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Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

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Abstract

Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture.

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References

  1. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    PubMed  Google Scholar 

  2. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251

    PubMed  Google Scholar 

  3. Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, Mann RM (2018) Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290(2):305–314

    PubMed  Google Scholar 

  4. Ribli D, Horváth A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with deep learning. Sci Rep 8(1):4165

    PubMed  PubMed Central  Google Scholar 

  5. Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ (2019) Identifying pneumonia in chest X-rays: a deep learning approach. Measurement

  6. Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663–2674

    PubMed  Google Scholar 

  7. Kaplan FS (1985) Osteoporosis. Women Health 10(2/3):95–114

    Google Scholar 

  8. Riggs BL, Melton III LJ (1995) The worldwide problem of osteoporosis: insights afforded by epidemiology. Bone 17(5):S505–S511

    Google Scholar 

  9. Ringe JD, Welzel D (1987) Salmon calcitonin in the therapy of corticoid-induced osteoporosis. Eur J Clin Pharmacol 33(1):35–39

    CAS  PubMed  Google Scholar 

  10. Nguyen ND, Ahlborg HG, Center JR, Eisman JA, Nguyen TV (2007) Residual lifetime risk of fractures in women and men. J Bone Miner Res 22(6):781–788

    PubMed  Google Scholar 

  11. Cooper C, Campion G, Melton L (1992) Hip fractures in the elderly: a world-wide projection. Osteoporos Int 2(6):285–289

    CAS  PubMed  Google Scholar 

  12. Cooper C, Melton LJ III (1992) Epidemiology of osteoporosis. Trends Endocrinol Metab 3(6):224–229

    CAS  PubMed  Google Scholar 

  13. Chrischilles EA, Cooper C, Lane AW, Riggs BL (1992) Perspective: how many women have osteoporosis. J Bone Miner Res 7:1005–1010

    PubMed  Google Scholar 

  14. Cummings SR, Rubin SM, Black D (1990) The future of hip fractures in the United States. Numbers, costs, and potential effects of postmenopausal estrogen. Clin Orthop Relat Res (252):163–166

  15. Schneider EL, Guralnik JM (1990) The aging of America: impact on health care costs. Jama 263(17):2335–2340

    CAS  PubMed  Google Scholar 

  16. Government of India: Ministry of Home Affairs 2011, Office of the Registrar General and Census Commissioner, India. Available from:http://censusindia.gov.in

  17. Dhanwal DK, Siwach R, Dixit V, Mithal A, Jameson K, Cooper C (2013) Incidence of hip fracture in Rohtak district, North India. Arch Osteoporos 8(1-2):135

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Chrischilles E, Shireman T, Wallace R (1994) Costs and health effects of osteoporotic fractures. Bone 15(4):377–386

    CAS  PubMed  Google Scholar 

  19. Grazier KL (1984) The frequency of occurrence, impact, cost of selected musculoskeletal conditions in the United States. Am Acad Orthop Surg

  20. Phillips S, Fox N, Jacobs J, Wright WE (1988) The direct medical costs of osteoporosis for American women aged 45 and older, 1986. Bone 9(5):271–279

    CAS  PubMed  Google Scholar 

  21. Mithal A, Bansal B, Kyer CS, Ebeling P (2014) The Asia-pacific regional audit-epidemiology, costs, and burden of osteoporosis in India 2013: a report of international osteoporosis foundation. Indian J Endocrinol Metab 18(4):449

    PubMed  PubMed Central  Google Scholar 

  22. WHO Study Group (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group. World Health Organ Tech Rep Ser 843:1–129

    Google Scholar 

  23. Mohamed EI, Maiolo C, Linder R, Pöppl SJ, De Lorenzo A (2003) Artificial neural network analysis: a novel application for predicting site-specific bone mineral density. Acta Diabetol 40(1):s19–s22

    PubMed  Google Scholar 

  24. Sadatsafavi M, Moayyeri A, Soltani A, Larijani B, Nouraie M, Akhondzadeh S (2005) Artificial neural networks in prediction of bone density among post-menopausal women. J Endocrinol Investig 28(7):425–431

    CAS  Google Scholar 

  25. Rae SA, Wang WJ, Partridge D (1999) Artificial neural networks: a potential role in osteoporosis. J R Soc Med 92(3):119–122

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu Q, Cui X, Chou YC, Abbod MF, Lin J, Shieh JS (2015) Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders. Biomed Signal Process Control 21:146–156

    Google Scholar 

  27. Chiu JS, Li YC, Yu FC, Wang YF (2006) Applying an artificial neural network to predict osteoporosis in the elderly. Stud Health Technol Inform 124:609

