Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model

Feriel Romdhane, Faouzi Benzarti, Hamid Amiri

Abstract

The denoising step for Computed Tomography (CT) images is an important challenge in the medical image processing. These images are degraded by low resolution and noise. In this paper, we propose a new method for 3D CT denoising based on Coherence Enhancing Diffusion model. Quantitative measures such as PSNR, SSIM and RMSE are computed to a phantom CT image in order to improve the efficiently of our proposed model, compared to a number of denoising algorithms. Furthermore, experimental results on a real 3D CT data show that this approach is effective and promising in removing noise and preserving details.

References

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Paper Citation


in Harvard Style

Romdhane F., Benzarti F. and Amiri H. (2016). Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 101-105. DOI: 10.5220/0005692701010105


in Bibtex Style

@conference{visapp16,
author={Feriel Romdhane and Faouzi Benzarti and Hamid Amiri},
title={Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={101-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005692701010105},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model
SN - 978-989-758-175-5
AU - Romdhane F.
AU - Benzarti F.
AU - Amiri H.
PY - 2016
SP - 101
EP - 105
DO - 10.5220/0005692701010105