Sequence Tolerant Segmentation System of Brain MRI
Irene Yu‐Hua Gu, Lawrence Hall, Dmitry B. Goldgof, Parag M. Kanade, F. Reed Murtagh · 2006
An automatic human brain segmentation system for magnetic resonance images is presented. It has two main parts: a fuzzy clustering algorithm and a set of cluster combination rules. Images are segmented into ten classes by the unsupervised fuzzy c-means clustering algorithm. Then a knowledge-based system labels the clusters into the tissues of interest: cerebrospinal fluid, gray matter and white matter. This approach can process MRI data that comes from different scanners with different sequences and head coils, using several different spin-echo images (with different echo times) and different slice thickness. The system adapts without manual intervention. Segmented synthetic image data from the brainWeb simulated normal brain database resulted in a one voxel away accuracy of 90%. The results from real data from various magnetic resonance imagers were compared with a radiologist's segmentation and found to generally agree within 10%, the typical range of inter-rater radiologist agreement.