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

@article{Phillips1995ApplicationOF,
  title={Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme.},
  author={Walter Phillips and Robert P. Velthuizen and Surasak Phuphanich and Lawrence O. Hall and Laurence P. Clarke and Martin L. Silbiger},
  journal={Magnetic resonance imaging},
  year={1995},
  volume={13 2},
  pages={277-90}
}
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 (T1-, proton density-, and T2-weighted) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition… CONTINUE READING

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