    PubMed  Google Scholar 

  28. Abdel-Mageed SM, Bayoumi AM, Mohamed EI (2015) Artificial neural networks analysis for estimating bone mineral density in an Egyptian population: towards standardization of DXA measurements. Am J Neural Network Appl 1(3):52–56

    Google Scholar 

  29. de Cos Juez FJ, Suárez-Suárez MA, Lasheras FS, Murcia-Mazón A (2011) Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Math Comput Model 54(7–8):1665–1670

    Google Scholar 

  30. Shaikh AB, Sarim M, Raffat SK, Ahsan K, Nadeem A, Siddiq M (2014) Artificial neural network: a tool for diagnosing osteoporosis. Res J Recent Sci ISSN 2277:2502

    Google Scholar 

  31. Bortone I, Trotta GF, Cascarano GD, Regina P, Brunetti A, De Feudis I, Buongiorno D, Loconsole C, Bevilacqua V (2018) A supervised approach to classify the status of bone mineral density in post-menopausal women through static and dynamic baropodometry. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, Piscataway, pp 1–7

    Google Scholar 

  32. Akgundogdu A, Jennane R, Aufort G, Benhamou CL, Ucan ON (2010) 3D image analysis and artificial intelligence for bone disease classification. J Med Syst 34(5):815–828

    PubMed  Google Scholar 

  33. Iliou T, Anagnostopoulos CN, Stephanakis IM, Anastassopoulos G (2017) A novel data preprocessing method for boosting neural network performance: a case study in osteoporosis prediction. Inf Sci 380:92–100

    Google Scholar 

  34. Xu Y, Li D, Chen Q, Fan Y (2013) Full supervised learning for osteoporosis diagnosis using micro-CT images. Microsc Res Tech 76(4):333–341

    PubMed  Google Scholar 

  35. (1986) Osteoporosis. American Academy of Orthopaedic Surgeons, Park Ridge

  36. Jama N (2001) Consensus development panel on osteoporosis prevention, diagnosis, and therapy. Osteoporosis prevention, diagnosis, and therapy. J Am Med Assoc 85:785–795

    Google Scholar 

  37. Mac Kinnon JL (1988) Osteoporosis: a review. Phys Ther 68(10):1533–1540

    CAS  PubMed  Google Scholar 

  38. Medical News Today; https://www.medicalnewstoday.com/articles/155646.php

  39. Raisz LG (2005) Pathogenesis of osteoporosis: concepts, conflicts, and prospects. J Clin Invest 115(12):3318–3325

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Tu, K.N., Lie, J.D., Wan, C.K.V., Cameron, M., Austel, A.G., Nguyen, J.K., Van, K. and Hyun, D., 2018. Osteoporosis: a review of treatment options. Pharmacy and Therapeutics, 43(2), p.92

  41. NIH Osteoporosis and Related Bone Diseases National Resource Center. The Surgeon General’s report on bone health and osteoporosis: what it means to you. December 2015, Available at: www. niams.nih.gov/Health_Info/Bone/SGR/surgeon_generals_report. asp. Accessed June 6, 2017

  42. Graham BA, Gleit CJ (1984) Osteoporosis: a major health problem in postmenopausal women. Orthop Nurs 3(6):19–26

    CAS  PubMed  Google Scholar 

  43. Melton JJ, O’Fallon WM, Riggs BL (1987) Secular trends in the incidence of hip fractures. Calcif Tissue Int 41(2):57–64

    PubMed  Google Scholar 

  44. Krolner B, Nielsen SP (1982) Bone mineral content of the lumbar spine in normal and osteoporotic women: cross-sectional and longitudinal studies. Clin Sci 62(3):329–336

    CAS  Google Scholar 

  45. Arnold JS (1973) Amount and quality of trabecular bone in osteoporotic vertebral fractures. Clin Endocrinol Metab 2(2):221–238

    CAS  PubMed  Google Scholar 

  46. Gallagher JC, Aaron J, Horsman A, Marshall DH, Wilkinson R, Nordin BEC (1973) The crush fracture syndrome in postmenopausal women. Clin Endocrinol Metab 2(2):293–315

    CAS  PubMed  Google Scholar 

  47. Riggs BL, Wahner HW, Seeman E, Offord KP, Dunn WL, Mazess RB, Johnson KA, Melton LJ (1982) Changes in bone mineral density of the proximal femur and spine with aging: differences between the postmenopausal and senile osteoporosis syndromes. J Clin Invest 70(4):716–723

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Firooznia H, Golimbu C, Rafii M, Schwartz MS, Alterman ER (1984) Quantitative computed tomography assessment of spinal trabecular bone. II. In osteoporotic women with and without vertebral fractures. J Comput Tomogr 8(2):99–103

