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Liver function classification based on local direction number and non-local binary pattern

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Abstract

Liver function can be used to track secretion, excretion, synthesis, and reserve function. This research proposes a new method based on CT images to address the problem of current methods of liver function measurement being invasive and insufficiently. To begin, Gabor filters are employed to extract the multiscale texture features of the region of interest in CT image, and the principal directions of each scale are encoded in a compact numerical mode in order to extract more discriminative features. Second, to achieve a wide range of pixel relationships, the non-local binary mode is used. Finally, the support vector machine is used to classify features. Extensive experiments have shown that evaluating liver function using a CT image is both feasible and effective. The relationship between liver function grades and CT image is examined using the model for end stage liver disease (MELD) score. It provides non-invasive and more efficient image-based auxiliary diagnosis for liver function evaluation.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was part of a project funded by “The National Natural Science Foundation of China” (Grant No. 61901195).

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Authors and Affiliations

  1. Ocean College, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, China

    Weijia Huang, Zhengyan Zhang, Caiping Xi, Zehui Wang & Yuanjiang Li

  2. Department of Intervention, Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital, 210029, Nanjing, China

    Wei Yang

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  1. Weijia Huang
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  2. Wei Yang
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  3. Zhengyan Zhang
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  4. Caiping Xi
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  5. Zehui Wang
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Correspondence to Zhengyan Zhang.

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Huang, W., Yang, W., Zhang, Z. et al. Liver function classification based on local direction number and non-local binary pattern. Multimed Tools Appl 81, 32305–32322 (2022). https://doi.org/10.1007/s11042-022-12986-x

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  • DOI: https://doi.org/10.1007/s11042-022-12986-x

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