AIMS Medical Science, 2015, 2(4): 328-346. doi: 10.3934/medsci.2015.4.328.

Research article

Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Automatic Detection of Cortical Bones Haversian Osteonal Boundaries

Department of Mechanical Engineering, American University of Beirut, Riad El-Solh, Beirut 1107 2020, Lebanon

This work aims to automatically detect cement lines in decalcified cortical bone sections stained with H&E. Employed is a methodology developed previously by the authors and proven to successfully count and disambiguate the micro-architectural features (namely Haversian canals, canaliculi, and osteocyte lacunae) present in the secondary osteons/Haversian system (osteon) of cortical bone. This methodology combines methods typically considered separately, namely pulse coupled neural networks (PCNN), particle swarm optimization (PSO), and adaptive threshold (AT). In lieu of human bone, slides (at 20× magnification) from bovid cortical bone are used in this study as proxy of human bone. Having been characterized, features with same orientation are used to detect the cement line viewed as the next coaxial layer adjacent to the outermost lamella of the osteon. Employed for this purpose are three attributes for each and every micro-sized feature identified in the osteon lamellar system: (1) orientation, (2) size (ellipse perimeter) and (3) Euler number (a topological measure). From a training image, automated parameters for the PCNN network are obtained by forming fitness functions extracted from these attributes. It is found that a 3-way combination of these features attributes yields good representations of the overall osteon boundary (cement line). Near-unity values of classical metrics of quality (precision, sensitivity, specificity, accuracy, and dice) suggest that the segments obtained automatically by the optimized artificial intelligent methodology are of high fidelity as compared with manual tracing. For bench marking, cement lines segmented by k-means did not fare as well. An analysis based on the modified Hausdorff distance (MHD) of the segmented cement lines also testified to the quality of the detected cement lines vis-a-vis the k-means method.
  Article Metrics

Keywords cortical bone; secondary osteon; cement line; image segmentation; pulse coupled neural networks; particle swarm optimization; adaptive threshold

Citation: Ilige Hage, Ramsey Hamade. Automatic Detection of Cortical Bones Haversian Osteonal Boundaries. AIMS Medical Science, 2015, 2(4): 328-346. doi: 10.3934/medsci.2015.4.328


