Chapter title | A hybrid approach to MR imaging segmentation using unsupervised clustering and approximate reducts |
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Authors | Widz, S., Revett, K. and Slezak, D. |
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Editors | Slezak, D., Yao, J., Peters, J.F., Ziarko, W. and Hu, X. |
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Abstract | We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89% for the noisiest image volume. We also tested the resultant classifier on real clinical data, which yielded an accuracy of approximately 84%. |
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Book title | Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005: Proceedings |
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Year | 2005 |
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Publisher | Berlin, Germany |
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Publication dates |
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Published | 2005 |
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Place of publication | Springer |
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Series | Lecture Notes in Computer Science |
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ISBN | 3540286608 |
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Digital Object Identifier (DOI) | https://doi.org/10.1007/11548706_39 |
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Journal citation | 2 (3642), pp. 372-382 |
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