NDLI: Denoising low dose CT images via 3D total variation using CUDA

Content Provider	IEEE Xplore Digital Library
Author	Baolin Mao Dayu Xiao Xin Xiong Xiaozhao Chen Wei Zhang Yan Kang
Copyright Year	2013
Description	Author affiliation: Sino-dutch Biomed. & Inf. Eng. Sch., Northeastern Univ., Shenyang, China (Baolin Mao; Dayu Xiao; Xin Xiong; Xiaozhao Chen; Wei Zhang; Yan Kang)
Abstract	The purpose of this paper is to improve the quality of low dose Computed Tomography (CT) images. Low dose artifacts are the Gaussian noises superimposed on the CT images and are often caused by insufficient calibrated detector and photon starvation. A noise reduction method via three dimensional total variation using Compute Unified Device Architecture (CUDA) is proposed on the three dimensional image. This method can also be employed for low dose noise removal of the two dimensional CT images by decreasing a total variation dimension processing technique. The performance of the proposed algorithm has been tested by using quantitative measures. For quantitative analysis, the quality assessment parameter Peak-signal-to-noise-ratio (PSNR) is used in this paper. Both simulation and real experiment results show that the proposed technique increases the image quality and has high calculation speed.
Sponsorship	IEEE Beijing Sect.
Starting Page	47
Ending Page	50
File Size	855090
Page Count	4
File Format	PDF
e-ISBN	9781467358873
DOI	10.1109/ICMIPE.2013.6864501
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	CUDA PSNR Low dose artifacts Computed tomography Noise reduction Graphics processing units Noise measurement 3D total variation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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