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

Ida C. Ang, Maria Fox, John D. Polk, Mariana E. Kersh

Research output: Contribution to journalArticle

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 μm. 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.

Original languageEnglish (US)
Article number8743421
Pages (from-to)924-930
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume67
Issue number3
Early online dateJun 21 2019
DOIs
StatePublished - Mar 2020

Keywords

  • Bone
  • cortical
  • feature extraction
  • high-resolution imaging
  • image analysis
  • image processing
  • image segmentation
  • MATLAB
  • morphological operations

ASJC Scopus subject areas

  • Biomedical Engineering

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