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Extraction of texture features from x-ray images: case of osteoarthritis detection

  • Mukti Akter
    ,
  • Livija Jakaite
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 143-150

Publication milestones

  • Published - 29/09/2018

Publication status

Published - 29/09/2018

Volume

797

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

External Publication IDs

  • handle.net: 10547/624181
  • Scopus: 85054338695

Host publication title

Third International Congress on Information and Communication Technology

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