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Extraction of gray-scale intensity distributions from micro computed tomography imaging for femoral cortical bone differentiation between low-magnesium and normal diets in a laboratory mouse model.

Authors :
Tu, Shu-Ju
Wang, Shun-Ping
Cheng, Fu-Chou
Chen, Ying-Ju
Source :
Scientific Reports; 5/31/2019, Vol. 9 Issue 1, pN.PAG-N.PAG, 1p
Publication Year :
2019

Abstract

Previous studies have shown that the geometric development of femoral trabecular bone is affected by insufficient dietary intake of magnesium. However, it is not clear whether the development of femoral cortical bone can be quantitatively evaluated according to a diet with inadequate magnesium supplementation. Therefore, we used a micro computed tomography (CT) imaging approach with a laboratory mouse model to explore the potential application of texture analysis for the quantitative assessment of femoral cortical bones. C57BL/6J male mice were divided into two groups, where one group was fed a normal diet and the other group was fed a low-magnesium diet. We used a micro CT scanner for image acquisition, and the subsequent development of cortical bone was examined by texture analysis based on the statistical distribution of gray-scale intensities in which seven essential parameters were extracted from the micro CT images. Our calculations showed that the mean intensity increased by 7.20% (p = 0.000134), sigma decreased by 29.18% (p = 1.98E-12), skewness decreased by 19.52% (p = 0.0000205), kurtosis increased by 9.62% (p = 0.0877), energy increased by 24.19% (p = 3.32E-09), entropy decreased by 6.14% (p = 3.00E-10), and the Nakagami parameter increased by 104.32% (p = 4.13E-12) in the low-magnesium group when compared to the normal group. We found that the statistical parameters extracted from the gray-scale intensity distribution were able to differentiate between femoral cortical bone developments in the two different diet groups. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20452322
Volume :
9
Issue :
1
Database :
Complementary Index
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
136768607
Full Text :
https://doi.org/10.1038/s41598-019-44610-8