Segmentation of MR Images Using Independent Component Analysis (original) (raw)

Abstract

Automated segmentation of MR images is a difficult problem due to complexity of the images. In this paper, we proposed a new method based on independent component analysis (ICA) for segmentation of MR images. We first extract thee independent components from the T1-weighted, T2-weighted and PD images by using ICA and then the extracted independent components are used for segmentation of MR images. Since ICA can enhance the local features, the MR images can be transformed to contrast-enhanced images by ICA. The effectiveness of the ICA-based method has been demonstrated.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Central South Forest University, Changsha, 410004, China
    Yen-Wei Chen & Daigo Sugiki
  2. College of Information Science and Eng., Ritsumeikan Univ., Shiga, 525-8577, Japan
    Yen-Wei Chen

Authors

  1. Yen-Wei Chen
  2. Daigo Sugiki

Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK
    Bogdan Gabrys
  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK
    Robert J. Howlett
  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, SA, 5095, Mawson Lakes, Australia
    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, YW., Sugiki, D. (2006). Segmentation of MR Images Using Independent Component Analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004\_8

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