Medical Imaging in the Diagnosis of Osteoporosis and Estimation of the Individual Bone Fracture Risk

  • Mark A. Haidekker
  • Geoff Dougherty
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)


Osteoporosis is a degenerative disease of the bone. In an advanced state, bone weakened by osteoporosis may fracture spontaneously with debilitating consequences. Beginning osteoporosis can be treated with exercise and calcium/vitamin D supplement, whereas osteoclast-inhibiting drugs are used in advanced stages. Choosing the proper treatment requires accurate diagnosis of the degree of osteoporosis. The most commonly used measurement of bone mineral content or bone mineral density provides a general orientation, but is insufficient as a predictor for load fractures or spontaneous fractures. There is wide agreement that the averaging nature of the density measurement does not take into account the microarchitectural deterioration, and imaging methods that provide a prediction of the load-bearing quality of the trabecular network are actively investigated. Studies have shown that X-ray projection images, computed tomography (CT) images, and magnetic resonance images (MRI) contain texture information that relates to the trabecular density and connectivity. In this chapter, image analysis methods are presented which allow to quantify the degree of microarchitectural deterioration of trabecular bone and have the potential to predict the load-bearing capability of bone.


Fractal Dimension Bone Density Trabecular Bone Bone Strength Fractional Brownian Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Klibanski, A., Adams-Campbell, L., Bassford, T., Blair, S.N., Boden, S.D., Dickersin, K., et al.: Osteoporosis prevention, diagnosis, and therapy. J. Am. Med. Assoc 285(6), 785–795 (2001)CrossRefGoogle Scholar
  2. 2.
    Hernandez, C.J., Keaveny, T.M.: A biomechanical perspective on bone quality. Bone 39(6), 1173–1181 (2006)CrossRefGoogle Scholar
  3. 3.
    Holroyd, C., Cooper, C., Dennison, E.: Epidemiology of osteoporosis. Best Pract. Res. Clin. Endocrinol. Metabol. 22(5), 671–685 (2008)CrossRefGoogle Scholar
  4. 4.
    Ritchie, R.O.: How does human bone resist fracture? Ann. New York Acad. Sci. 1192, 72–80 (2010)CrossRefGoogle Scholar
  5. 5.
    Small, R.E.: Uses and limitations of bone mineral density measurements in the management of osteoporosis. Medsc. Gen. Med. 7(2), 3 (2005)Google Scholar
  6. 6.
    Gennari, C.: Calcium and vitamin D nutrition and bone disease of the elderly. Publ. Health Nutr. 4, 547–559 (2001)Google Scholar
  7. 7.
    Rittweger, J.: Can exercise prevent osteoporosis? J. Musculosceletal Neuronal Interact. 6(2), 162 (2006)Google Scholar
  8. 8.
    Felsenberg, D., Boonen, S.: The bone quality framework: Determinants of bone strength and their interrelationships, and implications for osteoporosis management. Clin. Therapeut. 27(1), 1–11 (2005)CrossRefGoogle Scholar
  9. 9.
    Frost, H.M.: Dynamics of bone remodeling. Bone Biodynamics 315 (1964)Google Scholar
  10. 10.
    Wolff, I., Van Croonenborg, J.J., Kemper, H.C.G., Kostense, P.J., Twisk, J.W.R.: The effect of exercise training programs on bone mass: a meta-analysis of published controlled trials in pre-and postmenopausal women. Osteoporos. Int. 9(1), 1–12 (1999)CrossRefGoogle Scholar
  11. 11.
    Karlsson, M.K., Nordqvist, A., Karlsson, C.: Physical activity, muscle function, falls and fractures. Food Nutr. Res. 52 (2008)Google Scholar
  12. 12.
    Meunier, P.J., Sebert, J.L., Reginster, J.Y., Briancon, D., Appelboom, T., Netter, P., et al.: Fluoride salts are no better at preventing new vertebral fractures than calcium-vitamin D in postmenopausal osteoporosis: the FAVOStudy. Osteoporos. Int. 8(1), 4–12 (1998)CrossRefGoogle Scholar
  13. 13.
