Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Theoretical foundations of order-based genetic algorithms
Fundamenta Informaticae - Special issue: to the memory of Prof. Helena Rasiowa
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Automatic Generation of Training Data for Brain Tissue Classification from MRI
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
An Extensible MRI Simulator for Post-Processing Evaluation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Segmentation of Medical Images Based on Approximations in Rough Set Theory
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Fundamenta Informaticae
An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images
Pattern Recognition Letters
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Neuroeconomics: Yet Another Field Where Rough Sets Can Be Useful?
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation
Transactions on Rough Sets IX
A rough set-based magnetic resonance imaging partial volume detection system
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Bayesian rough set model: A further investigation
International Journal of Approximate Reasoning
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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%.