A Serial Hierarchical Support Vector Machine and 2D Feature Sets Act for Brain DTI Segmentation
Serial hierarchical support vector machine (SHSVM)
is proposed to discriminate three brain tissues which are white matter
(WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM
has novel classification approach by repeating the hierarchical
classification on data set iteratively. It used Radial Basis Function
(rbf) Kernel with different tuning to obtain accurate results. Also as
the second approach, segmentation performed with DAGSVM
method. In this article eight univariate features from the raw DTI data
are extracted and all the possible 2D feature sets are examined within
the segmentation process. SHSVM succeed to obtain DSI values
higher than 0.95 accuracy for all the three tissues, which are higher
than DAGSVM results.
<p>[1] E.D. Angelini, T. Song, B.D. Mensh, and A.F. Laine, “Brain MRI
segmentation with multiphase minimal partitioning: A comparative
study,” Int. Journal of Biomedical Imaging, vol. 2007, pp. 1–15.
[2] G. Heckenberg, Y. Xi, Y. Duan, and J. Hua, ”Brain structure
segmentation from MRI by geometric surface flow,” International
Journal of Biomedical Imaging, vol. 2006, pp. 1–6, 2006.
[3] O. Commowick, S.K. Warfield, “A continuous STAPLE for scalar,
vector, and tensor images: An application to DTI analysis,” IEEE
Transactions on Medical Imaging, vol. 28 , pp. 838–846, 2009.
[4] C. Lenglet, M. Rousson, and R. Deriche, "DTI segmentation by
statistical surface evolution", IEEE Trans. on Medical Imaging, vol. 25,
pp. 685–700, 2006
[5] I. Steinwart, A. Christmann, “Support Vector Machines (Book style),”
Springer, LA, USA, 2008, pp. 112-116.
[6] Y. Washizawa, “Feature extraction using constrained approximation and
suppression,” IEEE Trans. on Neural Networks, vol. 21, pp. 201–210,
2010.
[7] S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited:
A stepwise procedure for building and training a neural network,” in
Neurocomputing: Algorithms, Architectures and Applications, J.
Fogelman, Ed. New York: Springer-Verlag, 1990.
[8] J. Friedman. Another approach to polychotomous classification. Dept.
Statist., Stanford Univ., Stanford, CA, 1996. (Online). Available:
http://www-stat.stanford.edu/reports/friedman/poly.ps.Z
[9] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, “Large margin DAG’s
for multiclass classification,” in Advances in Neural Information
Processing Systems. Cambridge, MA: MIT Press, 2000, vol. 12, pp.
547–553.
[10] S. Miri, N. Passat, and J.P. Armspach, “Topology-preserving discrete
deformable model: Application to multi-segmentation of brain MRI,“
In Image and Signal Processing, pp. 67-75, Springer, 2008.
[11] C. Pierpaoli and P. J. Basser, "Toward a quantitative assessment of
diffusion anisotropy," Magnetic Resonance in Medicine, vol. 36, pp.
893–906, 1996.
[12] P.J. Basser, J. Mattiello, and D. Lebihan, “MR diffusion tensor
spectroscopy and imaging,” Biophysical Journal, vol. 66, pp. 259-267,
1994.
[13] A.L. Alexander, K. Hasan, G. Kindlmann, D.L. Parker, and J.S.
Tsuruda, “A Geometric Analysis of Diffusion Tensor Measurements of
The Human Brain,” Magnetic Resonance in Medicine, vol. 44, pp. 283–
291, 2000.
[14] L. Rittner and R. Lotufo, "Diffusion tensor imaging segmentation by
watershed transform on tensorial morphological gradient," in Computer
Graphics and Image Processing, 2008. SIBGRAPI'08. XXI Brazilian
Symposium on, pp. 196–203, 2008.
[15] E.M. Akkerman, “Efficient measurement and calculation of MR
diffusion anisotropy images using the platonic variance method,”
Magnetic Resonance in Medicine, vol. 49, pp. 599–604, 2003.
[16] M. Javadi, Brain tissue segmentation using DTI data, Dept. Chalmers
University of Technology, EX007, 2013. (Online). Available:
http://publications.lib.chalmers.se/records/fulltext/176182/176182.pdf
[17] V. Vapnik, “Statistical learning theory,” John Wiley and Sons, Inc., NY,
USA, 1998.
[18] Long Han, Mark J. Embrechts, Boleslaw Szymanski, “Sigma Tuning of
Gaussian Kernels: Detection of Ischemia from Magnetocardiograms,”
IGI Global 2011, pp. 206–223.</p>
<p>[1] E.D. Angelini, T. Song, B.D. Mensh, and A.F. Laine, “Brain MRI
segmentation with multiphase minimal partitioning: A comparative
study,” Int. Journal of Biomedical Imaging, vol. 2007, pp. 1–15.
