A Linear, Pixel-specific Color Normalization Algorithm for Hematology Imaging

Rachel Lou, Rachel Lou, Thanh Le

2021

Abstract

The automated cell recognition of hematology microscope images provides crucial information for the qualitative description of cell morphology and other quantitative applications in analyzing blood pathology. Computer-aided diagnostics and cell segmentation are invaluable tools to help reduce the cost of human labor and time. However, discrepancies in stain protocol and imaging hardware pose challenges to automated cell recognition; noise, blur, lighting contrast, and irregular coloration confound cell differentiation. In this study, we describe a linear pre-processing algorithm that addresses the color variation in hematology images. We qualitatively examine the image outputs and quantitatively assess the efficacy of the proposed algorithm by studying the performance of a cell detection model.

Paper Citation

in Harvard Style

Lou R. and Le T. (2021). A Linear, Pixel-specific Color Normalization Algorithm for Hematology Imaging. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING; ISBN 978-989-758-490-9, SciTePress, pages 201-208. DOI: 10.5220/0010344300002865

in Bibtex Style

@conference{bioimaging21,
author={Rachel Lou and Thanh Le},
title={A Linear, Pixel-specific Color Normalization Algorithm for Hematology Imaging},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING},
year={2021},
pages={201-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010344300002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING
TI - A Linear, Pixel-specific Color Normalization Algorithm for Hematology Imaging
SN - 978-989-758-490-9
AU - Lou R.
AU - Le T.
PY - 2021
SP - 201
EP - 208
DO - 10.5220/0010344300002865
PB - SciTePress