Abstract
In this paper, we present a non-rigid image registration method for DTMR images. This method consists of finding control points using a piecewise affine registration procedure and then estimating final transform between two images by minimizing corresponding Least Squares Support Vector Machine (LS-SVM) function of these control points. In our scheme, a fully symmetric grid points in the reference image is selected and the transformed grid points are computed using the results of piecewise affine registration. These control points are then employed to estimate final transform between images by minimizing the related LS-SVM function. In the transform functions, a finite strain (FS) based reorientation strategy is applied to adopt these methods for DTMR images. The main advantage of this method is that in estimating transform function, it considers all control points. Thus, each point in the reference image is transformed consistently with all other image points.
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References
Goldberg-Zimring, D., Mewes, A.U., Maddah, M., Warfield, S.K.: Diffusion tensor magnetic resonance imaging in multiple sclerosis. American Society of Neuroimaging 15, 68S–81S (2005)
Westin, C.F., Maier, S.E., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R.: Processing and visualization for diffusion tensor MRI. Med. Imag. Analysis 6, 93–108 (2002)
Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. Med. Imag. 20(11), 1131–1139 (2001)
Park, H.J., Kubicki, M., Shenton, M.E., Guimond, A., McCarley, R.W., Maier, S.E., Kikinis, R., Jolesz, F.A., Westin, C.F.: Spatial normalization of diffusion tensor MRI using multiple channels. Neuroimage 20(4), 1995–2009 (2003)
Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C.: Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med. Imag. Analysis 10, 764–785 (2006)
Zhang, H., Yushkevich, P.A., Gee, J.C.: Registration of diffusion tensor images. In: CVPR 2004, vol. 1, pp. 842–847 (2004)
Cao, Y., Miller, M.I., Mori, S., Winslow, R.L., Younes, L.: Diffeomorphic matching of diffusion tensor images. In: CVPRW 2006, pp. 67–74 (2006)
Peng, D.Q., Liu, J., Tian, J.W., Zheng, S.: Transformation model estimation of image registration via least square support vector machines. Pattern Recognition Letters 27(12), 1397–1404 (2006)
Besser, P.J., Jones, D.K.: Diffusion-tensor MRI: theory, experimental design and data analysis –a technical review. NMR in Biomedicine 15, 456–467 (2002)
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Davoodi-Bojd, E., Soltanian-Zadeh, H. (2008). Grid Based Registration of Diffusion Tensor Images Using Least Square Support Vector Machines. In: Sarbazi-Azad, H., Parhami, B., Miremadi, SG., Hessabi, S. (eds) Advances in Computer Science and Engineering. CSICC 2008. Communications in Computer and Information Science, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89985-3_76
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DOI: https://doi.org/10.1007/978-3-540-89985-3_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89984-6
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