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Automatic Measurement of Blood Vessel Angles in Immunohistochemical Images of Liver Cancer

  • Hongbin Zhang
  • Kun Zhang
  • Li Chen
  • Jianguo WuEmail author
  • Peijian Zhang
  • Huiyu Zhou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 761)

Abstract

This paper presents a method for automated measurement of vascular angle in immunohistochemical images of liver cancer. Firstly, Colour Deconvolution is used to conduct staining separation on a H&E-stained immunohistochemical image, and then blood vessels are segmented using an improved Otsu algorithm. Then the standard SURF algorithm is used to select feature points of the image, and then these feature points are divided into two equal groups according to the distance between individual feature points and the far left (or right) feature point. Finally, a standard least squares method is used to fit two lines using the two groups of points. When the linear deviation of the fitting result based on the two groups of feature points is significant, it is necessary to adjust the belonging of the points of the two groups, and then the two sets are fitted again respectively till the correlation coefficients of the two fitted lines are greater than the predefined threshold, meaning that the measurement of the blood vessel angle in the immunohistochemical map is completed. Compared with the experts’ results, our proposed technique results in better accuracy. It is worthy to point out that, to our knowledge, our system is the first one that conducts automated measurement of blood vessel angle of immunohistochemistry.

Keywords

Immunohistochemical image Color deconvolution Image segmentation Feature extraction Least square 

Notes

Acknowledgements

This work was financially supported by the Natural Science Foundation of Jiangsu Province, China under Grant No. BK20170443. Nantong Research Program of Application Foundation under Grant No. GY12016022, and Dr. H. Zhou is supported by UK EPSRC under Grant EP/N011074/1, and Newton Advanced Fellowship under Grant NA160342.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Hongbin Zhang
    • 1
  • Kun Zhang
    • 1
  • Li Chen
    • 2
  • Jianguo Wu
    • 1
    Email author
  • Peijian Zhang
    • 1
  • Huiyu Zhou
    • 3
  1. 1.School of Electrical EngineeringNantong UniversityNantongChina
  2. 2.Department of Pathology, Medical CollegeNantong UniversityNantongChina
  3. 3.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastNorthern Ireland, UK

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