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Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering.

Abstract : Reducing patient radiation dose, while maintaining a high-quality image, is a major challenge in Computed Tomography (CT). The purpose of this work is to improve abdomen tumor low-dose CT (LDCT) image quality by using a two-step strategy: a first patch-wise non linear processing is first applied to suppress the noise and artifacts, that is based on a sparsity prior in term of a learned dictionary, then an unsharp filtering aiming to enhance the contrast of tissues and compensate the contrast loss caused by the DL processing. Preliminary results show that the proposed method is effective in suppressing mottled noise as well as improving tumor detectability.
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https://www.hal.inserm.fr/inserm-00874954
Contributor : Christine Toumoulin <>
Submitted on : Saturday, October 19, 2013 - 7:20:16 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM
Long-term archiving on: : Monday, January 20, 2014 - 4:25:41 AM

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Chen Yang, Fei Yu, Limin Luo, Christine Toumoulin. Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2013, 2013, pp.4014-7. ⟨10.1109/EMBC.2013.6610425⟩. ⟨inserm-00874954⟩

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