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Extraction of Texture Features from X-Ray Images: Case of Osteoarthritis Detection

  • Mukti Akter
  • Livija JakaiteEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

Texture features quantitatively represent patterns of interest in image analysis and interpretation. Texture features can vary so largely that the analysis leads to interpretation errors and undesirable consequences. In such cases, finding of informative features becomes problematic. In medical imaging, the texture features were found useful for representing variations in patterns of pixel intensity, which were correlated with pathological changes. In this paper, we describe a new approach to extracting the texture features which are represented on the basis of Zernike orthogonal polynomials. We report the preliminary results which were obtained for a case of osteoarthritis detection in X-ray images using a deep learning paradigm known as group method of data handling.

Keywords

Texture features Deep learning X-ray imaging 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyUniversity of BedfordshireLutonUK

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