Histogram-Based generation method of membership function for extracting features of brain tissues on MRI images

  • Authors:

  • Weibei Dou;Yuan Ren;Yanping Chen;Su Ruan;Daniel Bloyet;Jean-Marc Constans

  • Affiliations:

  • Department of Electronic Engineering, Tsinghua University, Beijing, China;Department of Electronic Engineering, Tsinghua University, Beijing, China;Imaging Diagnostic Center, Nanfang Hospital, Guangzhou, China;GREYC-ENSICAEN CNRS UMR 6072, Caen, France;GREYC-ENSICAEN CNRS UMR 6072, Caen, France;Unité d'IRM, EA3916, CHRU, Caen, France

  • Venue:
  • FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
  • Year:
  • 2005

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Abstract

We propose a generation method of membership function for extracting features of brain tissues on images of Magnetic Resonance Imaging (MRI). This method is derived from histogram analysis to create a membership function. According to a priori knowledge given by the neuro-radiologist, such as the features of gray level of differentiate brain tissues in MR images, we detect the peak or valley features of the histogram of MRI brain images. Then we determine a transformation of the histogram by selecting the feature values to generate a fuzzy membership function that corresponds to one type of brain tissues. A function approximations process is used to build a continuous membership function. This proposed method is validated for extracting whiter matter (WM), gray matter (GM), cerebra spino fluid (CSF). It is evaluated also using simulated MR images with two different, T1-weighted, T2-weighted MRI sequences. The higher agreement with the reference fuzzy model has been discovered by kappa statistic.