Automated classification of multi-spectral MR images using Linear Discriminant Analysis

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

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Magnetic resonance imaging (MRI) is a valuable instrument in medical science owing to its capabilities in soft tissue characterization and 3D visualization. A potential application of MRI in clinical practice is brain parenchyma classification. This work proposes a novel approach called " Unsupervised Linear Discriminant Analysis (ULDA)" to classify and segment the three major tissues, i.e. gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF), from a multi-spectral MR image of the human brain. The ULDA comprises two processes, namely Target Generation Process (TGP) and Linear Discriminant Analysis (LDA) classification. TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to train the optimal division boundary by LDA, such that three tissues GM, WM and CSF are separated. Finally, two sets of images, namely computer-generated phantom images and real MR images are used in the experiments to evaluate the effectiveness of ULDA. Experiment results reveal that UDLA segments a multi-spectral MR image much more effectively than either FMRIB's Automated Segmentation Tool (FAST) or Fuzzy C-means (FC).

Original languageEnglish
Pages (from-to)251-268
Number of pages18
JournalComputerized Medical Imaging and Graphics
Issue number4
StatePublished - Jun 2010


  • Classification
  • FMRIB's Automated Segmentation Tool (FAST)
  • Fuzzy C-means
  • Linear Discriminant Analysis
  • Magnetic resonance imaging (MRI)
  • Multi-spectral
  • Unsupervised


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