Skip to main content

Advertisement

Log in

A study of intracortical porosity’s area fractions and aspect ratios using computer vision and pulse-coupled neural networks

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Employing computer vision (CV) and optimized pulse-coupled neural networks (PCNN), this work automatically quantifies the geometrical attributes of intracortical bone porosity (namely lacunae and canaliculi (L-C), Haversian canals, and resorption cavities). Fifty pathological slides of cortical bone (× 20 magnification) were prepared from middiaphysis of bovine forelegs collected fresh from butcher. Biopsies were subdivided into sectors encircling arcs (θ of 10°) and radial distances (R) originating from the bone’s geometric center toward posterior regions and spanning 3.3 mm. Microscopically, each pore is classified according to whether it belonged to primary or secondary osteon. Globally, each pore is assigned as being located in anterior or posterior regions. For each pore, area and major/minor axes lengths were determined as raw measures from which derived geometric measures, namely, area fraction (AF) and aspect ratio (AR), were derived. Said measures were plotted versus R (for different angles). Plots of AF and AR trends were found to vary linearly along the radial distance. Area fractions (%) significantly decreased linearly with R (p < 0.01) in the anterior region. In the posterior region, area fraction values are flat versus R. These findings are indicative of maturing osteons at the outer cortex with predominately near circular-shaped pores.

(Left) Grids of slides (magnified at 20X) of intra-cortical bone showing Lacunar-canalicular porosity (LCP). Areas marked with the dotted square represent a group of 25 images. The dashed line is a hand-drawn line that demarcates the anterior and posterior regions and the solid line is the best-fit arc radii (R =16.4 mm) of the dashed demarcation line. (Right) Images rotated in the polar coordinate system with their respective angles and radii shown.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

%so:

% area of secondary osteons in image

%po :

% area of primary osteons in image

A so :

Area of secondary osteons in image

A po :

Area of primary osteons in image

A rc :

Area of resorption cavity(ies) in image

\( S\left({A}_{so}^l\right) \) :

Summed areas of lacunae in secondary osteons in image

\( S\left({A}_{po}^l\right) \) :

Summed areas of lacunae in primary osteons in image

\( S\left({A}_{so}^c\right) \) :

Summed areas of canaliculi clusters in secondary osteons in image

\( S\left({A}_{po}^c\right) \) :

Summed areas of canaliculi clusters in primary osteons in image

\( S\left({A}_{so}^{hc}\right) \) :

Summed areas of Haversian canals in secondary osteons in image

\( {AF}_{so}^l \) :

Area fraction (%) of lacunae in secondary osteons in image

\( {AF}_{po}^l \) :

Area fraction (%) of lacunae in primary osteons in image

\( {AF}_{so}^c \) :

Area fraction (%) of canaliculi clusters in secondary osteons in image

\( {AF}_{po}^c \) :

Area fraction (%) of canaliculi clusters in primary osteons in image

\( {AF}_{so}^{hc} \) :

Area fraction (%) of Haversian canals in secondary osteons in image

AF rc :

Area fraction (%) of resorption cavity(ies) in image

References

  1. Remaggi F, Cane V, Palumbo C, Ferretti M (1998) Histomorphometric study on the osteocyte lacuno- canalicular network in animals of different species. I. Woven-fibered and parallel-fibered bones. Ital J Anat Embryol 103:145–155

    PubMed  CAS  Google Scholar 

  2. Cardoso L, Fritton SP, Gailani G, Benalla M, Cowin SC (2013) Advances in the assessment of bone porosity, permeability, and interstitial fluid flow. J Biomech 46:253–265

    Article  PubMed  Google Scholar 

  3. Wang X, Ni Q (2013) Determination of cortical bone porosity and pore size distribution using a low field pulsed NMR approach. J Orthop Res 21:312–319

    Article  CAS  Google Scholar 

  4. Lin Y, Xu S (2011) AFM analysis of the lacunar-canalicular network in demineralized compact bone. J Microsc 241:291–302

    Article  PubMed  CAS  Google Scholar 

  5. Thomas CDL, Feik SA, Clement JG (2005) Regional variation of intracortical porosity in the midshaft of the human femur: age and sex differences. J Anat 206:115–125

    Article  PubMed  PubMed Central  Google Scholar 

  6. Thomas CDL, Feik SA, Clement JG (2006) Increase in pore area, and not pore density, is the main determinant in the development of porosity in human cortical bone. J Anat 209(2):219–230

