Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 35-46.doi: 10.1007/s11390-019-1897-9

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section of Advances in Computer Science and Technology—Current Advances in the NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao 2014-2017 (Part 1) • Previous Articles     Next Articles

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

Zhe Liu1, Member, CCF, Cheng-Jian Qiu1, Yu-Qing Song1, Member, CCF, Xiao-Hong Liu1, Juan Wang1, and Victor S. Sheng2,*, Senior Member, IEEE, Member, ACM   

  1. 1 School of Computer Science and Telecommunication, Jiangsu University, Zhenjiang 212013, China;
    2 Department of Computer Science, University of Central Arkansas, Arkansas 72035, U.S.A.
  • Received:2018-07-12 Revised:2018-12-18 Online:2019-01-05 Published:2019-01-12
  • Contact: Victor S. Sheng
  • About author:Zhe Liu got her Ph.D. degree in computer science in 2012 from Jiangsu University, Zhenjiang. She is a visiting scholar of the Department of Radiology at University of Pittsburgh Medical Center, Pennsylvania, and also an associate professor at the School of Computer Science and Telecommunication, Jiangsu University, Zhenjiang. Her research interests include image processing, data mining and pattern recognition. She is a member of CCF.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61728205, 61772242, 61402204, and 61572239, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20130529, the Research Fund for Advanced Talents of Jiangsu University of China under Grant No. 14JDG141, the Science and Technology Project of Zhenjiang City of China under Grant No. SH20140110, the Special Software Development Foundation of Zhenjiang City of China under Grant No. 201322, and the Science and Technology Support Foundation of Zhenjiang City (Industrial) under Grant No. GY2014013.

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.

Key words: thyroid magnetic resonance imaging (MRI); local binary pattern; texture feature; complete local binary pattern (CLBP);

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