    Riggs, B.L., Hodgson, S.F., O’Fallon, W.M., Chao, E., Wahner, H.W., Muhs, J.M., et al.: Effect of fluoride treatment on the fracture rate in postmenopausal women with osteoporosis. Obstet. Gynecol. Surv. 45(8), 542 (1990)CrossRefGoogle Scholar
  14. 14.
    McCreadie, B.R., Goldstein, S.A.: Biomechanics of fracture: Is bone mineral density sufficient to assess risk? J. Bone Miner. Res. 15(12), 2305–2308 (2000)CrossRefGoogle Scholar
  15. 15.
    Rockoff, S.D., Sweet, E., Bleustein, J.: The relative contribution of trabecular and cortical bone to the strength of human lumbar vertebrae. Calcif. Tissue Int. 3(1), 163–175 (1969)CrossRefGoogle Scholar
  16. 16.
    Fields, A.J., Eswaran, S.K., Jekir, M.G., Keaveny, T.M.: Role of trabecular microarchitecture in whole-vertebral body biomechanical behavior. J. Bone Miner. Res. 24(9), 1523–1530 (2009)CrossRefGoogle Scholar
  17. 17.
    Keaveny, T.M., Morgan, E.F., Niebur, G.L., Yeh, O.C.: Biomechanics of trabecular bone. Annu. Rev. Biomed. Eng. 3(1), 307–333 (2001)CrossRefGoogle Scholar
  18. 18.
    Hernandez, C.J.: How can bone turnover modify bone strength independent of bone mass? Bone 42(6), 1014–1020 (2008)CrossRefGoogle Scholar
  19. 19.
    Ammann, P., Rizzoli, R.: Bone strength and its determinants. Osteoporos. Int. 14(S3), 13–18 (2003)Google Scholar
  20. 20.
    Chappard, D., Baslé, M.F., Legrand, E., Audran, M.: Trabecular bone microarchitecture: A review. Morphologie 92(299), 162–170 (2008)Google Scholar
  21. 21.
    Svendsen, O.L., Haarbo, J., Hassager, C., Christiansen, C.: Accuracy of measurements of body composition by dual-energy x-ray absorptiometry in vivo. Am. J. Clin. Nutr. 57(5), 605 (1993)Google Scholar
  22. 22.
    Lang, T.F.: Quantitative computed tomography. Radiol. Clin. N. Am. 48(3), 589–600 (2010)CrossRefGoogle Scholar
  23. 23.
    Bushberg, J., Seibert, J., Leidholdt, Jr. E.M., Boone, J.M.: The essential Physics of medical imaging. Lippincott Williams & Wilkins, New York (2002)Google Scholar
  24. 24.
    Njeh, C.F., Boivin, C.M., Langton, C.M.: The role of ultrasound in the assessment of osteoporosis: a review. Osteoporos. Int. 7(1), 7–22 (1997)CrossRefGoogle Scholar
  25. 25.
    Liu, X.S., Sajda, P., Saha, P.K., Wehrli, F.W., Bevill, G., Keaveny, T.M., et al.: Complete volumetric decomposition of individual trabecular plates and rods and its morphological correlations with anisotropic elastic moduli in human trabecular bone. J. Bone Miner. Res. 23(2), 223–235 (2008)CrossRefGoogle Scholar
  26. 26.
    Parfitt, A.M.: Bone histomorphometry: standardization of nomenclature, symbols and units (summary of proposed system). Bone 9(1), 67–69 (1988)CrossRefGoogle Scholar
  27. 27.
    Hildebrand, T., Laib, A., Müller, R., Dequeker, J., Rüegsegger, P.: Direct three dimensional morphometric analysis of human cancellous bone: microstructural data from Spine, Femur, Iliac Crest, and Calcaneus. J. Bone Miner. Res. 14(7), 1167–1174 (1999)CrossRefGoogle Scholar
  28. 28.
    Hernandez, C.J., Beaupre, G.S., Keller, T.S., Carter, D.R.: The influence of bone volume fraction and ash fraction on bone strength and modulus. Bone 29(1), 74–78 (2001)CrossRefGoogle Scholar
  29. 29.