[2] G. Heckenberg, Y. Xi, Y. Duan, and J. Hua, ”Brain structure
segmentation from MRI by geometric surface flow,” International
Journal of Biomedical Imaging, vol. 2006, pp. 1–6, 2006.
[3] O. Commowick, S.K. Warfield, “A continuous STAPLE for scalar,
vector, and tensor images: An application to DTI analysis,” IEEE
Transactions on Medical Imaging, vol. 28 , pp. 838–846, 2009.
[4] C. Lenglet, M. Rousson, and R. Deriche, "DTI segmentation by
statistical surface evolution", IEEE Trans. on Medical Imaging, vol. 25,
pp. 685–700, 2006
[5] I. Steinwart, A. Christmann, “Support Vector Machines (Book style),”
Springer, LA, USA, 2008, pp. 112-116.
[6] Y. Washizawa, “Feature extraction using constrained approximation and
suppression,” IEEE Trans. on Neural Networks, vol. 21, pp. 201–210,
2010.
[7] S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited:
A stepwise procedure for building and training a neural network,” in
Neurocomputing: Algorithms, Architectures and Applications, J.
Fogelman, Ed. New York: Springer-Verlag, 1990.
[8] J. Friedman. Another approach to polychotomous classification. Dept.
Statist., Stanford Univ., Stanford, CA, 1996. (Online). Available:
http://www-stat.stanford.edu/reports/friedman/poly.ps.Z
[9] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, “Large margin DAG’s
for multiclass classification,” in Advances in Neural Information
Processing Systems. Cambridge, MA: MIT Press, 2000, vol. 12, pp.
547–553.
[10] S. Miri, N. Passat, and J.P. Armspach, “Topology-preserving discrete
deformable model: Application to multi-segmentation of brain MRI,“
In Image and Signal Processing, pp. 67-75, Springer, 2008.
[11] C. Pierpaoli and P. J. Basser, "Toward a quantitative assessment of
diffusion anisotropy," Magnetic Resonance in Medicine, vol. 36, pp.
893–906, 1996.
[12] P.J. Basser, J. Mattiello, and D. Lebihan, “MR diffusion tensor
spectroscopy and imaging,” Biophysical Journal, vol. 66, pp. 259-267,
1994.
[13] A.L. Alexander, K. Hasan, G. Kindlmann, D.L. Parker, and J.S.
Tsuruda, “A Geometric Analysis of Diffusion Tensor Measurements of
The Human Brain,” Magnetic Resonance in Medicine, vol. 44, pp. 283–
291, 2000.
[14] L. Rittner and R. Lotufo, "Diffusion tensor imaging segmentation by
watershed transform on tensorial morphological gradient," in Computer
Graphics and Image Processing, 2008. SIBGRAPI'08. XXI Brazilian
Symposium on, pp. 196–203, 2008.
[15] E.M. Akkerman, “Efficient measurement and calculation of MR
diffusion anisotropy images using the platonic variance method,”
Magnetic Resonance in Medicine, vol. 49, pp. 599–604, 2003.
[16] M. Javadi, Brain tissue segmentation using DTI data, Dept. Chalmers
University of Technology, EX007, 2013. (Online). Available:
http://publications.lib.chalmers.se/records/fulltext/176182/176182.pdf
[17] V. Vapnik, “Statistical learning theory,” John Wiley and Sons, Inc., NY,
USA, 1998.
[18] Long Han, Mark J. Embrechts, Boleslaw Szymanski, “Sigma Tuning of
Gaussian Kernels: Detection of Ischemia from Magnetocardiograms,”
IGI Global 2011, pp. 206–223.</p>
@article{"International Journal of Information, Control and Computer Sciences:54965", author = "Mohammad Javadi", title = "A Serial Hierarchical Support Vector Machine and 2D Feature Sets Act for Brain DTI Segmentation", abstract = "Serial hierarchical support vector machine (SHSVM)
is proposed to discriminate three brain tissues which are white matter
(WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM
has novel classification approach by repeating the hierarchical
classification on data set iteratively. It used Radial Basis Function
(rbf) Kernel with different tuning to obtain accurate results. Also as
the second approach, segmentation performed with DAGSVM
method. In this article eight univariate features from the raw DTI data
are extracted and all the possible 2D feature sets are examined within
the segmentation process. SHSVM succeed to obtain DSI values
higher than 0.95 accuracy for all the three tissues, which are higher
than DAGSVM results.
", keywords = "Brain segmentation, DTI, hierarchical, SVM.", volume = "7", number = "7", pages = "919-4", }
