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MR Brain Images Segmentation Using Joint Information and Fuzzy C-Means

  • Ouarda AssasEmail author
  • Salah Eddine Bouhouita Guermech
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

Magnetic resonance imaging (MRI) is a powerful technique to help doctor to make decision and detect possible pathologies. This work is an improvement of an existing method which is Fuzzy C-Means (FCM) to separate the different tissues of MR Brain image based on the joint information of clusters: Fuzzy Joint Information Segmentation (FJIS). The joint information introduced into consideration the interaction of clusters for improving the segmentation. Joint information is based on the joint probability which expresses the confusion of a pixel to belong to a particular partition; in other words augmenting its chance of belonging to all partitions simultaneously which means increasing joint probability; so, minimizing the corresponding joint information. The evaluation of adopted approaches was compared using Peak signal to noise ratio: PSNR and information entropy: IE. The obtained results on synthetic medical images prove the efficiency and the accuracy of the proposed approaches. The information entropy seems to be a useful function for defining segmentation of the anatomical image.

Keywords

Component MR images Fuzzy logic Segmentation Joint information Fuzzy C-Means 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ouarda Assas
    • 1
    Email author
  • Salah Eddine Bouhouita Guermech
    • 1
  1. 1.Department of Computer ScienceUniversity of M’silaM’silaAlgeria

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