    CAS  PubMed  Google Scholar 

  49. Cann CE, Genant HK, Kolb FO, Ettinger B (1985) Quantitative computed tomography for prediction of vertebral fracture risk. Bone 6(1):1–7

    CAS  PubMed  Google Scholar 

  50. Alffram PA (1964) An epidemiologic study of cervical and trochanteric fractures of the femur in an urban population analysis of 1,664 cases with special reference to etiologic factors. Acta Orthop Scand 35(sup65):1–109

    Google Scholar 

  51. Knowelden J, Buhr AJ, Dunbar O (1964) Incidence of fractures in persons over 35 years of age: a report to the MRC working party on fractures in the elderly. Br J Prev Soc Med 18(3):130

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Gallagher JC, Melton LJ, Riggs BL, Bergstrath E (1980) Epidemiology of fractures of the proximal femur in Rochester, Minnesota. Clin Orthop Relat Res 150:163–171

    Google Scholar 

  53. Rose SH, Morrey BF, Ilstrup DM, Riggs BL (1982) Epidemiologic features of humeral fractures. Clin Orthop Relat Res 168:24–30

    Google Scholar 

  54. Alffram PA, Bauer GC (1962) Epidemiology of fractures of the forearm: a biomechanical investigation of bone strength. JBJS 44(1):105–114

    Google Scholar 

  55. Owen RA, Melton LJ 3rd, Johnson KA, Ilstrup DM, Riggs BL (1982) Incidence of Colles’ fracture in a North American community. Am J Public Health 72(6):605–607

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Sampson JM, Morrey BF, Ilstrup DM (1981) Epidemiologic features of pelvic fractures. Clin Orthop Relat Res 155:43–47

    Google Scholar 

  57. Garraway WM, Stauffer RN, Kurland LT, O’Fallon WM (1979) Limb fractures in a defined population. I. Frequency and distribution. In Mayo Clinic Proceedings (Vol. 54, no. 11, pp. 701-707)

  58. Orthoinfo; https://orthoinfo.aaos.org/en/diseases%2D%2Dconditions/osteoporosis-and-spinal-fractures/

  59. Spineuniverse; https://www.spineuniverse.com/conditions/osteoporosis/osteoporosis-compression-fractures

  60. Orthopedics; https://ryortho.com/breaking/incidence-of-osteopeniaosteoporosis-in-spine-fusion-patients/

  61. Mint Hill Dentistry; https://www.minthilldentistry.com/panoramic-x-ray

  62. Anil S, Preethanath RS, AlMoharib HS, Kamath KP, Anand PS (2013) Impact of osteoporosis and its treatment on oral health. Am J Med Sci 346(5):396–401

    PubMed  Google Scholar 

  63. healthlinehttps://www.healthline.com/health/osteoporosis#pictures

  64. Arthritis & Osteoporosis; https://www.arthritiswa.org.au/osteoporosis/

  65. http://peripheralnerve.org/meeting/abstracts/2017/images/AAHS1_1.jpg

  66. Adams JE (2009) Quantitative computed tomography. Eur J Radiol 71(3):415–424

    PubMed  Google Scholar 

  67. Hui SL, Slemenda CW, Johnston CC (1988) Age and bone mass as predictors of fracture in a prospective study. J Clin Invest 81(6):1804–1809

    CAS  PubMed  PubMed Central  Google Scholar 

  68. World Health Organization (ed) (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. WHO, Geneva World Health Organization, Geneve, 1994

    Google Scholar 

  69. Kanis JA (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: Synopsis of a WHO report. WHO study group. Osteoporos Int 4:368–381

    CAS  PubMed  Google Scholar 

  70. Faulkner, K.G., 2005. The tale of the T-score: review and perspective

    Google Scholar 

  71. Dimai HP (2017) Use of dual-energy X-ray absorptiometry (DXA) for diagnosis and fracture risk assessment; WHO-criteria, T-and Z-score, and reference databases. Bone 104:39–43

    PubMed  Google Scholar 

  72. Link TM (2012) Osteoporosis imaging: state of the art and advanced imaging. Radiology 263(1):3–17

    PubMed  PubMed Central  Google Scholar 

  73. Arifin AZ, Asano A, Taguchi A, Nakamoto T, Ohtsuka M, Tsuda M, Kudo Y, Tanimoto K (2006) Computer-aided system for measuring the mandibular cortical width on dental panoramic radiographs in identifying postmenopausal women with low bone mineral density. Osteoporos Int 17(5):753–759

