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Optimal Parameter Search for Colour Normalization Aiding Cell Nuclei Segmentation

  • Karolina NurzynskaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)

Abstract

Automatic segmentation of biological images is necessary to allow faster diagnosis of several diseases. There are numerous methods addressing this problem, yet any general solution has been proposed. Probably it might result from the lacking standardization in tissue staining by hematoxylin and eosin, which is necessary to better visualize cell structure. The colour space normalization seems to be a perfect solution, but choosing adequate parameters is still a difficult task. Therefore, in this work, a Monte Carlo Simulation method is applied to search for a set of parameters assuring the best performance of colour transfer normalization technique. The segmentation accuracy is evaluated for each parameter set on a dataset containing colon tissue. Three accuracy metrics are computed to compare manually prepared masks with those achieved automatically: the Dice coefficient, specificity, and sensitivity. The analysis of the aggregated results proved that it is possible to find a sub-space where the worst results are placed, and depending on the accuracy measure it is possible to find a plane dividing those results.

Keywords

Cell segmentation Colour space Monte Carlo simulation Dice coefficient 

Notes

Acknowledgement

This work was supported by statutory fonds (02/020/BK_18/0128) and statutory funds for young researchers (BKM-509/RAU2/2017) of the Institute of Informatics, Silesian University of Technology, Poland.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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