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Parameter set for computer-assisted texture analysis of fetal brain

Overview of attention for article published in BMC Research Notes, November 2016
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Title
Parameter set for computer-assisted texture analysis of fetal brain
Published in
BMC Research Notes, November 2016
DOI 10.1186/s13104-016-2300-3
Pubmed ID
Authors

Hugues Gentillon, Ludomir Stefańczyk, Michał Strzelecki, Maria Respondek-Liberska

Abstract

Magnetic resonance data were collected from a diverse population of gravid women to objectively compare the quality of 1.5-tesla (1.5 T) versus 3-T magnetic resonance imaging of the developing human brain. MaZda and B11 computational-visual cognition tools were used to process 2D images. We proposed a wavelet-based parameter and two novel histogram-based parameters for Fisher texture analysis in three-dimensional space. Wavenhl, focus index, and dispersion index revealed better quality for 3 T. Though both 1.5 and 3 T images were 16-bit DICOM encoded, nearly 16 and 12 usable bits were measured in 3 and 1.5 T images, respectively. The four-bit padding observed in 1.5 T K-space encoding mimics noise by adding illusionistic details, which are not really part of the image. In contrast, zero-bit padding in 3 T provides space for storing more details and increases the likelihood of noise but as well as edges, which in turn are very crucial for differentiation of closely related anatomical structures. Both encoding modes are possible with both units, but higher 3 T resolution is the main difference. It contributes to higher perceived and available dynamic range. Apart from surprisingly larger Fisher coefficient, no significant difference was observed when testing was conducted with down-converted 8-bit BMP images.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 19%
Student > Bachelor 7 13%
Student > Master 5 9%
Professor 4 8%
Student > Doctoral Student 3 6%
Other 9 17%
Unknown 15 28%
Readers by discipline Count As %
Medicine and Dentistry 14 26%
Engineering 5 9%
Computer Science 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Agricultural and Biological Sciences 2 4%
Other 10 19%
Unknown 17 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 November 2016.
All research outputs
#20,355,479
of 22,903,988 outputs
Outputs from BMC Research Notes
#3,567
of 4,271 outputs
Outputs of similar age
#349,453
of 415,669 outputs
Outputs of similar age from BMC Research Notes
#45
of 49 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,271 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.