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Multimedia Tools and Applications

, Volume 77, Issue 23, pp 30769–30789 | Cite as

Texture analysis of magnetic resonance brain images to assess multiple sclerosis lesions

  • Samah Yahia
  • Yassine Ben Salem
  • Mohamed Naceur Abdelkrim
Article
  • 28 Downloads

Abstract

The novel contribution of this work is to introduce a new promising method for the analysis of textures: the Decimal Descriptor Patterns (DDP). Two best known methods of texture measures, always considered as references in image analysis are chosen for comparison: the Local Binary Patterns (LBP) and the Grey Level Co-occurrence Matrix (GLCM). We realized numerous experimentations for analyzing the brain Magnetic Resonance (MR) images in order to demonstrate the interest of our proposition. We used the 3D Brainweb database with two brain MR images sequences, and different levels of noise and intensity non-uniformity. This way accuracy of the three methods is tested in front on the image artifacts. Tests of classification are performed in the same conditions of work by means of the classifier multiclass Support Vector Machines (SVM). Experimental results demonstrate clearly the robustness and the stability of the proposed approach with respect to the noise level, intensity non-uniformity and to different T1- and T2- weighted MR images.

Keywords

Brain MR images Noise level Intensity non-uniformity DDP GLCM LBP SVM 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Samah Yahia
    • 1
  • Yassine Ben Salem
    • 1
  • Mohamed Naceur Abdelkrim
    • 1
  1. 1.Research Laboratory Modeling, Analysis and Control of Systems (MACS)University of Gabes - Tunisia, National Engineering School of Gabes (ENIG)GabesTunisia

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