Abstract
We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropy-based metric defined on high dimensional probability density functions. Combined with an atlas-based initialization, it is completely automatic. Experiments on real and simulated data demonstrate the advantages of the method in comparison to other approaches.
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Tasdizen, T., Awate, S.P., Whitaker, R.T., Foster, N.L. (2005). MRI Tissue Classification with Neighborhood Statistics: A Nonparametric, Entropy-Minimizing Approach. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_64
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DOI: https://doi.org/10.1007/11566489_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29326-2
Online ISBN: 978-3-540-32095-1
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Keywords
- White Matter
- Expectation Maximization Algorithm
- Magnetic Resonance Image Data
- Tissue Class
- Image Neighborhood
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.
