Automatic Quantification of MS Lesions in 3D MRI Brain Data Sets: Validation of INSECT
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A hybrid approach to MR imaging segmentation using unsupervised clustering and approximate reducts
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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A novel fully automatic procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described. The procedure uses feature space proximity measures, and does not make any assumptions about the tissue intensity distributions. As opposed to existing methods, which are often sensitive to anatomical variability and pathology (such as atrophy), the proposed procedure is robust against morphological deviations from the model. Starting from a set of samples generated from prior tissue probability maps (the "model") in a standard, brain-based coordinate system ("stereotaxic space"), the method reduces the fraction of incorrectly labeled samples in this set from 25% down to 5%. The corrected set of samples is then used by a supervised classifier for classifying the entire 3D image. Validation experiments were performed on both real and simulated MRI data; the Kappa similarity measure increased from 0.83 to 0.94.