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• W2951182923 abstract "Objective: Structural measurements after separation of cortical from trabecular bone are of interest to a wide variety of communities but are difficult to obtain because of the lack of accurate automated techniques. Methods: We present a structure-based algorithm for separating cortical from trabecular bone in binarized images. Using the thickness of the cortex as a seed value, bone connected to the cortex within a spatially local threshold value is identified and separated from the remaining bone. The algorithm was tested on seven biological data sets from four species imaged using micro-computed tomography ( μ -CT) and high-resolution peripheral quantitative computed tomography (HR-pQCT). Area and local thickness measurements were compared to images segmented manually. Results: The algorithm was approximately 11 times faster than manual measurements and the median error in cortical area was −4.47 ± 4.15%. The median error in cortical thickness was approximately 0.5 voxels for μ -CT data and less than 0.05 voxels for HR-pQCT images resulting in an overall difference of −28.1 ± 71.1 μ m. Conclusion: A simple and readily implementable methodology has been developed that is repeatable, efficient, and requires few user inputs, providing an unbiased means of separating cortical from trabecular bone. Significance: Automating the segmentation of variably thick cortices will allow for the evaluation of large data sets in a time-efficient manner and allow for full-field analyses that have been previously limited to small regions of interest. The MATLAB code can be downloaded from https://github.com/TBL-UIUC/downloads.git ." @default.
• W2951182923 created "2019-06-27" @default.
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• W2951182923 date "2020-03-01" @default.
• W2951182923 modified "2023-10-17" @default.
• W2951182923 title "An Algorithm for Automated Separation of Trabecular Bone From Variably Thick Cortices in High-Resolution Computed Tomography Data" @default.
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• W2951182923 doi "https://doi.org/10.1109/tbme.2019.2924398" @default.
• W2951182923 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31247539" @default.
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