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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Widz, S., Revett, K., Ślȩzak, D. (2005). A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_39
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DOI: https://doi.org/10.1007/11548706_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
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