    Hildebrand, T., Rüegsegger, P.: A new method for the model-independent assessment of thickness in three-dimensional images. J. Microsc. 185(1), 67–75 (1997)CrossRefGoogle Scholar
  30. 30.
    Cortet, B., Bourel, P., Dubois, P., Boutry, N., Cotten, A., Marchandise, X.: CT scan texture analysis of the distal radius: influence of age and menopausal status. Rev. Rhum. (English edn.) 65(2), 109 (1998)Google Scholar
  31. 31.
    Ito, M., Ohki, M., Hayashi, K., Yamada, M., Uetani, M., Nakamura, T.: Trabecular texture analysis of CT images in the relationship with spinal fracture. Radiology 194(1), 55 (1995)Google Scholar
  32. 32.
    Thomsen, J.S., Ebbesen, E.N., Mosekilde, L.: Relationships between static histomorphometry and bone strength measurements in human iliac crest bone biopsies. Bone 22(2), 153–163 (1998)CrossRefGoogle Scholar
  33. 33.
    Saha, P.K., Gomberg, B.R., Wehrli, F.W.: Three-dimensional digital topological characterization of cancellous bone architecture. Int. J. Imag. Syst. Tech. 11(1), 81–90 (2000)CrossRefGoogle Scholar
  34. 34.
    Le, H.M., Holmes, R.E., Shors, E.C., Rosenstein, D.A.: Computerized quantitative analysis of the interconnectivity of porous biomaterials. Acta. Stereologica. 11, 267–267 (1992)Google Scholar
  35. 35.
    Vesterby, A., Gundersen, H.J.G., Melsen, F.: Star volume of marrow space and trabeculae of the first lumbar vertebra: sampling efficiency and biological variation. Bone 10(1), 7–13 (1989)CrossRefGoogle Scholar
  36. 36.
    Hahn, M., Vogel, M., Pompesius-Kempa, M., Delling, G.: Trabecular bone pattern factor–a new parameter for simple quantification of bone microarchitecture. Bone 13(4), 327–330 (1992)CrossRefGoogle Scholar
  37. 37.
    Laib, A., Hildebrand, T., Häuselmann, H.J., Rüegsegger, P.: Ridge number density: a new parameter for in vivo bone structure analysis. Bone 21(6), 541–546 (1997)CrossRefGoogle Scholar
  38. 38.
    Hildebrand, T., Rüegsegger, P.: Quantification of bone microarchitecture with the structure model index. Comput. Meth. Biomech. Biomed. Eng. 1(1), 15–23 (1997)CrossRefGoogle Scholar
  39. 39.
    Haidekker, M.A.: Advanced Biomedical Image Analysis. Wiley, Hoboken, NJ (2011)MATHGoogle Scholar
  40. 40.
    Dougherty, G.: Image enhancement in the spatial domain. In: Digital image processing for medical applications, p. 170–188. Cambridge University Press, New York (2009)Google Scholar
  41. 41.
    Caldwell, C.B., Willett, K., Cuncins, A.V., Hearn, T.C.: Characterization of vertebral strength using digital radiographic analysis of bone structure. Med. Phys. 22, 611 (1995)CrossRefGoogle Scholar
  42. 42.
    Lespessailles, E., Gadois, C., Kousignian, I., Neveu, J.P., Fardellone, P., Kolta, S., et al.: Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study. Osteoporos. Int. 19(7), 1019–1028 (2008)CrossRefGoogle Scholar
  43. 43.
    Haidekker, M.A., Andresen, R., Evertsz, C.J., Banzer, D., Peitgen, H.O.: Issues of threshold selection when determining the fractal dimension in HRCT slices of lumbar vertebrae. Br. J. Radiol. 73(865), 69 (2000)Google Scholar
  44. 44.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man. Cybern. Syst. Hum. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  45. 45.
    Laws, K.I.: Texture energy measures. Proc DARPA Image Unerstanding Workshop, pp. 47–51 (1979)Google Scholar
  46. 46.
    Lee, R.L., Dacre, J.E., Hart, D.J., Spector, T.D.: Femoral neck trabecular patterns predict osteoporotic fractures. Med. Phys. 29, 1391 (2002)CrossRefGoogle Scholar
  47. 47.
