Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic

Geng Cheng Lin, Chuin Mu Wang, Wen June Wang, Sheng Yih Sun

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents "Unsupervised CEM (UCEM)," a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.

Original languageEnglish
Pages (from-to)721-738
Number of pages18
JournalMagnetic Resonance Imaging
Volume28
Issue number5
DOIs
StatePublished - Jun 2010

Keywords

  • Classification
  • Constrained Energy Minimization (CEM)
  • FMRIB's Automated Segmentation Tool (FAST)
  • Fuzzy C-means
  • Magnetic resonance imaging (MRI)
  • Multispectral
  • Unsupervised

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