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Journal of Computer Science and Technology

, Volume 34, Issue 1, pp 35–46 | Cite as

Texture Feature Extraction from Thyroid MR Imaging Using High-Order Derived Mean CLBP

  • Zhe Liu
  • Cheng-Jian Qiu
  • Yu-Qing Song
  • Xiao-Hong Liu
  • Juan Wang
  • Victor S. ShengEmail author
Regular Paper
  • 6 Downloads

Abstract

In the field of medical imaging, the traditional local binary pattern (LBP) and its improved algorithms are often sensitive to noise. Traditional LBPs are solely based on the signal information from local differences, and the binary quantization method oversimplifies the local texture features while disregarding the imaging information from the concaveconvex regions between the high-order pixels and the neighboring sampling points. Therefore, we propose an improved Derived Mean Complete Local Binary Pattern (DM CLBP) algorithm based on high-order derivatives. In the DM CLBP method, the grey value of a single pixel is replaced by the mean grey value of the rectangular area block, and the difference between pixel values in the area is obtained using the second-order differentiation method. Based on the calculation concept of the complete local binary pattern (CLBP) algorithm, the cascade signs and magnitudes of the two components are encoded and recombined in DM CLBP using a uniform pattern. The results from the experiments showed that the proposed DM CLBP descriptors achieved a classification accuracy of 94.4%. Compared with LBP and other improved algorithms, the DM CLBP algorithm presented in this study can effectively differentiate between lesion areas and normal areas in thyroid MR (magnetic resonance) images and shows the improved accuracy of area classification.

Keywords

thyroid magnetic resonance imaging (MRI) local binary pattern texture feature complete local binary pattern (CLBP) 

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Notes

Acknowledgements

The authors would like to thank Dr. Zhu of the Department of Medical at the University of Jiangsu.

Supplementary material

11390_2019_1897_MOESM1_ESM.pdf (223 kb)
ESM 1 (PDF 223 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zhe Liu
    • 1
  • Cheng-Jian Qiu
    • 1
  • Yu-Qing Song
    • 1
  • Xiao-Hong Liu
    • 1
  • Juan Wang
    • 1
  • Victor S. Sheng
    • 2
    Email author
  1. 1.School of Computer Science and TelecommunicationJiangsu UniversityZhenjiangChina
  2. 2.Department of Computer ScienceUniversity of Central ArkansasArkansasUSA

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