Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme
- 1 January 1995
- journal article
- case report
- Published by Elsevier BV in Magnetic Resonance Imaging
- Vol. 13 (2), 277-290
- https://doi.org/10.1016/0730-725x(94)00093-i
Abstract
No abstract availableThis publication has 18 references indexed in Scilit:
- A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brainIEEE Transactions on Neural Networks, 1992
- Multispectral analysis of uterine corpus tumors in magnetic resonance imagingMagnetic Resonance in Medicine, 1992
- Automatic segmentation of head mri images by knowledge guided thresholdingComputerized Medical Imaging and Graphics, 1991
- Characterization of normal brain tissue using seven calculated MRI parameters and a statistical analysis systemMagnetic Resonance in Medicine, 1989
- Information processing in nuclear magnetic resonance imagingMagnetic Resonance Imaging, 1988
- Recent convergence results for the fuzzy c-means clustering algorithmsJournal of Classification, 1988
- Toward an automated analysis system for nuclear magnetic resonance imaging. II. Initial segmentation algorithmMedical Physics, 1986
- Efficient Implementation of the Fuzzy c-Means Clustering AlgorithmsIEEE Transactions on Pattern Analysis and Machine Intelligence, 1986
- Image segmentation techniquesComputer Vision, Graphics, and Image Processing, 1985
- Multispectral analysis of magnetic resonance images.Radiology, 1985