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bousson V, Meunier A, Bergot C, Vicaut É, Rocha MA, Morais MH, Laval-Jeantet A, Laredo J (2001) Distribution of intracortical porosity in human midfemoral cortex by age and gender. J Bone Miner Res 16(7):1308–1317

    Article  PubMed  CAS  Google Scholar 

  8. Nirody JA, Cheng KP, Parrish RM, Burghardt AJ, Majumdar S, Link TM, Kazakia GJ (2015) Spatial distribution of intracortical porosity varies across age and sex. Bone 75:88–95

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tjong W, Nirody J, Burghardt AJ, Carballido-Gamio J, Kazakia GJ (2014) Structural analysis of cortical porosity applied to HR-pQCT data. Med Phys 41(1):013701

    Article  PubMed  Google Scholar 

  10. Burghardt AJ, Buie HR, Laib A, Majumdar S, Boyd SK (2010) Reproducibility of direct quantitative measures of cortical bone microarchitecture of the distal radius and tibia by HR-pQCT. Bone 4:519–528

    Article  Google Scholar 

  11. Stein M, Feik S, Thomas C, Clement J, Wark J (1999) An automated analysis of intracortical porosity in human femoral bone across age. J Bone Miner Res 14(4):624–632

    Article  PubMed  CAS  Google Scholar 

  12. Hage IS, Hamade RF (2013) Segmentation of histology slides of cortical bone using pulse coupled neural networks optimized by particle-swarm optimization. Comput Med Imaging Graphics 7:466–474

    Article  Google Scholar 

  13. Hage IS, Hamade RF Toward quantifying geometric microstructural differences between primary and secondary osteons via segmentation, 2014 Middle East Conference on Biomedical Engineering (MECBME), February 17–20, 2014, Hilton Hotel, Doha, Qatar, IEEE 371–374

  14. Hage IS, Hamade RF. Distribution of porosity in cortical (bovine) bone, Proceedings of the ASME 2015 International Mechanical Engineering Congress & Exposition, IMECE2015, IMECE2015-51703, November 13–19, 2015, Houston, Texas

  15. Hage IS, Hamade RF (2016) Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks. J Bone Miner Metab 34:251–265

    Article  PubMed  Google Scholar 

  16. Hage IS, Hamade RF (2016) Detecting individual osteons in histology cortical bone slides using PSO-optimized PCNN. AIMS Med Sci 2:328–346

    Article  Google Scholar 

  17. Revell P (1983) Histomorphometry of bone. J Clin Pathol 12:1323–1331

    Article  Google Scholar 

  18. Lindblad T, Kinser J (2013) Image processing using pulse-coupled neural networks. Springer-Verlag, Berlin Heidelberg

    Book  Google Scholar 

  19. Gao K, Dong M, Jia F, Gao M OTSU image segmentation algorithm with immune computation optimized PCNN parameters. IEEE, Congress on Engineering and Technology (S-CET), 27–30 May 2012

  20. Mayya A, Banerjee A, Rajesh R (2013) Mammalian cortical bone in tension is non-Haversian. Sci Rep 3:Article number 2533

    Article  Google Scholar 

  21. Clarke B (2008) Normal bone anatomy and physiology. Clin J Am Soc Nephrol Suppl 3:S131–S139

    Article  CAS  Google Scholar 

  22. Zhou X, Novotny JE, Wang L (2009) Anatomic variations of the lacunar–canalicular system influence solute transport in bone. Bone 45(4):704–710

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Lerner UH (2012) Osteoblasts, osteoclasts, and osteocytes: unveiling their intimate-associated responses to applied orthodontic forces. Semin Orthod 18:237–248

    Article  Google Scholar 

  24. Raisz LG (1999) Physiology and pathophysiology of bone remodeling. Clin Chem 45(8 Pt 2):1353–1358

    PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This publication was made possible by Award #103087 from the Lebanese NCSR. The authors also acknowledge the support of the university research board (URB) at the American University of Beirut.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. F. Hamade.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hage, I.S., Hamade, R.F. A study of intracortical porosity’s area fractions and aspect ratios using computer vision and pulse-coupled neural networks. Med Biol Eng Comput 57, 577–588 (2019). https://doi.org/10.1007/s11517-018-1900-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1900-6

Keywords

Navigation