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The Influence of Different Segmentation Methods on the Extraction of Imaging Histological Features of Hepatocellular Carcinoma CT

Abstract

In order to analyze the influence of different segmentation techniques on hepatocellular carcinoma (HCC) CT (Computed Tomography) imaging histological feature extraction, Grow Cut method and Graph Cut method are used to segment hepatocellular carcinoma from arterial CT images of HCC patients, and the stability and repeatability of imaging histological features are studied. Meanwhile, hierarchical clustering method is used to reduce the redundancy of features. The results show that the repeatability and redundancy mainly depend on the method of tumor segmentation. Semi-automatic segmentation method can improve the repeatability of image features, and hierarchical clustering can reduce the redundancy of features. Different segmentation techniques have different effects on the extraction of histological features of CT images of HCC.

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Correspondence to Zhixue Wang.

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Author Sen Zhao declares that he has no conflict of interest. Author Wenyan Ren declares that he has no conflict of interest. Author Yan Zhuang declares that he has no conflict of interest. Author Zhixue Wang declares that he has no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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This article is part of the Topical Collection on Image & Signal Processing

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Zhao, S., Ren, W., Zhuang, Y. et al. The Influence of Different Segmentation Methods on the Extraction of Imaging Histological Features of Hepatocellular Carcinoma CT. J Med Syst 43, 101 (2019). https://doi.org/10.1007/s10916-019-1207-7

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Keywords

  • Segmentation
  • HCC
  • Imaging histological features
  • Repeatability