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Tissue Differentiation Based on Classification of Morphometric Features of Nuclei

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Applied Informatics (ICAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1277))

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Abstract

The aim of the article is to analyze the shape of nuclei of various tissues and to assess the tumor differentiation based on morphometric measurements. For this purpose, an experiment was conducted, the results of which determine whether it is possible to determine a tissue’s type based on the mentioned features. The measurements were performed on a publicly available data set containing 1,356 hematoxylin- and eosin-stained images with nucleus segmentations for 14 different human tissues. Morphometric analysis of cell nuclei using ImageJ software took 17 parameters into account. Classification of the obtained results was performed in Matlab R2018b software using the SVM and t-SNE algorithms, which showed that some cancers can be distinguished with an accuracy close to 90% (lung squamous cell cancer vs others; breast cancer vs cervical cancer).

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Acknowledgement

This publication was funded by AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering under grant number 16.16.120.773.

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Authors and Affiliations

  1. Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, A. Mickiewicza 30 Av., 30–059, Cracow, Poland

    Dominika Dudzińska & Adam Piórkowski

Authors
  1. Dominika Dudzińska
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  2. Adam Piórkowski
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Corresponding author

Correspondence to Adam Piórkowski .

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Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Covenant University, Ota, Nigeria

    Sanjay Misra

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Dudzińska, D., Piórkowski, A. (2020). Tissue Differentiation Based on Classification of Morphometric Features of Nuclei. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_29

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  • DOI: https://doi.org/10.1007/978-3-030-61702-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61701-1

  • Online ISBN: 978-3-030-61702-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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