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Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

delete2019-01-01
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OA
AI
Y
Yi Liu
陈阳 cover
陈阳 (Yang Chen)
陈萍 (Ping Chen)
乔增杰 cover
乔增杰 (Zhiwei Qiao)
Z
Zhiguo Gui *
DOI:10.1109/ACCESS.2019.2933541delete
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Abstract

Abstract

En 中文
Computed tomography (CT) images with a low-dose protocol generally have severe mottle noise and streak artifacts. In this paper, we propose a novel diffusion method named artifact suppressed nonlinear diffusion filtering (ASNDF), to process low-dose CT (LDCT) images. Different from other diffusion filtering methods, the proposed ASNDF not only includes image gradient as the main cue to construct a diffusion coefficient function, but also incorporates the local variances of image to be diffused and residual image between two adjacent diffusions. In detail, the classical PM diffusion is first performed to get the initial residual image, and then from the second iteration, the LDCT image is processed according to the ASNDF processing. Simulated data, clinical data and rat data are conducted to evaluate the proposed method, and the comparison experiments with other competing methods show that the proposed ASNDF method makes an improvement in artifact suppression and structure preservation, and offers a sound alternative to process LDCT images from most current CT systems.
Keywords:
Low-dose computed tomography
nonlinear diffusion
local variance
residual local variance
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IEEE Access cover
IEEE Access
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3.6
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North University of China
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southeast university - china
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Shanxi University
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