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ObjectiveUse of quantitative computed tomography (CT) to evaluate bone mineral density was suggested in the 1970s. Despite its reliability and accuracy, technical shortcomings restricted its usage, and dual-energy X-ray absorptiometry (DXA) became the gold standard evaluation method. Advances in CT technology have reduced its previous limitations, and CT evaluation of bone quality may now be applicable in clinical practice. The aim of this study was to determine if the Hounsfield unit (HU) values obtained from CT correlate with patient age and bone mineral density.MethodsA total of 128 female patients who underwent lumbar CT for back pain were enrolled in the study. Their mean age was 66.4 years. Among them, 70 patients also underwent DXA. The patients were stratified by decade of life, forming five age groups. Lumbar vertebrae L1-4 were analyzed. The HU value of each vertebra was determined by averaging three measurements of the vertebra's trabecular portion, as shown in consecutive axial CT images. The HU values were compared between age groups, and correlations of HU value with bone mineral density and T-scores were determined.ResultsThe HU values consistently decreased with increasing age with significant differences between age groups (p<0.001). There were significant positive correlations (p<0.001) of HU value with bone mineral density and T-score.ConclusionThe trabecular area HU value consistently decreases with age. Based on the strong positive correlation between HU value and bone mineral density, CT-based HU values might be useful in detecting bone mineral diseases, such as osteoporosis.

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This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.