Mandible Bone Osteoporosis Detection using Cone-beam Computed Tomography


  • R. F. A. Marar Department of Computer Science, Faculty of Information Technology, Middle East University, Jordan
  • D. M. Uliyan Department of Information Computer Science, College of Computer Science and Engineering, University of Hail, Saudi Arabia
  • H. A. Al-Sewadi Department of Computer Science, Faculty of Information Technology, Middle East University, Jordan
Volume: 10 | Issue: 4 | Pages: 6027-6033 | August 2020 |


Osteoporosis is a common health problem that affects one-third of women over the age of 50 and it may not be detected until bone fractures occur. Osteoporosis is low bone mass and microarchitectural deterioration of bone tissue, which affects bone fragility and raises fracture risks. Early mandible bone osteoporosis detection could help reduce the risk of jaw fracture and dental implant failure. To solve this problem, a diagnostic algorithm for automatic detection of osteoporosis in Cone-Beam Computed Tomography (CBCT) images is presented and 120 mandible CBCT images of 50-85 year-old women have been utilized. These images are classified into two classes: normal and osteoporotic. Their classification is based on the T-score which derives from the Dual-Energy X-ray Absorptiometry (DEXA). The proposed algorithm consists of image processing, feature extraction, and Artificial Neural Network (ANN) classification. Images are segmented and edges are detected. Then, texture features are extracted from the segmented regions. Finally, a feed-forward back-propagation ANN classifier is employed. Seven parameters were involved in the experiment data preparation as input: coarseness, contrast, direction, number of edges, length of edges, mean length of edges, and the number of edge pixels. The results demonstrate the effectiveness of the proposed method. With the help of the proposed method, dentists will be able to predict osteoporosis accurately and efficiently without the need for further examination since CBCT has been widely accepted in dentistry and the dentist is the most common health care professional that elderly visit regularly.


artificial intelligence, neural network classifiers, osteoporosis, cone-beam CT, image processing


Download data is not yet available.


E. Gungor, D. Yıldırım, and R. Cevik, “Evaluation of osteoporosis in jaw bones using cone beam CT and dual-energy X-ray absorptiometry,” Journal of Oral Science, vol. 58, no. 2, pp. 185–194, Jun. 2016. DOI:

T. Link, “Osteoporosis Imaging: State of the Art and Advanced Imaging,” Radiology, vol. 263, no. 1, pp. 3–17, Apr. 2012. DOI:

F. Esmaeli, S. Payahoo, M. Mobasseri, M. Johari, and J. Yazdani, “Efficacy of radiographic density values of the first and second cervical vertebrae recorded by CBCT technique to identify patients with osteoporosis and osteopenia,” Journal of Dental Research, Dental Clinics, Dental Prospects, vol. 11, no. 3, pp. 189–194, Jul. 2017. DOI:

I. Barngkgei, I. Haffar, and R. Khattab, “Osteoporosis prediction from the mandible using cone-beam computed tomography,” Imaging science in dentistry, vol. 44, no. 4, pp. 263–271, Dec. 2014. DOI:

E. Klintstrom, Image Analysis for Trabecular Bone Properties on Cone-Beam CT Data. Linkoping, Sverige: Linkoping University Electronic Press, 2017.

Y. Hua, O. Nackaerts, J. Duyck, F. Maes, and R. Jacobs, “Bone quality assessment based on cone beam computed tomography imaging,” Clinical oral implants research, vol. 20, no. 8, pp. 767–771, Mar. 2009. DOI:

Y. Liu et al., “Calibration of cone beam CT using relative attenuation ratio for quantitative assessment of bone density: A small animal study,” International journal of computer assisted radiology and surgery, vol. 8, no. 5, pp. 733–739, Dec. 2012. DOI:

D. Kannan, L. Bijai Kumar, and P. Subiksha, “Cone Beam Computed Tomography Evaluation of Postmenopausal Alveolar Bone Changes in Osteoporotic Women,” International Journal of Oral Implantology & Clinical Research, vol. 6, no. 3, pp. 65–68, Sep. 2014. DOI:

I. Barngkgei, E. Joury, and A. Jawad, “An Innovative Approach In Osteoporosis Opportunistic Screening By The Dental Practitioner: The Use Of Cervical Vertebrae And Cone Beam Computed Tomography With Its Viewer Program,” Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, vol. 120, no. 5, pp. 651–659, Aug. 2015. DOI:

M. B. Alonso, T. Vasconcelos, L. Lopes, P. Watanabe, and D. Freitas, “Validation of cone-beam computed tomography as a predictor of osteoporosis using the Klemetti classification,” Brazilian Oral Research, vol. 30, no. 1, pp. 1–8, May 2016. DOI:

Suprijanto, Azhari, E. Juliastuti, A. Septyvergy, and N. Setyagar, “Dental panoramic image analysis for enhancement biomarker of mandibular condyle for osteoporosis early detection,” Journal of Physics: Conference Series, vol. 694, no. 1, Mar. 2016, Art no. 012066. DOI:

R. Mostafa, E. Arnout, and M. M. A. El-Fotouh, “Feasibility of Cone Beam Computed Tomography Radiomorphometric Analysis and Fractal Dimension in Assessment of Postmenopausal Osteoporosis in Correlation with Dual X-ray Absorptiometry,” Dento maxillo facial radiology, vol. 45, no. 7, Jul. 2016, Art no. 20160212. DOI:

E. Gungor, O. S. Aglarci, M. Unal, M. S. Dogan, and S. Guven, “Evaluation of mental foramen location in the 10-70 years age range using cone-beam computed tomography,” Nigerian Journal of Clinical Practice, vol. 20, no. 1, pp. 88–92, Jan. 2017. DOI:

M. Alkhader, A. Aldawoodyeh, and N. Abdo, “Usefulness of measuring bone density of mandibular condyle in patients at risk of osteoporosis: A cone beam computed tomography study,” European Journal of Dentistry, vol. 12, no. 3, pp. 363–368, Aug. 2018. DOI:

N. Shameena and R. Jabbar, “A Study of Preprocessing and Segmentation Techniques on Cardiac Medical Images,” International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 4, pp. 336–341, Apr. 2014.

T. Acharya and A. K. Ray, Image Processing: Principles and Applications. Hoboken, New Jersey: John Wiley & Sons, 2005. DOI:

Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1–8, Feb. 1997. DOI:

P. Scheunders, “A multivalued image wavelet representation based on multiscale fundamental forms,” IEEE Transactions on Image Processing, vol. 11, no. 5, pp. 568–575, May 2002. DOI:

W. Ma, J. Morel, S. Osher, and A. Chien, “An L1-based variational model for Retinex theory and its application to medical images,” in CVPR 2011, Providence, RI, USA, Jun. 2011, pp. 153–160. DOI:

E. H. Land, “Recent advances in retinex theory,” Vision Research, vol. 26, no. 1, pp. 7–21, Jan. 1986. DOI:

L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recognition, vol. 34, no. 3, pp. 721–725, Mar. 2001. DOI:

R. Muthukrishnan and R. Radha, “Edge detection techniques for image segmentation,” International Journal of Computer Science & Information Technology, vol. 3, no. 6, pp. 259–267, 2011. DOI:

J. Zhang and J. Hu, “Image Segmentation Based on 2D Otsu Method with Histogram Analysis,” in International Conference on Computer Science and Software Engineering, Hubei, China, Dec. 2008, vol. 6, pp. 105–108.

H. Tamura, S. Mori, and T. Yamawaki, “Textural Features Corresponding to Visual Perception,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 6, pp. 460–473, Jun. 1978. DOI:

N. Bagri and P. Johari, “A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval,” International Journal of Advanced Science and Technology, vol. 80, no. 4, pp. 41–52, Jul. 2015. DOI:

B. Klintström, E. Klintström, O. Smedby, and R. Moreno, “Feature Space Clustering for Trabecular Bone Segmentation,” presented at the Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromso, Norway, Jun. 2017, pp. 65–75. DOI:

T. Majtner and D. Svoboda, “Extension of Tamura Texture Features for 3D Fluorescence Microscopy,” in Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission, Zurich, Switzerland, Oct. 2012, pp. 301–307. DOI:

B. R. Kowalski and C. Bender, “k-Nearest Neighbor Classification Rule (pattern recognition) applied to nuclear magnetic resonance spectral interpretation,” Analytical Chemistry, vol. 44, no. 8, pp. 1405–1411, 1972.

R. Hecht-Nielsen, “Kolmogorov’s Mapping Neural Network Existence Theorem,” presented at the First International Conference on Neural Networks, San Diego, CA, USA, 1987, pp. 11–13.

C. Peterson, T. Rognvaldsson, and L. Lonnblad, “JETNET 3.0—A versatile artificial neural network package,” Computer Physics Communications, vol. 81, no. 1–2, pp. 185–220, Jun. 1994. DOI:


How to Cite

R. F. A. Marar, D. M. Uliyan, and H. A. Al-Sewadi, “Mandible Bone Osteoporosis Detection using Cone-beam Computed Tomography”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 4, pp. 6027–6033, Aug. 2020.


Abstract Views: 1143
PDF Downloads: 703

Metrics Information

Most read articles by the same author(s)