Abstract
This paper proposes MR image segmentation based on Fast Fourier Transform based Expectation and Maximization Gaussian Mixture Model algorithm (GMM). No spatial correlation exists when classifying tissue type by using GMM and it also assumes that each class of the tissues is described by one Gaussian distribution but these assumptions lead to poor performance. It fails to utilize strong spatial correlation between neighboring pixels when used for the classification of tissues. The FFT based EM-GMM algorithm improves the classification accuracy as it takes into account of spatial correlation of neighboring pixels and as the segmentation done in Fourier domain instead of spatial domain. The solution via FFT is significantly faster compared to the classical solution in spatial domain — it is just O(N log 2N) instead of O(N^2) and therefore enables the use EM-GMM for high-throughput and real-time applications.
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
Grossman, R.J., McGowan, M.C.: Perspectives on Multiple Sclerosis. Am. J. Neuroradiol. 19, 1251–1265 (1998)
Khayati, R., Vafadust, M., Towhidkhah, F., Massood Nabavi, S.: Fully Automatic Segmentation of Multiple Sclerosis Lesions In Brain MR FLAIR images using Adaptive Mixtures. Computers in Biology and Medicine 38, 379–390 (2008)
Soni, A.: Brain Tissue Classification of Magnetic Resonance Images Using Conditional Random Fields Department of Computer Sciences University of Wisconsin-Madison (2007)
Jagannathan: Classification of Magnetic Resonance Brain Images using Wavelets as Input To Support Vector Machine And Neural Network. Biomedical Signal Processing and Control 1, 86–92 (2006)
Maitra, M., Chatterjee, A.: Hybrid multiresolution Slantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation. Med. Eng. Phys. (2007), doi:10.1016/j.medengphy.2007.06.009
Fletcher-Heath, L.M., Hall, L.O., Goldgof, D.B., Murtagh, F.R.: Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 43–63 (2001)
Abdolmaleki, P., Mihara, F., Masuda, K., Buadu, L.D.: Neural Networks Analysis of Astrocytic Gliomas from MRI appearances. Cancer Letters 118, 69–78 (1997)
Rosenbaum, T., Engelbrecht, V., Kroll, W., van Dorstenc, F.A., Hoehn-Berlagec, M., Lenard, H.-G.: MRI abnormalities in neurofibromatosis type 1 (NF1): a study of men and mice. Brain & Development 21, 268–273 (1999)
Cocosco, C., Zijdenbos, A.P., Evans, A.C.: A Fully Automatic and Robust Brain MRI Tissue Classification Method. Medical Image Analysis 7, 513–527 (2003)
El-dahshan, E.-S.A., Salem, A.-B.M., Youni, T.H.: A Hybrid Technique For Automatic MRI Brain Images Classification, STUDIA UNIV. Babes_Bolyai. Informatica LIV(1) (2009)
Gelenbe, E., Feng, Y., Krishnan, K.R.R.: Neural network methods for volumetric magnetic resonance imaging of the human brain. Proc. IEEE 84, 1488–1496 (1996)
Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., Bezdek, J.C.: A Comparison Of Neural Network And Fuzzy Clustering Techniques In Segmenting Magnetic Resonance Images of The Brain. IEEE Transactions on Neural Networks 3, 672–682 (1992)
Cocosco, C.A., Zijdenbox, A.P., Evans, A.C.: A Fully Automatic and Robust Brain MRI Tissue Classification Method. Medical Image Analysis 7(4), 513–527 (2003)
Ashburner, J., Friston, K.J.: Image Segmentation.: Human Brain Function, 2nd edn. Academic Press, London (2003)
Zhang, Y., Brady, M., Smith, S.: Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)
Tolba, M.F., Mostafa, M.G., Gharib, T.F., Salem, M.A.: MR-Brain Image Segmentation Using Gaussian Multiresolution Analysis and the EM Algorithm. In: ICEIS, vol. 2, pp. 165–170 (2003)
Lustig, M., Tsaig, J., Lee, J.H., Donoho, D.: Fast Spiral Fourier Transform For Iterative MRImage Reconstruction. Stanford University, Stanford (2004)
Rowe, D.B., Logan, B.R.: A complex way to compute fMRI activation. NeuroImage 23, 1078–1092 (2004)
Rowe, D.B.: Modeling both the magnitude and phase of complex-valued fMRI data. NeuroImage 25, 1310–1324 (2005)
Rowe, D.B., Nencka, A.S., Hoffmann, R.G.: Signal and noise of Fourier reconstructed fMRI data. Journal of Neuroscience Methods 159, 361–369 (2007)
Zwicker, E., Fastl, H.: Psychoacoustics: Facts and Models, 2nd edn. Springer, Berlin (1999)
Kailath, T., Sayed, A.H., Hassibi, B.: Linear Estimation. Prentice-Hall, Inc., Englewood Cliffs (2000)
Gray, R.M., Davisson, L.D.: An Introduction to Statistical Signal Processing. Cambridge University Press, Cambridge (2003)
Paquet, E., Rioux, M., Arsenaul, H.: Range image segmentation using the Fourier transform. Optical Engineering 32(09), 2173–2180 (1993)
Li, C.T., Wilson, R.: Image Segmentation Using Multiresolution Fourier Transform.Technical report, Department of Computer Science, University of Warwick (1995)
Wu, H.S., Barba, J., Gil, J.: An iterative algorithm for cell segmentation usingshort-time Fourier transform. J. Microsc. 184(Pt 2), 127–132 (1996)
Escofet, J., Millan, M.S., Rallo, M.: Applied Optics 40(34), 6170–6176 (2001)
Zou, W., Wang, D.: Texture identification and image segmentation via Fourier transform. In: Zhang, T., Bhanu, B., Shu, N. (eds.) Image Extraction, Segmentation, and Recognition. Proc. SPIE, vol. 4550, pp. 34–39 (2001)
Harte, T.P., Hanka, R.: Number Theoretic Transforms in Neural Network Image Classification (1997)
Kunttu, I., Lepisto, L., Rauhamaa, J., Visa, A.: Multiscale Fourier Descriptor for Shape Classification. In: Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP 2003). IEEE, Los Alamitos (2003)
Rowe, D.B., Logan, B.R.: A complex way to compute fMRI activation. NeuroImage 24, 1078–1092 (2004)
Rowe, D.B.: Modeling both magnitude and phase of complex-valued fMRI data. NeuroImage 25, 1310–1324 (2005)
Rowe, D.B., Nencka, A.S., Hoffman, R.G.: Signal and noise of Fourier reconstructed fMRI data. Journal of Neuroscience Methods 159, 361–369 (2007)
Mezrich, R.: A perspective on K-space. Radiology 195, 297–315
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramasamy, R., Anandhakumar, P. (2011). Brain Tissue Classification of MR Images Using Fast Fourier Transform Based Expectation- Maximization Gaussian Mixture Model. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Computing and Information Technology. ACITY 2011. Communications in Computer and Information Science, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22555-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-22555-0_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22554-3
Online ISBN: 978-3-642-22555-0
eBook Packages: Computer ScienceComputer Science (R0)