Pectoral Muscle Segmentation in Tomosynthesis Images using Geometry Information and Grey Wolf Optimizer

Mohamed Abdel-Nasser, Mohamed Abdel-Nasser, Francesc Porta Solsona, Domenec Puig

2020

Abstract

Digital breast tomosynthesis (DBT) is quickly replacing full-field digital mammography because it allows a more efficient breast cancer diagnostic workflow and yields a more confident interpretation. The visual characteristics of the pectoral muscle on mediolateral oblique (MLO) views may increase the false positive rate in computer-aided diagnosis systems. Therefore, the pectoral muscle should be extracted from MLO images before further analysis. Notably, most pectoral muscle segmentation method has a fixed parameter setting that may yield good results with some images and fail with others due to the variations in breast density. In this paper, we propose a promising method to segment pectoral muscles from tomosynthesis images based on geometric information of the pectoral muscle and a meta-heuristic optimization algorithm. Concretely, our method involves four steps: 1) a preprocessing step, 2) obtaining of geometric information of pectoral muscle, 3) selection of pectoral muscle pixels, and 4) finding the optimal parameters using the grey wolf optimizer (GWO). The GWO optimizer gets different parameters for each input image as they depend on the visual characteristics of the images (i.e., breast density). With each input image, the GWO optimizer determines different values of the parameters because they rely on the visual characteristics of tomosynthesis images that are highly related to breast density. The proposed method is evaluated with a set of tomosynthesis images obtaining a Dice score of 0.823 and an IoU score of 0.726.

Paper Citation

in Harvard Style

Abdel-Nasser M., Solsona F. and Puig D. (2020). Pectoral Muscle Segmentation in Tomosynthesis Images using Geometry Information and Grey Wolf Optimizer. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 829-836. DOI: 10.5220/0009156408290836

in Bibtex Style

@conference{visapp20,
author={Mohamed Abdel-Nasser and Francesc Porta Solsona and Domenec Puig},
title={Pectoral Muscle Segmentation in Tomosynthesis Images using Geometry Information and Grey Wolf Optimizer},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={829-836},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009156408290836},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Pectoral Muscle Segmentation in Tomosynthesis Images using Geometry Information and Grey Wolf Optimizer
SN - 978-989-758-402-2
AU - Abdel-Nasser M.
AU - Solsona F.
AU - Puig D.
PY - 2020
SP - 829
EP - 836
DO - 10.5220/0009156408290836
PB - SciTePress