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.
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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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
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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)
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