Abstract
In this paper, a robust statistical model-based brain MRI image segmentation method is presented. The MRI images are modeled by Gaussian mixture model. This method, based on the statistical model, approximately finds the maximum a posteriori estimation of the segmentation and estimates the model parameters from the image data. The proposed strategy for segmentation is based on the EM and FCM algorithm. The prior model parameters are estimated via EM algorithm. Then, in order to obtain a good segmentation and speed up the convergence rate, initial estimates of the parameters were done by FCM algorithm. The proposed image segmentation methods have been tested using phantom simulated MRI data. The experimental results show the proposed method is effective and robust.
Chapter PDF
Similar content being viewed by others
Keywords
- Gray Matter
- Gaussian Mixture Model
- Segmentation Result
- Magnetic Resonance Image Image
- Brain Magnetic Resonance Image Image
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (2000)
Clark, M.C., Hall, L.O., Goldgof, D.B.: MRI segmentation using fuzzy clustering techniques: integrating knowledge. IEEE Eng. Med. Biol. 13(5), 730–742 (1994)
Ozkan, M., Dawant, B.M.: Neural-Network Based Segmentation of Multi-Modal Medical Images. IEEE Transaction on Medical Imaging 12, 534–544 (1993)
Kapur, T., Grimson, W.E., Wells, W.M., Kikinis, R.: Segmentation of brain tissue from magnetic resonance images. Med Image Anal. 1, 109–127 (1996)
Wang, Y., Adali, T.: Quantification and segmentation of brain tissues from MR images: A probabilistic neural network approach. IEEE Trans. on Image Processing 7, 1165–1180 (1998)
Tsai, C., Manjunath, B.S., Jagadeesan, R.: Automated segmentation of brain MR images. Pattern Recogn 28, 1825–1862 (1995)
Wells, W.M., Grimson, W.E.L.: Adaptive Segmentation of MRI data. IEEE Transaction on Medical Imaging 15, 429–442 (1996)
Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classiffication of MR images of the brain. IEEE trans. on medical imaging 18, 897–908 (1999)
McLachlan, G.J., Krishnan, T.: The EM algorithm and extensions. John Wiley and Sons, New York (1996)
McLachlan, G.M., Peel, D.: Finite Mixture Models. John Wiley & Sons, Inc., New York (2001)
Lorette, A., Descombes, X., Zerubia, J.: Urban aereas extraction based on texture analysis through a markovian modeling. International journal of computer vision 36, 219–234 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qin, B., Wen, J., Chen, M. (2005). A Robust Statistical Method for Brain Magnetic Resonance Image Segmentation. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_6
Download citation
DOI: https://doi.org/10.1007/11578079_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)