Abstract
Automated segmentation of MR images is a difficult problem due to complexity of the images. In this paper, we proposed a new method based on independent component analysis (ICA) for segmentation of MR images. We first extract thee independent components from the T1-weighted, T2-weighted and PD images by using ICA and then the extracted independent components are used for segmentation of MR images. Since ICA can enhance the local features, the MR images can be transformed to contrast-enhanced images by ICA. The effectiveness of the ICA-based method has been demonstrated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bezdek, L.C., Hall, L.O., Clarke, L.P.: Review of MR image segmentation techiniques using pattern recognition. Medical Physics 20, 1033-1048 (1993)
Cohen, L.: On active contour models and ballons. Computer vision, Graphics and Image Processing: Image Understanding 53, 211–218 (1991)
Hohne, K., Hanson, W.: Interactive 3d segmentation of MRI and CT volumes using morphological operations. Journal of Computer Assisted Tomography 16, 285–294 (1992)
Cline, H.E., Lorensen, W.E., Kikinis, R., Jolesz, F.A.: Three-dimensional segmentation of MR images of the head using probability and connectivity. Journal of Computer Assisted Tomography 14, 1037–1045 (1990)
Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification, vol. 4, pp. 43–55 (2000)
Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A.: Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging 11, 221–232 (1992)
Mohamed, N.A., Aly, A.F.: Volume segmentation of CT/MRI images using multiscale features, self-organizing principal component analysis, and self-organizing feature map. In: Proc. of Int. Conf. on Artificial Neural Networks, Houston (1997)
Clark, M., Hall, L., Goldgof, D., Clarke, L., Silbiger, L.: MRI segmentation using fuzzy clustering techniques:Integrating knowledge. IEEE Eng. Med. & Biol. Mag. 13, 730–742 (1994)
Atkins, M.S., Mackiewich, M.T.: Fully automatic segmentation of the brain in MRI. IEEE Trans. Med. Imagin 17, 98–107 (1998)
Nakai, T., Muraki, S., Bagarinao, E., Miki, Y., Takehara, Y., Matsuo, K., Kato, C., Sakahara, H., Isoda, H.: Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter. NeuroImage 21, 251–260 (2004)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129-1159 (1995)
Chen, Y.-W.: Independent Component Analysis (1) Cocktail Party Effect. Medical Imaging Technology 21, 81-85 (2003) (in Japanese)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley and Sons, Inc., Chichester (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, YW., Sugiki, D. (2006). Segmentation of MR Images Using Independent Component Analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_8
Download citation
DOI: https://doi.org/10.1007/11893004_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
eBook Packages: Computer ScienceComputer Science (R0)