Abstract-A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T 1-weighted, proton density, and T 2-weighted feature images and are processed by a system which integrates knowledgebased (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.
The results of the system generally correspond well to ground truth, both on a per state basis and more importantly in tracking total volume during treatment over time REF .
Automatic tumor segmentation using knowledge-based techniques
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging
Mathematics Medicine