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

被引：118
作者：
PHILLIPS, WE
VELTHUIZEN, RP
PHUPHANICH, S
HALL, LO
CLARKE, LP
SILBIGER, ML
机构：
[1] Department of Radiology, Medical College of Georgia, Augusta, GA
[2] College of Medicine, University of South Florida, Tampa, FL
[3] College of Engineering, University of South Florida, Tampa, FL
[4] H. Lee Moffitt Cancer Center, Research Institute, Neuro-Oncology Program, Tampa, FL
来源：
MAGNETIC RESONANCE IMAGING | 1995年 / 13卷 / 02期
关键词：
MRI; COMPUTER ANALYSIS; IMAGE SEGMENTATION; FUZZY C-MEANS (FCM) ALGORITHMS; PATTERN RECOGNITION METHODS; CLUSTER ANALYSIS; GLIOBLASTOMA MULTIFORME; BRAIN; BRAIN TUMOR; EXPERIMENTAL TECHNOLOGY;
D O I：
10.1016/0730-725X(94)00093-I
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
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.
引用
收藏
页码：277 / 290
页数：14
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