An Algorithm for Automated Separation of Trabecular Bone From Variably Thick Cortices in High-Resolution Computed Tomography Data

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 3; pp. 924 - 930
• Main Authors: Ang, Ida C., Fox, Maria, Polk, John D., Kersh, Mariana E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Automation; Bone; Bones; Cancellous bone; Cancellous Bone - diagnostic imaging; Computed tomography; cortical; Cortical bone; Cortical Bone - diagnostic imaging; Datasets; feature extraction; Gorilla gorilla; Head; High resolution; high-resolution imaging; Humans; image analysis; image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Matlab; Medical imaging; morphological operations; Neck; Pan troglodytes; Rats; Separation; Thickness measurement; X-Ray Microtomography - methods
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2924398

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 .