Abstract
The images acquired from different techniques of medical equipment are generally noisy data. The noise distribution in the CT image is modeled as a Gaussian distribution which appears in the images as a random fluctuation allowing to a misdiagnosis. So the denoising of the CT images is a challenge task in medical area. In this paper we propose a new denoised method based on combination between the Non local mean filter and the Diffusion Tensor for 3D Computed tomography scan data with a MAD estimator for gaussian noise. A quantitative measures was calculated and compared to other common denoising methods, improving the efficiently of our algorithm in term of removing noise and preserving significant details.
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Romdhane, F., Benzarti, F., Amiri, H. (2017). 3D CT Denoising by New Combination Between Nl-Mean Filter and Diffusion Tensor. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_24
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DOI: https://doi.org/10.1007/978-3-319-52941-7_24
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