Enhancing Image Quality to Improve Medical Image Classification: Application to Nuclear Medicine Planar Images

Ouassim Boukhennoufa, Laurent Comas, Laurent Comas, Jean-Marc Nicod, Noureddine Zerhouni, Hatem Boulahdour, Hatem Boulahdour

2025

Abstract

Nuclear Medicine images are obtained by injecting small amounts of radio-tracers into the body to track specific organs. Particular cameras detect radiations emitted from the radio-tracers resulting in images that visualize the function of the organs rather than their structure. The association of the cameras and radio-tracers causes low resolution and low signal-to-noise ratio, therefore, the images are often of poor quality. Image Quality Enhancement (IQE) is one possible solution to this problem as it improves the clarity of the images by removing noise and correcting distortions. In this paper, we propose a methodology based on artificial intelligence (AI) with the integration of an IQE step for the detection of normal/abnormal parathyroid glands. Two different IQE techniques are employed, one based on a statistical filter and the other on AI. The enhanced images are processed with a Convolutional Neural Network (CNN), and Lasso regression for features extraction and selection. Finally, several AI models are used for binary image classification. The obtained results achieved an accuracy of 83% in distinguishing normal/abnormal parathyroid glands. IQE step significantly improves the accuracy of the AI model by 16.9% over the initial accuracy of 71%, demonstrating the importance of IQE in assessing image classification performance.

Paper Citation

in Harvard Style

Boukhennoufa O., Comas L., Nicod J., Zerhouni N. and Boulahdour H. (2025). Enhancing Image Quality to Improve Medical Image Classification: Application to Nuclear Medicine Planar Images. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 303-310. DOI: 10.5220/0013113800003911

in Bibtex Style

@conference{bioimaging25,
author={Ouassim Boukhennoufa and Laurent Comas and Jean-Marc Nicod and Noureddine Zerhouni and Hatem Boulahdour},
title={Enhancing Image Quality to Improve Medical Image Classification: Application to Nuclear Medicine Planar Images},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={303-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013113800003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Enhancing Image Quality to Improve Medical Image Classification: Application to Nuclear Medicine Planar Images
SN - 978-989-758-731-3
AU - Boukhennoufa O.
AU - Comas L.
AU - Nicod J.
AU - Zerhouni N.
AU - Boulahdour H.
PY - 2025
SP - 303
EP - 310
DO - 10.5220/0013113800003911
PB - SciTePress