scholarly journals Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review

2017 ◽  
Vol 44 (3) ◽  
pp. 1168-1185 ◽  
Author(s):  
Hao Zhang ◽  
Dong Zeng ◽  
Hua Zhang ◽  
Jing Wang ◽  
Zhengrong Liang ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Dose ◽  
Ct Image ◽  
Nonlocal Means ◽  
X Ray

scholarly journals Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means

2012 ◽  
Vol 57 (9) ◽  
pp. 2667-2688 ◽  
Author(s):  
Yang Chen ◽  
Zhou Yang ◽  
Yining Hu ◽  
Guanyu Yang ◽  
Yongcheng Zhu ◽  
...  
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Low Dose ◽  
Ct Image ◽  
Nonlocal Means ◽  
Low Dose Ct

Fast Low-Dose CT Image Processing Using Improved Parallelized Nonlocal Means Filtering

2014 ◽  
Author(s):  
Zhikun Zhuang ◽  
Yang Chen ◽  
Huazhong Shu ◽  
Limin Luo ◽  
Christine Toumoulin ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Dose ◽  
Ct Image ◽  
Nonlocal Means ◽  
Low Dose Ct

Interactive multiple area spectrum integration (MASI)

1994 ◽  
Vol 52 ◽  
pp. 942-943
Author(s):  
John A. Hunt ◽  
Richard D. Leapman ◽  
David B. Williams
Keyword(s):  
Image Processing ◽  
Low Dose ◽  
Routine Analysis ◽  
Reference Image ◽  
Area Spectrum ◽  
List Type ◽  
Type Number ◽  
Image Processor ◽  
X Ray ◽  
Scanning Path

Interactive MASI involves controlling the raster of a STEM or SEM probe to areas predefined byan integration mask which is formed by image processing, drawing or selecting regions manually. EELS, x-ray, or other spectra are then acquired while the probe is scanning over the areas defined by the integration mask. The technique has several advantages: (1) Low-dose spectra can be acquired by averaging the dose over a great many similar features. (2) MASI can eliminate the risks of spatial under- or over-sampling of multiple, complicated, and irregularly shaped objects. (3) MASI is an extremely rapid and convenient way to record spectra for routine analysis. The technique is performed as follows:Acquire reference imageOptionally blank beam for beam-sensitive specimensUse image processor to select integration mask from reference imageCalculate scanning path for probeUnblank probe (if blanked)Correct for specimen drift since reference image acquisition

Learning-Based Artifact Removal via Image Decomposition for Low-Dose CT Image Processing

2016 ◽  
Vol 63 (3) ◽  
pp. 1860-1873 ◽  
Author(s):  
Xue-Ying Cui ◽  
Zhi-Guo Gui ◽  
Quan Zhang ◽  
Hong Shangguan ◽  
An-Hong Wang
Keyword(s):  
Image Processing ◽  
Low Dose ◽  
Image Decomposition ◽  
Ct Image ◽  
Artifact Removal ◽  
Low Dose Ct

scholarly journals Detection of beetle damage in forests by X-ray CT image processing

2003 ◽  
Vol 27 (5) ◽  
pp. 747-752 ◽  
Author(s):  
Paulo Estevão Cruvinel ◽  
João de Mendonça Naime ◽  
Miguel Borges ◽  
Álvaro Macedo ◽  
Aijun Zhang
Keyword(s):  
United States ◽  
Image Processing ◽  
The United States ◽  
Correct Identification ◽  
South American ◽  
Ct Image ◽  
Damage Evaluation ◽  
Beetle Species ◽  
X Ray ◽  
Beetle Damage

Some beetle species can have devastating economic impacts on forest and nursery industries. A recent example is Anophophora glabripennis, a species of beetle known in the United States as the ''Asian Longhorrned beetle'', which has damaged many American forests, and is a threat which can unintentionally reach south American countries, including Brazil. This work presents a new method based on X-ray computerized tomography (CT) and image processing for beetle injury detection in forests. Its results show a set of images with correct identification of the location of beetles in living trees as well as damage evaluation with time.

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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 109856-109869
Author(s):  
Yi Liu ◽  
Yang Chen ◽  
Ping Chen ◽  
Zhiwei Qiao ◽  
Zhiguo Gui
Keyword(s):  
Image Processing ◽  
Nonlinear Diffusion ◽  
Low Dose ◽  
Ct Image ◽  
Low Dose Ct ◽  
Diffusion Filtering ◽  
Nonlinear Diffusion Filtering
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