Fast Low-dose CT Image Processing Using Improved parallelized Nonlocal Means Filtering

被引:2
作者
Zhuang, Zhikun [1 ,2 ]
Chen, Yang [1 ,2 ,3 ,4 ]
Shu, Huazhong [1 ,2 ]
Luo, Limin [1 ,2 ]
Toumoulin, Christine [2 ,3 ,4 ]
Coatrieux, Jean-Louis [2 ,3 ,4 ]
机构
[1] Southeast Univ, Lab Image Sci & Technol, Nanjing, Jiangsu, Peoples R China
[2] Ctr Rech Informat Biomed Sinofrancais LIA CRIBs, Rennes, France
[3] INSERM, U1099, F-35000 Rennes, France
[4] Univ Rennes 1, LTSI, F-35000 Rennes, France
来源
2014 INTERNATIONAL CONFERENCE ON MEDICAL BIOMETRICS (ICMB 2014) | 2014年
关键词
nonlocal means (NLM); low-dose CT; GPU;
D O I
10.1109/ICMB.2014.33
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 GPU-based 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.
引用
收藏
页码:147 / 150
页数:4
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