Super-resolved multi-channel fuzzy segmentation of MR brain images

Ying Bai, Xiao Han, Dzung L. Pham, Jerry L. Prince

Research output: Contribution to journalConference articlepeer-review


We propose a new fuzzy segmentation framework that incorporates the idea of super-resolution image reconstruction. The new framework is designed to segment data sets comprised of orthogonally acquired magnetic resonance (MR) images by taking into account their different system point spread functions. Formulating the reconstruction within the segmentation framework improves its robustness and stability, and makes it possible to incorporate multispectral scans that possess different contrast properties into the super-resolution reconstruction process. Our method has been tested on both simulated and real 3D MR brain data.

Original languageEnglish (US)
Article number59
Pages (from-to)580-589
Number of pages10
JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Issue numberI
StatePublished - Aug 25 2005
EventMedical Imaging 2005 - Image Processing - San Diego, CA, United States
Duration: Feb 13 2005Feb 17 2005


  • Brain cortex segmentation
  • Fuzzy segmentation
  • MRI
  • Super-resolution image reconstruction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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