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.
Keywords
- Independent Component Analysis
- Independent Component
- False Negative Rate
- Independent Component Analysis
- Target Class
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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© 2006 Springer-Verlag Berlin Heidelberg
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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
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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
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