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Diagnostic Method of Liver Cirrhosis Based on MR Image Texture Feature Extraction and Classification Algorithm

Journal of Medical Systems volume 44, Article number: 11 (2020) Cite this article

Abstract

In order to improve the accuracy of cirrhosis staging diagnosis based on MR images, a diagnostic method combining image texture feature extraction and classification algorithm is proposed. Firstly, the liver MR image is preprocessed, the region of interest (ROI) image patch is extracted therefrom, and the ROI image is quantized and compressed by the Lloyd algorithm. Then, the ROI image is filtered by a local binary pattern (LBP) operator, and then the texture feature of a 20-dimensional gray-level co-occurrence Matrix (GLCM) in four directions on the LBP image is extracted. Finally, MR image is classified by performing support vector machine (SVM) and the final diagnosis of liver cirrhosis is obtained. The experimental results show that the proposed method can accurately diagnose liver cirrhosis.

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Affiliations

  1. Department of Radiology, Jinan Infectious Disease Hospital affiliated to Shandong University, Jinan, 250021, Shandong, China

    Xiong chunmei, Han mei & Zhao yan

  2. Department of Pharmacy, Jinan Infectious Disease Hospital affiliated to Shandong University, Jinan, 250021, Shandong, China

    Wang haiying

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  1. Xiong chunmei
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  2. Han mei
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  3. Zhao yan
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Correspondence to Wang haiying.

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We declare that we have no conflict of interest. The paper does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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chunmei, X., mei, H., yan, Z. et al. Diagnostic Method of Liver Cirrhosis Based on MR Image Texture Feature Extraction and Classification Algorithm. J Med Syst 44, 11 (2020). https://doi.org/10.1007/s10916-019-1508-x

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  • DOI: https://doi.org/10.1007/s10916-019-1508-x

Keywords

  • MR image
  • Cirrhosis staging diagnosis
  • Texture feature
  • Local binary mode
  • Gray level co-occurrence matrix