Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme

• Published in: Magnetic resonance imaging Vol. 13; no. 2; pp. 277 - 290
• Main Authors: Phillips, W.E., Velthuizen, R.P., Phuphanich, S., Hall, L.O., Clarke, L.P., Silbiger, M.L.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 1995; Elsevier Science
• Subjects: Adult; Algorithms; Biological and medical sciences; Brain; Brain - pathology; Brain Neoplasms - diagnosis; Brain tumor; Cerebral Hemorrhage - diagnosis; Cluster analysis; Computer analysis; Experimental technology; Fuzzy c-means (FCM) algorithms; Fuzzy Logic; Glioblastoma - diagnosis; Glioblastoma multiforme; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Magnetic Resonance Imaging - methods; Male; Medical sciences; MRI; Neurology; Pattern recognition methods; Pattern Recognition, Automated; Tumors of the nervous system. Phacomatoses; Cluster analysis; Brain tumor; Experimental technology; Image segmentation; Brain; Fuzzy c-means (FCM) algorithms; Pattern recognition methods; MRI; Computer analysis; Glioblastoma multiforme; Fuzzy algorithm; Segmentation; Technology; Tumor; Diagnosis; Pattern recognition; Hemorrhage; Nuclear magnetic resonance imaging; Computer aid; Brain (vertebrata)
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/0730-725X(94)00093-I

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