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Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions

Overview of attention for article published in BioMedical Engineering OnLine, July 2015
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1 tweeter
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1 patent

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Title
Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions
Published in
BioMedical Engineering OnLine, July 2015
DOI 10.1186/s12938-015-0064-y
Pubmed ID
Authors

Xiaofei Sun, Lin Shi, Yishan Luo, Wei Yang, Hongpeng Li, Peipeng Liang, Kuncheng Li, Vincent C T Mok, Winnie C W Chu, Defeng Wang

Abstract

Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. We have proposed a histogram-based MRI intensity normalization method. The method can normalize scans which were acquired on different MRI units. We have validated that the method can greatly improve the image analysis performance. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
United States 1 <1%
Unknown 147 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 31 21%
Researcher 20 13%
Student > Bachelor 19 13%
Student > Ph. D. Student 18 12%
Student > Doctoral Student 5 3%
Other 19 13%
Unknown 37 25%
Readers by discipline Count As %
Engineering 33 22%
Computer Science 19 13%
Medicine and Dentistry 18 12%
Neuroscience 10 7%
Physics and Astronomy 7 5%
Other 16 11%
Unknown 46 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 April 2021.
All research outputs
#6,294,389
of 20,775,181 outputs
Outputs from BioMedical Engineering OnLine
#160
of 778 outputs
Outputs of similar age
#75,295
of 247,416 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 5 outputs
Altmetric has tracked 20,775,181 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 778 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 247,416 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them