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Texture Analysis for Trabecular Bone X-Ray Images Using Anisotropic Morlet Wavelet and Rényi Entropy

  • Ahmed Salmi EL Boumnini El Hassani
  • Mohammed El Hassouni
  • Rachid Jennane
  • Mohammed Rziza
  • Eric Lespessailles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

In this paper, we propose a new method based on texture analysis for the early diagnosis of bone disease such as osteoporosis. Our proposed method is based on a combination of four methods. First, bone X-ray images are enhanced using the algorithm of Retinex. Then, the enhanced images are analyzed using the fully anisotropic Morlet wavelet. This step is followed by the quantification of the anisotropy of the images using the Rényi entropy. Finally, the Rényi entropies are used as entries for a neural network. Applied on two different populations composed of osteoporotic (OP) patients and control (CT) subjects, a classification rate of 95% is achieved which provides a good discrimination between OP patients and CT subjects.

Keywords

Texture Analysis Receiver Operating Characteristic Curve Local Binary Pattern Area Under Curve Morlet Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmed Salmi EL Boumnini El Hassani
    • 1
  • Mohammed El Hassouni
    • 2
  • Rachid Jennane
    • 3
  • Mohammed Rziza
    • 1
  • Eric Lespessailles
    • 4
  1. 1.LRIT, Faculty of ScienceUniversity Mohammed V - AgdalRabatMorocco
  2. 2.DESTEC-FLSHRUniversity Mohammed V - AgdalRabatMorocco
  3. 3.PRISME LaboratoryUniversity of OrléansOrléansFrance
  4. 4.Hospital of Orleans, IPROSOrléansFrance

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