    Lespessailles, E., Gadois, C., Lemineur, G., Do-Huu, J.P., Benhamou, L.: Bone texture analysis on direct digital radiographic images: precision study and relationship with bone mineral density at the os calcis. Calcif. Tissue Int. 80(2), 97–102 (2007)CrossRefGoogle Scholar
  48. 48.
    Rachidi, M., Marchadier, A., Gadois, C., Lespessailles, E., Chappard, C., Benhamou, C.L.: Laws’ masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis. Skeletal. Radiol. 37(6), 541–548 (2008)CrossRefGoogle Scholar
  49. 49.
    Vokes, T., Lauderdale, D., Ma, S.L., Chinander, M., Childs, K., Giger, M.: Radiographic texture analysis of densitometric calcaneal images: Relationship to clinical characteristics and to bone fragility. J. Bone Miner. Res. 25(1), 56–63 (2010)CrossRefGoogle Scholar
  50. 50.
    Wilkie, J.R., Giger, M.L., Engh, Sr. C.A., Hopper, Jr. R.H., Martell, J.M.: Radiographic texture analysis in the characterization of trabecular patterns in periprosthetic osteolysis1. Acad. Radiol. 15(2), 176–185 (2008)CrossRefGoogle Scholar
  51. 51.
    Chappard, C., Brunet-Imbault, B., Lemineur, G., Giraudeau, B., Basillais, A., Harba, R., et al.: Anisotropy changes in post-menopausal osteoporosis: characterization by a new index applied to trabecular bone radiographic images. Osteoporos. Int. 16(10), 1193–1202 (2005)CrossRefGoogle Scholar
  52. 52.
    Brunet-Imbault, B., Lemineur, G., Chappard, C., Harba, R., Benhamou, C.L.: A new anisotropy index on trabecular bone radiographic images using the fast Fourier transform. BMC Med. Imag. 5(1), 4 (2005)CrossRefGoogle Scholar
  53. 53.
    Peitgen, H.O., Jürgens, H., Saupe, D.: Chaos and fractals: new frontiers of science. Springer, New York (2004)MATHGoogle Scholar
  54. 54.
    Mandelbrot, B.B.: The Fractal Geometry of Nature. Freeman, USA (1982)MATHGoogle Scholar
  55. 55.
    Martínez-Lopez, F., Cabrerizo-Vílchez, M., Hidalgo-Alvarez, R.: A study of the different methods usually employed to compute the fractal dimension1. Phys. Stat. Mech. Appl. 311, 411–428 (2002)MATHCrossRefGoogle Scholar
  56. 56.
    Saupe, D.: Algorithms for random fractals. In: Peitgen, H.-O., and Saupe, D. (eds.), The Science of Fractal Images, pp. 71–136. Springer, New York (1988)Google Scholar
  57. 57.
    Stein, M.C.: Nonparametric estimation of fractal dimension, vol. 1001 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. SPIE (1988)Google Scholar
  58. 58.
    Chung, H.W., Chu, C.C., Underweiser, M., Wehrli, F.W.: On the fractal nature of trabecular structure. Med. Phys. 21, 1535 (1994)CrossRefGoogle Scholar
  59. 59.
    Dubuc, B., Zucker, S., Tricot, C., Quiniou, J., Wehbi, D.: Evaluating the fractal dimension of surfaces. Proc. Roy. Soc. Lond. Math. Phys. Sci. 425(1868), 113–127 (1989)MathSciNetMATHCrossRefGoogle Scholar
  60. 60.
    Huang, Q., Lorch, J.R., Dubes, R.C.: Can the fractal dimension of images be measured? Pattern Recogn. 27(3), 339–349 (1994)CrossRefGoogle Scholar
  61. 61.
    Geraets, W.G., Van Der Stelt, P.F.: Fractal properties of bone. Dentomaxillofacial Radiology 29(3), 144 (2000)Google Scholar
  62. 62.
    Lopes, R., Betrouni, N.: Fractal and multifractal analysis: A review. Med. Image. Anal. 13(4), 634–649 (2009)CrossRefGoogle Scholar
  63. 63.
