Journal of Medical and Biological Engineering

, Volume 36, Issue 1, pp 96–104 | Cite as

Trabecular Bone Morphological Analysis for Preclinical Osteoporosis Application Using Micro Computed Tomography Scanner

  • David Shih-Chun Jin
  • Chien-Hao Chu
  • Jyh-Cheng Chen
Original Article


Trabecular bone morphological parameter (TMP) analysis with micro computed tomography (micro-CT) has been used to evaluate the risk of fracture of osteoporosis in small animals. Many researchers have pointed out the drawback of making decisions based on bone mineral density only due to the lack of morphological information. Our study describes the application of a laboratory micro-CT system and a self-designed TMP algorithm combined with two statistical methodological tools for the evaluation of the artificially induced animal model by the ovariectomy (OVX) surgery process. The results show that the percentage bone volume (BV/TV), the trabecular properties thickness (Tb Th ), number (Tb N ), and separation (Tb Sp ) have significant differences between the normal and OVX groups. Tb Th and Tb Sp had very low p-values and are associated with bone loss caused by osteoporosis. The method can be used to early detect osteoporosis to prevent the risk of fracture in aging small animals.


Three-dimensional segmentation Bone mineral density (BMD) Biomedical image analysis Osteoporosis Trabecular bone morphological parameters (TMPs) 



The authors acknowledge the funding support by Ministry of Science and Technology (MOST) under grants NSC 96-2320-B-010-018-MY3, NSC 100-2320-B-010-002 and NSC 102-2627-E-010-001. We would like to thank the Institute of Anatomy and Cell Biology (IACB), Institute of Traditional Medicine (ITM), Institute of Clinical Medicine (ICM) of National Yang-Ming University, National Research Institute of Chinese Medicine (NRICM), Taipei Medical University, and Taipei City Hospital for giving us some technical support and providing us with SD rats. We would especially like to thank C. K. Yu. In a previous study, he developed the segmentation algorithm and helped us finish this study. Finally, we would like to thank Dr. C. H. Chen and his colleagues at Chang-Gung Memorial Hospital in Keelung for using our system and algorithm for their research, providing us with good validation and feedback.


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Copyright information

© Taiwanese Society of Biomedical Engineering 2016

Authors and Affiliations

  • David Shih-Chun Jin
    • 1
  • Chien-Hao Chu
    • 4
  • Jyh-Cheng Chen
    • 1
    • 2
    • 3
  1. 1.Department of Biomedical Imaging and Radiological SciencesNational Yang-Ming UniversityTaipeiTaiwan
  2. 2.Biophotonics and Molecular Imaging Research CenterNational Yang-Ming UniversityTaipeiTaiwan
  3. 3.Biomedical Engineering Research CenterNational Yang-Ming UniversityTaipeiTaiwan
  4. 4.Institute of Nuclear Energy ResearchAtomic Energy CouncilTaoyuanTaiwan

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