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%. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Widz, S., Revett, K., & Ślȩzak, D. (2005). A hybrid approach to MR imaging segmentation using unsupervised clustering and approximate reducts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3642 LNAI, pp. 372–382). https://doi.org/10.1007/11548706_39
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