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Conference papers
Fast Low-Dose CT Image Processing Using Improved Parallelized Nonlocal Means Filtering
Abstract : Although effectively reducing the radiation exposure to patients, low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging is also accompanied with high computation cost as a large searching window is often required to include much neighboring information for noise/artifact suppression. To accelerate the NLM filtering and improve its clinical feasibility, we propose in this paper an improved GPUbased parallelization approach. In addition to the straight pixel wise parallelization, the improved parallelization approach exploits the high I/O speed of GPU shared memory. Quantitative experiment demonstrates that significant acceleration is achieved with respect to the traditional pixel-wise parallelization
Keywords :
Acceleration
Computed Tomography
computerised tomography
dosimetry
fast low-dose computerised tomography image processing
Filtering
filtering theory
GPU
GPU-based parallelization
GPU shared memory
graphics processing units
high I-O speed
image denoising
Image processing
improved parallelized nonlocal means filtering
Kernel
large-scale patch similarity information
large searching window
low-dose CT
lowered diagnostic accuracy
medical image processing
mottled noise-artifact removal
Noise
noise-artifact suppression
nonlocal means (NLM)
radiation exposure
severely increased mottled noise-artifacts
straight pixel-wise parallelization
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01260663
Contributor : Laurent Jonchère <>
Submitted on : Friday, January 22, 2016 - 2:40:26 PM
Last modification on : Thursday, January 14, 2021 - 11:17:03 AM
Contributor : Laurent Jonchère <>
Submitted on : Friday, January 22, 2016 - 2:40:26 PM
Last modification on : Thursday, January 14, 2021 - 11:17:03 AM