Feature selection algorithm for classification of multispectral MR images using Constrained Energy Minimization

Geng Cheng Lin, Wen June Wang, Chuin Mu Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

This study proposes a new unsupervised approach for targets detection and classification in multispectral Magnetic Resonance (MR) images. The proposed method comprises two processes, namely Target Generation Process (TGP) and Constrained Energy Minimization (CEM). TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to be desired targets in CEM Finally, the real MR images are used in the experiments to evaluate the effectiveness of proposed method. Experiment results reveal that the proposed method segments a multispectral MR image much more effectively than either FMRIB's Automated Segmentation Tool (FAST) or Fuzzy C-means (FC).

Original languageEnglish
Title of host publication2010 10th International Conference on Hybrid Intelligent Systems, HIS 2010
Pages43-46
Number of pages4
DOIs
StatePublished - 2010
Event2010 10th International Conference on Hybrid Intelligent Systems, HIS 2010 - Atlanta, GA, United States
Duration: 23 Aug 201025 Aug 2010

Publication series

Name2010 10th International Conference on Hybrid Intelligent Systems, HIS 2010

Conference

Conference2010 10th International Conference on Hybrid Intelligent Systems, HIS 2010
Country/TerritoryUnited States
CityAtlanta, GA
Period23/08/1025/08/10

Keywords

  • Classification
  • Constrained energy minimization (CEM)
  • Magnetic resonance imaging (MRI)
  • Multispectral

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