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




Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering. - Download this document for free, or read online. Document in PDF available to download.

* Corresponding author 1 LTSI - Laboratoire Traitement du Signal et de l-Image 2 CRIBS - Centre de Recherche en Information Biomédicale sino-français 3 LIST - Laboratory of Image Science and Technology Nanjing

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.

Mots-clés : X-ray CT Image denoising Image enhancement





Author: Chen Yang - Fei Yu - Limin Luo - Christine Toumoulin -

Source: https://hal.archives-ouvertes.fr/



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