ArthurL.da Cunha,Jianping Zhou, The Non subsampled Contourlet Transform: Theory, Design, and Applications. IEEE,TRANS, ON IMAGE PROCESSING,VOL.15, NO.10 pp.3089-3101, ,OCTOBER2006

DOI: 10.1109/tip.2006.877507