    CAS  PubMed  Google Scholar 

  74. Kavitha MS, Samopa F, Asano A, Taguchi A, Sanada M (2012) Computer-aided measurement of mandibular cortical width on dental panoramic radiographs for identifying osteoporosis. J Investig Clin Dent 3(1):36–44

    CAS  PubMed  Google Scholar 

  75. Nakamoto T, Taguchi A, Verdonschot RG, Kakimoto N (2019) Improvement of region of interest extraction and scanning method of computer-aided diagnosis system for osteoporosis using panoramic radiographs. Oral Radiol 35(2):143–151

    PubMed  Google Scholar 

  76. Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ III, Khaltaev N (2008) A reference standard for the description of osteoporosis. Bone 42(3):467–475

    CAS  PubMed  Google Scholar 

  77. Genant HK, Engelke K, Fuerst T, Glüer CC, Grampp S, Harris ST, Jergas M, Lang T, Lu Y, Majumdar S, Mathur A (1996) Noninvasive assessment of bone mineral and structure: state of the art. J Bone Miner Res 11(6):707–730

    CAS  PubMed  Google Scholar 

  78. Rosholm A, Hyldstrup L, Baeksgaard L, Grunkin M, Thodberg HH (2001) Estimation of bone mineral density by digital X-ray radiogrammetry: theoretical background and clinical testing. Osteoporos Int 12(11):961–969

    CAS  PubMed  Google Scholar 

  79. Jørgensen JT, Andersen PB, Rosholm A, Bjarnason NH (2000) Digital X-ray radiogrammetry: a new appendicular bone densitometric method with high precision. Clin Physiol 20(5):330–335

    PubMed  Google Scholar 

  80. Areeckal AS, Kocher M (2018) Current and emerging diagnostic imaging-based techniques for assessment of osteoporosis and fracture risk. IEEE Rev Biomed Eng 12:254–268

    PubMed  Google Scholar 

  81. Böttcher J, Pfeil A, (2008) Diagnosis of periarticular osteoporosis in rheumatoid arthritis using digital X-ray radiogrammetry

  82. Bouxsein ML, Palermo L, Yeung C, Black DM (2002) 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 13(5):358–365

    CAS  PubMed  Google Scholar 

  83. Srinivasan S, Peh WC (2013) Radiography in osteoporosis. In: Osteoporosis and bone densitometry measurements. Springer, Berlin, pp 15–30

    Google Scholar 

  84. Brahem M, Jguirim M, Khemiss M, Chaabani I, Chebil E, Younes M, Alaya TB, Khelifa MB, Bejia I, Touzi M, Zrour S (2017) AB0848 dental panoramic radiography as a tool for identification of osteoporosis: among Tunisian women

  85. Yamada S, Uchida K, Iwamoto Y, Sugino N, Yoshinari N, Kagami H, Taguchi A (2015) Panoramic radiography measurements, osteoporosis diagnoses and fractures in Japanese men and women. Oral Dis 21(3):335–341

    CAS  PubMed  Google Scholar 

  86. Baum T, Eggl E, Malecki A, Schaff F, Potdevin G, Gordijenko O, Garcia EG, Burgkart R, Rummeny EJ, Noël PB, Bauer JS (2015) X-ray dark-field vector radiography—a novel technique for osteoporosis imaging. J Comput Assist Tomogr 39(2):286–289

    PubMed  Google Scholar 

  87. Zheng K, Makrogiannis S (2016) Bone texture characterization for osteoporosis diagnosis using digital radiography. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Piscataway, pp 1034–1037

    Google Scholar 

  88. Gokalp G, Mutlu FS, Yazici Z, Yildirim N (2011) Evaluation of vertebral bone marrow fat content by chemical-shift MRI in osteoporosis. Skelet Radiol 40(5):577–585

    Google Scholar 

  89. Bandirali M, Di Leo G, Papini GDE, Messina C, Sconfienza LM, Ulivieri FM, Sardanelli F (2015) A new diagnostic score to detect osteoporosis in patients undergoing lumbar spine MRI. Eur Radiol 25(10):2951–2959

    PubMed  Google Scholar 

  90. Ferizi U, Besser H, Hysi P, Jacobs J, Rajapakse CS, Chen C, Saha PK, Honig S, Chang G (2019) Artificial intelligence applied to osteoporosis: a performance comparison of machine learning algorithms in predicting fragility fractures from MRI data. J Magn Reson Imaging 49(4):1029–1038

    PubMed  Google Scholar 

  91. Chen Y, Guo Y, Zhang X, Mei Y, Feng Y, Zhang X (2018) Bone susceptibility mapping with MRI is an alternative and reliable biomarker of osteoporosis in postmenopausal women. Eur Radiol 28(12):5027–5034

