APPLICATION OF FUZZY C-MEANS SEGMENTATION TECHNIQUE FOR TISSUE DIFFERENTIATION IN MR-IMAGES OF A HEMORRHAGIC GLIOBLASTOMA-MULTIFORME

The application of a raw data-based, operator-independent MR segmentat ion technique to differentiate boundaries of tumor from edema or hemor rhage is demonstrated. A case of a glioblastoma multiforme with gross and histopathologic correlation is presented. The MR image data set wa s segmented into tissue classes based on three different MR weighted i mage parameters (T-1-, proton density-, and T-2-weighted) using unsupe rvised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition. A radiological examination of the MR images and correlati on with fuzzy clustering segmentations was performed. Results were con firmed by gross and histopathology which, to the best of our knowledge , reports the first application of this demanding approach. Based on t he results of neuropathologic correlation, the application of FCM MR i mage segmentation to several MR images of a glioblastoma multiforme re presents a viable technique for displaying diagnostically relevant tis sue contrast information used in 3D volume reconstruction. With this t echnique, it is possible to generate segmentation images that display clinically important neuroanatomic and neuropathologic tissue contrast information from raw MR image data.