  • 1. Salguero L, Saadat F, Sevostianov I (2014) Micromechanical modeling of elastic properties of cortical bone accounting for anisotropy of dense tissue. J Biomechanics 47: 3279-3287.
  • 2. Aishwarya R, Nagaraju V (2012) Automatic region of interest based medical image segmentation using spatial fuzzy k clustering method. Int J Electronics Commu 3: 226-229.
  • 3. Deselaers T, Keysers D, Ney H (2004) Features for image retrieval: A quantitative comparison. Pattern Recogn: 228-236.
  • 4. Couprie C, Najman L, Talbot H (2011) Seeded segmentation methods for medical image analysis. Med Image Process: 27-57.
  • 5. Beham MP, Gurulakshmi A (2012) Morphological image processing approach on the detection of tumor and cancer cells. Devices Circuits Systems (ICDCS), 2012 International Conference on: 350-354.
  • 6. Quelhas P, Marcuzzo M, Mendonça AM, et al. (2009) Cancer cell detection and invasion depth estimation in brightfield images. BMVC: 1-10.
  • 7. Tassani S, Korfiatis V, Matsopoulos GK. Segmentation of micro-CT images: An understudied problem.
  • 8. Wu Y, Bergot C, Jolivet E, et al. (2009) Cortical bone mineralization differences between hip-fractured females and controls. A microradiographic study. Bone 45: 207-212.
  • 9. Ligesh CAS, Shanker N, Vijay A, et al. (2011) Estimation of bone mineral density from the digital image of the calcanium bone. Electro Comput Techno (ICECT), 2011 3rd International Conference on 3: 365-369.
  • 10. Ma X, Overton T (1991) Automated image analysis for bone density measurements using computed tomography. Med Imaging 10: 611-615.
  • 11. Wolf P, Luechinger R, Stacoff A, et al. (2007) Reliability of tarsal bone segmentation and its contribution to MR kinematic analysis methods. Comput Med Imaging Graphics 31:523-530.
  • 12. Zhang Y, He Z, Fan S, et al. (2008) Automatic thresholding of micro-CT trabecular bone images. BioMed Engine Inform 2: 23-27.
  • 13. Liu Z, Austin TJ, Moore D, et al. (1995) Image processing techniques for bone image analysis. Image Processing 1: 458-461.
  • 14. Liu Z, Liew HL, Dance S (1996) Image processing techniques for quantitative bone image analysis. Sign Process Applica 1: 431-432.
  • 15. Jatti A (2010) Segmentation of microscopic bone images. Int J Electron Engine 2.
  • 16. Jatti A (2011) Segmentation and Analysis of Microscopic Osteosarcoma Bone Images. Int J Inform Techno Knowledge Manage 4: 195-200.
  • 17. Liu Z, Liew HL, Clement JG, et al. (1999) Bone image segmentation. Biomed Engine 46: 565-573.
  • 18. Liu Z, Austin TJ, Thomas CDL, et al. (1996) Bone feature analysis using image processing techniques. Comput Biol Med 26: 65-76.    
  • 19. Cooper D, Erickson B, Peele AG, et al. (2011) Visualization of 3D osteon morphology by synchrotron radiation micro‐CT. J Anatomy 219: 481-489.
  • 20. Dong P, Pacureanu A, Zuluaga MA, et al. (2014) Quantification of the 3D morphology of the bone cell network from synchrotron micro-CT images. Image Analy Stereol 33: 157-166.    
  • 21. Hage IS, Hamade RF (2012) Structural micro processing of Haversian systems of a cortical bovine femur using optical stereomicroscope and MATLAB. ASME 2012 Int Mechan Engine Congress Exposit: 595-601.
  • 22. 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 37: 466-474.
  • 23. Hage IS, Hamade RF (2015) Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks. J Bone Mine Metabol. In press.
  • 24. Hage IS, Hamade RF (2013) Micro-FEM orthogonal cutting model for bone using microscope images enhanced via artificial intelligence. Procedia CIRP 8: 384-389.
  • 25. Hage IS, Hamade RF (2013) Micro FEM simulations of single-cutting-edge sawing of cortical bone. ASME 2013 Int Mechan Engine Congress Exposit: V03AT03A049-V03AT03A049.
  • 26. Lindblad T, Kinser JM (2005) Image processing using pulse-coupled neural networks, Springer.
  • 27. Wang Z, Ma Y, Gu J (2010) Multi-focus image fusion using PCNN. Pattern Recog 43: 2003-2016.    
  • 28. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intelligence 1: 33-57.
  • 29. Gao K, Dong M, Jia F, et al. (2012) OTSU image segmentation algorithm with immune computation optimized PCNN parameters. Engine Technol (S-CET), 2012 Spring Congress: 1-4.
  • 30. Pai Y, Chang Y, Ruan SJ (2010) Adaptive thresholding algorithm: Efficient computation technique based on intelligent block detection for degraded document images. Pattern Recog 43: 3177-3187.
  • 31. Gonzalez RC, Woods RE (2002) Digital image processing using MATLAB. Pearson Education India.
  • 32. Ma Y, Zhan K, Wang Z (2010) Applications of pulse-coupled neural networks. Higher Education Press.
  • 33. Deng Y, Manjunath BS, Shin H, et al. (1999) Color Image Segmentation. IEEE CVPR 2: 2446.
  • 34. Appleford MR, Pilia M (2014). U.S. Patent No. 20,140,236,312. Washington, DC: U.S. Patent and Trademark Office.
  • 35. Dawant BM, Hartmann SL, Thirion JP, et al. (1999) Medical Imaging, Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects. IEEE 18: 909-916.
  • 36. Conte D, Foggia P, Tufano F, et al. (2011) An Enhanced Level Set Algorithm for Wrist Bone Segmentation. Image Segment, InTech: 293-308.
  • 37. Calder J, Tahmasebi AM, Mansouri AR (2011) A variational approach to bone segmentation in CT images. SPIE Med Imaging: 79620B-79620B-15.
  • 38. Mahendran S, Baboo S (2011) Enhanced automatic X-ray bone image segmentation using wavelets and morphological operators. Int Conf on Information and Electronics Eng: 125-129.
  • 39. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Image 15: 29.    
  • 40. Bégin S, Dupont-Therrien O, Bélanger E, et al. (2014) Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue. Biomed Optics Express 5: 4145-4161.    


This article has been cited by

  • 1. Herbert F. Jelinek, Andrei V. Kelarev, A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression, AIMS Medical Science, 2016, 3, 2, 217, 10.3934/medsci.2016.2.217
  • 2. Andrei V. Kelarev, Xun Yi, Hui Cui, Leanne Rylands, Herbert F. Jelinek, A survey of state-of-the-art methods for securing medical databases, AIMS Medical Science, 2018, 5, 1, 1, 10.3934/medsci.2018.1.1
  • 3. Ilige S. Hage, R. F. Hamade, A study of intracortical porosity’s area fractions and aspect ratios using computer vision and pulse-coupled neural networks, Medical & Biological Engineering & Computing, 2018, 10.1007/s11517-018-1900-6
  • 4. Yu-xi Liu, Ai-hua Li, Yan-hua Li, L. Yang, Z. Xu, Effects of Microstructure Characteristics of Cortical Bone on its Microcrack Propagation, E3S Web of Conferences, 2020, 185, 03027, 10.1051/e3sconf/202018503027

Reader Comments

your name: *   your email: *  

Copyright Info: 2015, Ramsey Hamade, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved