A Survey on Osteoporosis Detection Methods with a Focus on X-ray and DEXA Images

References

• N. B. Watts, B. Ettinger, and M. S. LeBoff. “FRAX facts,” J. Bone Miner. Res.,” Vol. 24, no. 6, pp. 975–79, 2009. doi: 10.1359/jbmr.090402
   PubMed Web of Science ®Google Scholar
• L. J. Melton III, and C. Cooper. “Magnitude and impact of osteoporosis and fractures,” in Osteoporosis, 2nd ed., R. Marcus, D. Feldman, and J. Kelsey, Ed. New York: Academic Press, 2001, pp. 557–77.
   Google Scholar
• R. Bartl, and B. Frisch. Osteoporosis: Diagnosis, Prevention, Therapy. Berlin: Springer Science & Business Media, 2009.
   Google Scholar
• F. Cosman, B. Herrington, S. Himmelstein, and R. Lindsay, “Radiographic absorptiometry: A simple method for determination of bone mass,” Osteoporos. Int., Vol. 2, no. 1, pp. 34–8, 1991. doi: 10.1007/BF01627076
   PubMed Web of Science ®Google Scholar
• A. J. Yates, P. D. Ross, E. Lydick, and R. S. Epstein, “Radiographic absorptiometry in the diagnosis of osteoporosis,” Am. J. Med., Vol. 98, no. 2, pp. 41S–7S, 1995. doi: 10.1016/S0002-9343(05)80045-2
   PubMedGoogle Scholar
• A. Rosholm, L. Hyldstrup, L. Baeksgaard L, M. Grunkin, and H. H. Thodberg, “Estimation of bone mineral density by digital X-ray radiogrammetry: Theoretical background and clinical testing,” Osteoporos. Int., Vol. 12, no. 11, pp. 961–9, 2001. doi: 10.1007/s001980170026
   PubMed Web of Science ®Google Scholar
• M. L. Bouxsein, L. Palermo, C. Yeung, and D. M. Black, “Digital X-ray radiogrammetry predicts hip, wrist and vertebral fracture risk in elderly women: A prospective analysis from the study of osteoporotic fractures,” Osteoporos. Int., Vol. 13, no. 5, pp. 358–65, 2002. doi: 10.1007/s001980200040
   PubMed Web of Science ®Google Scholar
• K. G. Faulkner, C. C. Glüer, S. Majumdar, P. Lang, K. Engelke, and H. K. Genant, “Noninvasive measurements of bone mass, structure, and strength: Current methods and experimental techniques,” AJR. Am. J. Roentgenol., Vol. 157, no. 6, pp. 1229–37, Dec 1991. doi: 10.2214/ajr.157.6.1950872
   PubMed Web of Science ®Google Scholar
• L. Nilas, J. Borg, A. Gotfredsen, and C. Christiansen, “Comparison of single-and dual-photon absorptiometry in postmenopausal bone mineral loss,” J. Nucl. Med., Vol. 26, no. 11, pp. 1257–62, Nov 1985.
   PubMed Web of Science ®Google Scholar
• L. M. Thorson, and H. W. Wahner, “Single-and dual-photon absorptiometry techniques for bone mineral analysis,” J. Nucl. Med. Technol., Vol. 14, no. 3, pp. 163–71, Sep 1986.
   Google Scholar
• J. Borg, A. Møllgaard, and B. J. Riis, “Single X-ray absorptiometry: Performance characteristics and comparison with single photon absorptiometry,” Osteoporos. Int., Vol. 1; 5, no. 5, pp. 377–81, Sep 1995. doi: 10.1007/BF01622260
   Web of Science ®Google Scholar
• T. L. Kelly, G. Crane, and D. T. Baran, “Single X-ray absorptiometry of the forearm: Precision, correlation, and reference data,” Calcif. Tissue Int., Vol. 54, no. 3, pp. 212–8, Mar 1994. doi: 10.1007/BF00301681
   PubMed Web of Science ®Google Scholar
• K. I. Jassamm, “Removal of random noise from conventional digital X-ray images,” International Archives of Photogrammetry and Remote Sensing, Vol. 29, pp. 113–7, 1993.
   Google Scholar
• T. Kirti, K. Jitendra, and S. Ashok, “Poisson noise reduction from X-ray images by region classification and response median filtering,” Sādhanā, Vol. 42, no. 6, pp. 855–63, June 2017. doi: 10.1007/s12046-017-0654-4
   Web of Science ®Google Scholar
• J. Lu, L. Wang, Y. Li, and T. Yahagi, “Noise removal for medical X-ray images in multiwavelet domain,” Int. J. Image. Graph., Vol. 8, no. 01, pp. 25–46, Jan 2008. doi: 10.1142/S0219467808002952
   Google Scholar
• B. Luo, Z. Sun, M. Xue, and H. Liu. “ Improved noise reduction algorithms for medical X-ray images.” Proceedings of the International Conference on Consumer Electronics, Communications and Networks. 2003, pp. 359–62.
   Google Scholar
• A. Materka, P. Cichy, and J. Tuliszkiewicz. “Texture analysis of X-ray images for detection of changes in bone mass and structure,” in Texture Analysis in Machine Vision. Singapore: World Scientific, 2000, pp. 189–95.
   Google Scholar
• E. Hassani, J. El Hassouni, M. Rziza, and E. Lespessailles. “ Texture analysis for trabecular bone X-ray images using anisotropic morlet wavelet and Rényi entropy.” Proceedings of the International Conference on Image and Signal Processing; Springer, Berlin, Heidelberg, 2012, pp. 290–7.
   Google Scholar
• F. Yger. “ Application of covariance matrices and wavelet marginals.” arXiv:1410.2663, 2014.
   Google Scholar
• R. Kavya, and J. M. Shivram, “Texture based bone-radiograph image analysis for the assessment of osteoporosis,” Int. J. Eng. Res. Technol., Vol. 4, no. 6, pp. 700–706, 2015.
   Google Scholar
• D. W. Yap, Y. Chen, W. K. Leow, and T. S. Howe. “ Detecting femur fractures by texture analysis of trabeculae.” Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 730–733.
   Google Scholar
• O. Bandyopadhyay, B. Chanda, and B. B. Bhattacharya, “Automatic segmentation of bones in X-ray images based on entropy measure,” Int. J. Image. Graph., Vol. 16, no. 01, pp. 1650001, 2016. doi: 10.1142/S0219467816500017
   Google Scholar
• P. Santhoshini, R. Tamilselvi, and R. Sivakumar, “Automatic segmentation of femur bone features and analysis of osteoporosis,” Lect. Notes Softw. Eng., Vol. 1, no. 2, pp. 194, 2013. doi: 10.7763/LNSE.2013.V1.44
   Google Scholar
• A. Mikhaylichenko, Y. Demyanenk, and E. Grushko. “ Automatic detection of bone contours in X-ray images.” InAIST (Supplement). pp. 212–23. 2016.
   Google Scholar
• Y. Chen, X. Ee, W. K. Leow, and T. S. Howe. “ Automatic extraction of femur contours from hip x-ray images.” International Workshop on Computer Vision for Biomedical Image Applications, Springer, Berlin, Heidelberg, 2005, pp. 200–9.
   Google Scholar
• G. Zamora, H. Sari-Sarraf, and L. R. Long. “ Hierarchical segmentation of vertebrae from X-ray images.” Proc. of SPIE Medical Imaging: Image Processing. 2003, p. 5032.
   Google Scholar
• J. Wu, and M. R. Mahfouz, “Robust x-ray image segmentation by spectral clustering and active shape model,” J. Med. Imaging, Vol. 3, no. 3, pp. 034005, Sep 2016. doi: 10.1117/1.JMI.3.3.034005
   Web of Science ®Google Scholar
• N. Boukala, E. Favier, B. Laget, and P. Radeva. “ Active shape model based segmentation of bone structures in hip radiographs.” Proceedings of IEEE International Conference on Industrial Technology, 2004, pp. 1682–7. IEEE.
   Google Scholar
• F. Ding, W. K. Leow, and T. S. Howe. “ Automatic segmentation of femur bones in anterior-posterior pelvis x-ray images.” Proceedings of International Conference on Computer Analysis of Images and Patterns, 2007, pp. 205–12. Springer, Berlin, Heidelberg.
   Google Scholar
• M. G. Roberts, T. F. Cootes, and J. E. Adams. “ Vertebral shape: Automatic measurement with dynamically sequenced active appearance models.” Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005, pp. 733–40. Springer, Berlin, Heidelberg.
   Google Scholar
• P. Sharma, and J. M. Singh, “A novel approach towards X-ray bone image segmentation using discrete step algorithm,” Int. J. Emerg. Trends Technol. Comput. Sci., Vol. 2, no. 5, pp. 191–5, 2013.
   Google Scholar
• T. Hegemann, C. A. Morariu, J. Pauli, and R. Jennane. “ Detection of osteoporosis in X-ray image data.” 1st YRA MedTech Symosium, Young Researchers Academy, MedTech,. NRW University of Duisburg-Essen, 2016, April 8, Duisburg, Germany.
   Google Scholar
• A. B. Pal, and M. Anburajan. “ Digital image processing of calcaneum X-ray in the evaluation of osteoporosis in women: A comparison with DXA bone densitometer as a ‘standard’.” Proceedings of the International conference on Electronics and Communication Systems, 2016, pp. 1865–9.
   Google Scholar
• K. Harrar, and L. Hamami, “An interconnectivity index for osteoporosis assessment using X-ray images,” J. Med. Biol. Eng., Vol. 33, pp. 569–75, 2012. doi: 10.5405/jmbe.1294
   Web of Science ®Google Scholar
• R. Riandini, and M. K. Delimayanti, “Feature extraction and classification of Thorax X-ray image in the assessment of osteoporosis,” Proc. Electr. Eng. Comput. Sci. Inform., Vol. 1, no. 4, pp. 160–4, Oct. 2017.
   Google Scholar
• N. Aga Shankar, V. Sathish Babu, and R. Mohan, “Analysis of trabecular bone of femur radiographs for detecting osteoporosis using MWT and DWT,” Int. J. Pure. Appl. Math., Vol. 119, no. 15, pp. 2129–40, 2018.
   Google Scholar
• K. Supaporn, and C. Kosin. “ Femur bone volumetric estimation from a single X-ray image for osteoporosis diagnosis.” International Symposium on Communications and Information Technologies, Oct. 2006, pp. 1149–52.
   Google Scholar
• C. A. Ligesh, N. Shanker, A. Vijay, M. Anburajan, and C. C. Glueer. “ Estimation of bone mineral density from the digital image of the calcanium bone.” Proceedings of 3rd International Conference on Electronics Computer Technology, pp. 365–9. 2011.
   Google Scholar
• V. Sapthagirivasan, and M. Anburajan. “ Analysis of trabecular proximal femur bone in diagnosing osteoporosis using digital X-ray: A comparison with DXA”, in Proceedings of International Conference on Recent Trends in Information Technology (ICRTIT), Jun 2011, pp. 869–574.
   Google Scholar
• C. Reshmalakshmi, and M. Sasikumar. “ Trabecular bone quality metric from X-ray images for osteoporosis detection.” International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 1694–7. July 2017.
   Google Scholar
• S. S. Khan, A. S. Jayan, and S. Nageswaran. “ An image processing algorithm to estimate bone mineral density using digital X-ray images.” Proceedings of Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–4. 2017.
   Google Scholar
• S. E. Lim, Y. Xing, Y. Chen, W. K. Leow, T. S. Howe, and M. A. Png. “ Detection of femur and radius fractures in X-ray images.” Proceedings of 2nd International Conference on Advances in Medical Signal and Info. 2004.
   Google Scholar
• T. K. Hazra, and S. Dutta, “A new approach to identify the fracture zone and detection of bone diseases of X-ray image,” Int. J. Sci. Res. (IJSR), Vol. 5, no. 9, pp. 1640–1646, Sep 2016. doi: 10.21275/ART20161989
   Google Scholar
• E. I. Sela, and R. Widyaningrum, “Osteoporosis detection using important shape-based features of the porous trabecular bone on the dental X-ray images,” Int. J. Adv. Comput Sci Appl, Vol. 6, no. 9, pp. 247–250, Sep 2015.
   Google Scholar
• D. Herumurti, A. Arifin, R. Sulaeman, A. Asano, A. Taguchi, T. Nakamoto, and K. Uchimura, “Weighted fuzzy ARTMAP for osteoporosis detection,” , 89–95, Feb 2010.
   Google Scholar
• E. I. Sela, S. Hartati, A. Harjoko, R. Wardoyo, and M. Mudjosemedi, “Feature selection of the combination of porous trabecular with anthropometric features for osteoporosis screening,” Int. J. Electr. Comput. Eng., Vol. 5, no. 1, pp. 78, Feb 2015.
   Google Scholar
• E. I. Sela, S. Hartati, A. Harjoko, R. Wardoyo, and M. S. Munakhir, “Segmentation on the dental periapical X-Ray images for osteoporosis screening,” (IJACSA) Int. J. Adv. Comput. Sci. Appl., Vol. 4, no. 7, pp. 147–151, Jul 2013.
   Google Scholar
• H. S. Bennett, A. Dienstfrey, L. T. Hudson, T. Oreskovic, T. Fuerst, and J. Shepherd, “Standards and measurements for assessing bone health – workshop report co-sponsored by the International Society for Clinical Densitometry (ISCD) and the National Institute of Standards and Technology (NIST),” J. Clin. Densitom., Vol. 1; 9, no. 4, pp. 399–405, Oct 2006. doi: 10.1016/j.jocd.2006.06.003
   Web of Science ®Google Scholar
• A. Dhainaut, M. Hoff, U. Syversen, and G. Haugeberg, “Technologies for assessment of bone reflecting bone strength and bone mineral density in elderly women: an update,” Women’s Health, Vol. 12, no. 2, pp. 209–16, Mar 2016. doi: 10.2217/whe.15.94
   PubMedGoogle Scholar
• R. H. Choplin, L. Lenchik, and S. Wuertzer, “A practical approach to interpretation of dual-energy X-ray absorptiometry (DXA) for assessment of bone density,” Curr. Radiol. Rep., Vol. 2, no. 6, pp. 48, Jun 2012. doi: 10.1007/s40134-014-0048-x
   Google Scholar
• I. M. Van der Sluis, M. A. De Ridder, A. M. Boot, E. P. Krenning, and S. M. de Muinck Keizer-Schrama, “Reference data for bone density and body composition measured with dual energy x ray absorptiometry in white children and young adults,” Archives of Disease in Childhood.1, Vol. 87, no. 4, pp. 341–7, Oct 2002. doi: 10.1136/adc.87.4.341
   PubMed Web of Science ®Google Scholar
• S. N. Fathima, R. Tamilselvi, and M. P. Beham. “ DEXSIT: A benchmark database for BMD measurement and analysis.” Proceedings of Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII), Mar 2018, pp. 206–12.
   Google Scholar
• J. J. Carey, and M. F. Delaney. “T-scores and Z-scores,” Clin. rev. bone miner. metab., Vol. 8 no. 3, pp. 113–21, Sep. 2010. doi: 10.1007/s12018-009-9064-4
   Google Scholar
• J. W. Kwon, S. I. Cho, Y. B. Ahn, and Y. M. Ro. “ Noise reduction in DEXA image based on system noise modeling.” Proceedings of International Conference on Biomedical and Pharmaceutical Engineering, Dec 2009, pp. 1–6.
   Google Scholar
• M. A. Al-antari, et al. “ Non-local means filter denoising for DEXA images.” Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul 2017, pp. 572–5.
   Google Scholar
• O. Ahmad, K. Ramamurthi, K. E. Wilson, K. Engelke, R. L. Prince, and R. H. Taylor, “Volumetric DXA (VXA): a new method to extract 3D information from multiple in vivo DXA images,” J. Bone Miner. Res., Vol. 25, no. 12, pp. 2744–51, Dec 2010. doi: 10.1002/jbmr.140
   PubMed Web of Science ®Google Scholar
• L. Humbert, T. Whitmarsh, M. De Craene, L. M. del Río Barquero, K. Fritscher, R. Schubert, F. Eckstein, T. Link, and A. F. Frangi. “ 3D reconstruction of both shape and bone mineral density distribution of the femur from DXA images.” Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Apr 2010, pp. 456–9.
   Google Scholar
• T. Whitmarsh, L. Humbert, M. De Craene, L. M. Barquero, and A. F. Frangi, “Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray absorptiometry,” IEEE Trans. Med. Imaging, Vol. 30, no. 12, pp. 2101–14, Jul 2011. doi: 10.1109/TMI.2011.2163074
   PubMed Web of Science ®Google Scholar
• T. Whitmarsh, K. D. Fritscher, L. Humbert, and L. M. DelRio-Barquero. Rainer Schubert and Alejandro F. Frangi “Hip fracture discrimination using 3D reconstructions from dual-energy X-ray absorptiometry”, The Biomedical Engineering International Conference, 2012.
   Google Scholar
• F. Jahan. “ An investigation of bone image texture analysis for predicting fracture risk,” 2010, Thesis, University of Manitoba, Canada.
   Google Scholar
• R. S. Lu, E. Dennison, H. Denison, C. Cooper, M. Taylor, and M. J. Bottema, “Texture analysis based on Gabor filters improves the estimate of bone fracture risk from DXA images,” Comput. Methods Biomech. Biomed. Eng.: Imaging Vis., Vol. 6, no. 4, pp. 453–64, Jul 2018.
   Web of Science ®Google Scholar
• M. Roberts, T. Cootes, E. Pacheco, and J. Adams, “Quantitative vertebral fracture detection on DXA images using shape and appearance models,” Acad. Radiol., Vol. 14, no. 10, pp. 1166–78, Oct 2007. doi: 10.1016/j.acra.2007.06.012
   PubMed Web of Science ®Google Scholar
• T. A. Burkhart, K. L. Arthurs, and D. M. Andrews, “Manual segmentation of DXA scan images results in reliable upper and lower extremity soft and rigid tissue mass estimates,” J. Biomech., Vol. 42, no. 8, pp. 1138–42, May 2009. doi: 10.1016/j.jbiomech.2009.02.017
   PubMed Web of Science ®Google Scholar
• C. Ruth, H. P. Weiss, K. E. Wilson, E. Von Stetten, and T. Richardson, inventors; Hologic Inc, assignee. Automatic region of interest locator for AP spinal images and for hip images in bone densitometry. United States patent US 7,609,871. Oct 2009.
   Google Scholar
• C. Ruth, H. P. Weiss, K. E. Wilson, E. Von Stetten, and T. Richardson. inventors; Hologic Inc, assignee. Automatic region of interest locator for AP spinal images and for hip images in bone densitometry. United States patent US 6,853,741. Feb 2005.
   Google Scholar
• D. Hussain, M. A. Al-antari, M. A. Al-masni, S. M. Han, and T. S. Kim, “Femur segmentation in DXA imaging using a machine learning decision tree,” J. Xray. Sci. Technol., Vol. 26, no. 5, pp. 727–46, 2018.
   PubMed Web of Science ®Google Scholar
• V. Mahadevan, and V. Sapthagirivasan, “Information processing of medical images for the detection of osteoporosis in hip region of interest,” Int. J. Innov., Manag. Technol., Vol. 1, no. 5, pp. 516, Dec 2010.
   Google Scholar
• K. E. Naylor, E. V. McCloskey, R. Eastell, and L. Yang, “Use of DXA-based finite element analysis of the proximal femur in a longitudinal study of hip fracture,” J. Bone Miner. Res., Vol. 28, no. 5, pp. 1014–21, May 2013. doi: 10.1002/jbmr.1856
   PubMed Web of Science ®Google Scholar
• P. A. Bromiley, J. E. Adams, and T. F. Cootes, “Localisation of vertebrae on DXA images using constrained local models with random forest regression voting,” in Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Switzerland: Springer. pp. 159–71, 2015.
   Google Scholar
• V. Sapthagirivasan, and M. Anburajan, “Diagnosis of osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: An investigation panorama with DXA,” Comput. Biol. Med., Vol. 43, no. 11, pp. 1910–9, Nov 2013. doi: 10.1016/j.compbiomed.2013.09.002
   PubMed Web of Science ®Google Scholar
• B. C. Silva, W. D. Leslie, H. Resch, O. Lamy, O. Lesnyak, N. Binkley, E. V. McCloskey, J. A. Kanis, and J. P. Bilezikian, “Trabecular bone score: A noninvasive analytical method based upon the DXA image,” J. Bone Miner. Res., Vol. 29, no. 3, pp. 518–30, Mar 2014. doi: 10.1002/jbmr.2176
   PubMed Web of Science ®Google Scholar
• L. Chen, and V. S. Prasath, “Measuring bone density connectivity using dual energy X-ray absorptiometry images,” Int. J. Fuzzy Log. Intell. Syst., Vol. 17, no. 4, pp. 235–44, Dec 2017. doi: 10.5391/IJFIS.2017.17.4.235
   Web of Science ®Google Scholar
• D. Hussain, and S. M. Han, “Computer-aided osteoporosis detection from DXA imaging,” Comput. Methods Programs Biomed., Vol. 173, pp. 87–107, May 2019. doi: 10.1016/j.cmpb.2019.03.011
   PubMed Web of Science ®Google Scholar
• H. Y. Chai, L. K. Wee, T. T. Swee, S. H. Salleh, and A. K. Ariff, “Gray-level co-occurrence matrix bone fracture detection,” Amer. J. Appl. Sci., Vol. 8, no. 1, pp. 26, Jan 2011. doi: 10.3844/ajassp.2011.26.32
   Google Scholar
• A. Kaur, and K. S. Mann, “Segmenting bone parts for bone age assessment using point distribution model and contour modelling,” Int. J. Phys., Vol. 933, no. 1, pp. 012004, 2018.
   Google Scholar
• M. Wolski, P. Podsiadlo, and G. W. Stachowiak, “Directional fractal signature methods for trabecular bone texture in hand radiographs: Data from the osteoarthritis initiative,” Med. Phys., Vol. 41, no. 8Part1, pp. 081914, 2014. doi: 10.1118/1.4890101
   PubMedGoogle Scholar
• E. Juliastuti, Y. Diputra, and M. Mayantasari. “ Dental panoramic image analysis on mandibular bone for osteoporosis early detection.” Proceedings of 3rd International Conference on Instrumentation Control and Automation (ICA). pp. 138–43. 2013.
   Google Scholar
• T. C. Anu, and R. Raman, “Detection of bone fracture using image processing methods,” Int. J. Comput. Appl., Vol. 975, pp. 8887, 2015.
   Google Scholar
• S. K. Mahendran, and S. S. Baboo, “Ensemble systems for automatic fracture detection,” Int. J. Eng. Technol., Vol. 4, no. 1, pp. 7, Feb 2012. doi: 10.7763/IJET.2012.V4.310
   Google Scholar
• B. L. Riggs, H. W. Wahner, E. Seeman, K. P. Offord, W. L. Dunn, R. B. Mazess, K. A. Johnson, and L. J. Melton, “Changes in bone mineral density of the proximal femur and spine with aging: Differences between the postmenopausal and senile osteoporosis syndromes,” J. Clin. Invest., Vol. 70, no. 4, pp. 716–23, 1982. doi: 10.1172/JCI110667
   PubMed Web of Science ®Google Scholar