Foreground Extraction in Histo-Pathological Image by Combining Mathematical Morphology Operations and U-Net

Jia Li, Jia Li, Junling He, Jingmin Long, Chenxu Wang, Jesper Kers, Fons Verbeek

2023

Abstract

In recent years, computational pathology is rapidly developing. This resulted in various artificial intelligence approaches that have been proposed and applied to images common to the pathology practice, i.e. Whole Slide Images. It is very important to pre-process these images for a deep learning classifier because they are simply too large to feed into such a network. In order to get useful information from these images, we propose a new background removal method for the extracted Regions Of Interest in these images. We combine traditional morphology image operators and a U-Net framework. Firstly, we pre-process the images by using Contrast Limited Adaptive Histogram Equalization and thresholding. Then we predict the mask by using pre-trained U-Net weights. Finally, we use morphological opening and propagation operators on the predicted mask to refine the masks. The experiments based on different types of staining (H&E, PAS, and JONES silver) show the effectiveness of our method compared to 3 state-of-the-art models.

Paper Citation

in Harvard Style

Li J., He J., Long J., Wang C., Kers J. and Verbeek F. (2023). Foreground Extraction in Histo-Pathological Image by Combining Mathematical Morphology Operations and U-Net. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING; ISBN 978-989-758-631-6, SciTePress, pages 146-153. DOI: 10.5220/0011803500003414

in Bibtex Style

@conference{bioimaging23,
author={Jia Li and Junling He and Jingmin Long and Chenxu Wang and Jesper Kers and Fons Verbeek},
title={Foreground Extraction in Histo-Pathological Image by Combining Mathematical Morphology Operations and U-Net},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING},
year={2023},
pages={146-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011803500003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING
TI - Foreground Extraction in Histo-Pathological Image by Combining Mathematical Morphology Operations and U-Net
SN - 978-989-758-631-6
AU - Li J.
AU - He J.
AU - Long J.
AU - Wang C.
AU - Kers J.
AU - Verbeek F.
PY - 2023
SP - 146
EP - 153
DO - 10.5220/0011803500003414
PB - SciTePress