    Lundahl, T., Ohley, W., Kuklinski, W.: Analysis and interpolation of angiographic images by use of fractals. Computers in Cardiology, p. 355. Linkoping, Sweden (1985)Google Scholar
  64. 64.
    Ruttimann, U.E., Webber, R.L., Hazelrig, J.B.: Fractal dimension from radiographs of peridental alveolar bone:: A possible diagnostic indicator of osteoporosis. Oral. Surg. Oral. Med. Oral. Pathol. 74(1), 98–110 (1992)CrossRefGoogle Scholar
  65. 65.
    Webber, R., Underhill, T., Horton, R., Dixon, R., Pope, Jr. T.: Predicting osseous changes in ankle fractures. IEEE Eng. Med. Biol. Mag. 12(1), 103–110 (2002)CrossRefGoogle Scholar
  66. 66.
    Majumdar, S., Weinstein, R.S., Prasad, R.R.: Application of fractal geometry techniques to the study of trabecular bone. Med. Phys. 20, 1611 (1993)CrossRefGoogle Scholar
  67. 67.
    Southard, T.E., Southard, K.A.: Detection of simulated osteoporosis in maxillae using radiographic texture analysis. IEEE Trans. Biomed. Eng. 43(2), 123–132 (2002)CrossRefGoogle Scholar
  68. 68.
    Veenland, J., Grashuis, J., Van der Meer, F., Beckers, A., Gelsema, E.: Estimation of fractal dimension in radiographs. Med. Phys. 23, 585 (1996)CrossRefGoogle Scholar
  69. 69.
    Fortin, C., Kumaresan, R., Ohley, W., Hoefer, S.: Fractal dimension in the analysis of medical images. IEEE Eng. Med. Biol. Mag. 11(2), 65–71 (2002)CrossRefGoogle Scholar
  70. 70.
    Messent, E., Buckland-Wright, J., Blake, G.: Fractal analysis of trabecular bone in knee osteoarthritis (OA) is a more sensitive marker of disease status than bone mineral density (BMD). Calcif. Tissue Int. 76(6), 419–425 (2005)CrossRefGoogle Scholar
  71. 71.
    Dougherty, G., Henebry, G.M.: Fractal signature and lacunarity in the measurement of the texture of trabecular bone in clinical CT images. Med. Eng. Phys. 23(6), 369–380 (2001)CrossRefGoogle Scholar
  72. 72.
    Dong, P.: Test of a new lacunarity estimation method for image texture analysis. Int. J. Rem. Sens. 21(17), 3369–3373 (2000)CrossRefGoogle Scholar
  73. 73.
    Plotnick, R.E., Gardner, R.H., Hargrove, W.W., Prestegaard, K., Perlmutter, M.: Lacunarity analysis: a general technique for the analysis of spatial patterns. Phys. Rev. E 53(5), 5461–5468 (1996)CrossRefGoogle Scholar
  74. 74.
    Dougherty, G., Henebry, G.M.: Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis. Med. Eng. Phys. 24(2), 129–138 (2002)CrossRefGoogle Scholar
  75. 75.
    Zaia, A., Eleonori, R., Maponi, P., Rossi, R., Murri, R.: MR imaging and osteoporosis: Fractal lacunarity analysis of trabecular bone. IEEE Trans. Inform. Tech. Biomed. 10(3), 484–489 (2006)CrossRefGoogle Scholar
  76. 76.
    Panagiotopoulou, O.: Finite element analysis (FEA): applying an engineering method to functional morphology in anthropology and human biology. Ann. Hum. Biol. 36(5), 609–623 (2009)CrossRefGoogle Scholar
  77. 77.
    Vesterby, A., Mosekilde, L., Gundersen, H.J.G., Melsen, F., Holme, K., Sørensen, S.: Biologically meaningful determinants of the in vitro strength of lumbar vertebrae. Bone 12(3), 219–224 (1991)CrossRefGoogle Scholar
  78. 78.
    Jones, A.C., Wilcox, R.K.: Finite element analysis of the spine: Towards a framework of verification, validation and sensitivity analysis. Med. Eng. Phys. 30(10), 1287–1304 (2008)CrossRefGoogle Scholar
  79. 79.
    Lavaste, F., Skalli, W., Robin, S., Roy-Camille, R., Mazel, C.: Three-dimensional geometrical and mechanical modelling of the lumbar spine. J. Biomech. 25(10), 1153–1164 (1992)CrossRefGoogle Scholar
  80. 80.
    Kuo, C.S., Hu, H.T., Lin, R.M., Huang, K.Y., Lin, P.C., Zhong, Z.C., et al.: Biomechanical analysis of the lumbar spine on facet joint force and intradiscal pressure-a finite element study. BMC Muscoskel. Disord. 11, 151 (2010)CrossRefGoogle Scholar
  81. 81.
    Gibson, L.J.: The mechanical behaviour of cancellous bone. J. Biomech. 18(5), 317–328 (1985)CrossRefGoogle Scholar
  82. 82.
    Jensen, K.S., Mosekilde, L.: A model of vertebral trabecular bone architecture and its mechanical properties. Bone 11(6), 417–423 (1990)CrossRefGoogle Scholar
  83. 83.
    Hollister, S.J., Brennan, J.M., Kikuchi, N.: A homogenization sampling procedure for calculating trabecular bone effective stiffness and tissue level stress. J. Biomech. 27(4), 433–444 (1994)CrossRefGoogle Scholar
  84. 84.
    Müller, R., Rüegsegger, P.: Three-dimensional finite element modelling of non-invasively assessed trabecular bone structures. Med. Eng. Phys. 17(2), 126–133 (1995)CrossRefGoogle Scholar
  85. 85.
    Magland, J., Vasilic, B., Wehrli, F.W.: Fast Low Angle Dual Spin Echo (FLADE): A new robust pulse sequence for structural imaging of trabecular bone. Magn. Reson. Med. 55(3), 465–471 (2006)CrossRefGoogle Scholar
  86. 86.
    Karjalainen, J.P., Toyras, J., Riekkinen, O., Hakulinen, M., Jurvelin, P.S.: Ultrasound backscatter imaging provides frequency-dependent information on structure, composition and mechanical properties of human trabecular bone. Ultrasound Med. Biol. 35(8), 1376–1384 (2009)CrossRefGoogle Scholar
  87. 87.
    Haïat, G., Padilla, F., Svrcekova, M., Chevalier, Y., Pahr, D., Peyrin, F., et al.: Relationship between ultrasonic parameters and apparent trabecular bone elastic modulus: A numerical approach. J. Biomech. 42(13), 2033–2039 (2009)CrossRefGoogle Scholar
  88. 88.
    Hosokawa, A.: Effect of porosity distribution in the propagation direction on ultrasound waves through cancellous bone. IEEE Trans. Ultrason. Ferroelectrics Freq. Contr. 57(6), 1320–1328 (2010)CrossRefGoogle Scholar
  89. 89.
    Davison, K.S., Kendler, D.L., Ammann, P., Bauer, D.C., Dempster, D.W., Dian, L., et al.: Assessing fracture risk and effects of osteoporosis drugs: bone mineral density and beyond. Am. J. Med. 122(11), 992–997 (2009)CrossRefGoogle Scholar
  90. 90.
    Resch, H., Libanati, C., Farley, S., Bettica, P., Schulz, E., Baylink, D.J.: Evidence that fluoride therapy increases trabecular bone density in a peripheral skeletal site. J. Clin. Endocrinol. Metabol. 76(6), 1622 (1993)CrossRefGoogle Scholar
  91. 91.
    Grynpas, M.D.: Fluoride effects on bone crystals. J. Bone Miner. Res. 5(S1), S169–S175 (1990)Google Scholar
  92. 92.
    Jiang, Y., Zhao, J., Liao, E.Y., Dai, R.C., Wu, X.P., Genant, H.K.: Application of micro-CT assessment of 3-D bone microstructure in preclinical and clinical studies. J. Bone Miner. Metabol. 23, 122–131 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of GeorgiaAthensGeorgia
  2. 2.California State University Channel IslandsCamarilloUSA

Personalised recommendations