    PubMed  Google Scholar 

  92. Guglielmi G, de Terlizzi F (2009) Quantitative ultrasound in the assessment of osteoporosis. Eur J Radiol 71(3):425–431

    PubMed  Google Scholar 

  93. Bouxsein ML, Coan BS, Lee SC (1999) Prediction of the strength of the elderly proximal femur by bone mineral density and quantitative ultrasound measurements of the heel and tibia. Bone 25(1):49–54

    CAS  PubMed  Google Scholar 

  94. Gregg EW, Kriska AM, Salamone LM, Roberts MM, Aderson SJ, Ferrell RE, Kuller LH, Cauley JA (1997) The epidemiology of quantitative ultrasound: a review of the relationships with bone mass, osteoporosis and fracture risk. Osteoporos Int 7(2):89–99

    CAS  PubMed  Google Scholar 

  95. Adams JE (2013) Advances in bone imaging for osteoporosis. Nat Rev Endocrinol 9(1):28

    CAS  PubMed  Google Scholar 

  96. Hans D, Baim S (2017) Quantitative ultrasound (QUS) in the management of osteoporosis and assessment of fracture risk. J Clin Densitom 20(3):322–333

    PubMed  Google Scholar 

  97. Zha XY, Hu Y, Pang XN, Chang GL, Li L (2015) Diagnostic value of Osteoporosis Self-Assessment Tool for Asians (OSTA) and quantitative bone ultrasound (QUS) in detecting high-risk populations for osteoporosis among elderly Chinese men. J Bone Miner Metab 33(2):230–238

    PubMed  Google Scholar 

  98. Damilakis J, Adams JE, Guglielmi G, Link TM (2010) Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur Radiol 20(11):2707–2714

    PubMed  PubMed Central  Google Scholar 

  99. Brett AD, Brown JK (2015) Quantitative computed tomography and opportunistic bone density screening by dual use of computed tomography scans. J Orthop Transl 3(4):178–184

    Google Scholar 

  100. Chen C, Zhang X, Guo J, Jin D, Letuchy EM, Burns TL, Levy SM, Hoffman EA, Saha PK (2018) Quantitative imaging of peripheral trabecular bone microarchitecture using MDCT. Med Phys 45(1):236–249

    PubMed  Google Scholar 

  101. Emami A, Ghadiri H, Rahmim A, Ay MR (2018) A novel dual energy method for enhanced quantitative computed tomography. J Instrum 13(01):P01030

    Google Scholar 

  102. Wesarg S, Hosseini AG, Erdt M, Kafchitsas K, Khan MF (2010) Segmental assessment and visualization of trabecular bone mineral density in vertebrae. In: Eurographics Workshop on Visual Computing for Biology and Medicine, pp 1–3

    Google Scholar 

  103. Jiang H, Yates CJ, Gorelik A, Kale A, Song Q, Wark JD (2018) Peripheral quantitative computed tomography (pQCT) measures contribute to the understanding of bone fragility in older patients with low-trauma fracture. J Clin Densitom 21(1):140–147

    PubMed  Google Scholar 

  104. McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108

    CAS  PubMed  Google Scholar 

  105. Castro-Mateos I, Pozo JM, Cootes TF, Wilkinson JM, Eastell R, Frangi AF (2014) Statistical shape and appearance models in osteoporosis. Curr Osteoporos Rep 12(2):163–173

    PubMed  Google Scholar 

  106. Thodberg HH, Rosholm A (2003) Application of the active shape model in a commercial medical device for bone densitometry. Image Vis Comput 21(13–14):1155–1161

    Google Scholar 

  107. Allen PD, Graham J, Farnell DJ, Harrison EJ, Jacobs R, Nicopolou-Karayianni K, Lindh C, van der Stelt PF, Horner K, Devlin H (2007) Detecting reduced bone mineral density from dental radiographs using statistical shape models. IEEE Trans Inf Technol Biomed 11(6):601–610

    PubMed  Google Scholar 

  108. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Google Scholar 

  109. Abhyankar SS, Shriram R (2012) Orthopentogram based osteoporosis prediction. Int J Eng Res Appl 2(6):753–758

    Google Scholar 

  110. Dendere R, Kabelitz G, Douglas TS (2013) Model-based segmentation of the middle phalanx in digital radiographic images of the hand. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Piscataway, pp 3702–3705

    Google Scholar 

  111. Sela EI, Widyaningrum R (2015) Osteoporosis detection using important shape-based features of the porous trabecular bone on the dental X-ray images. Int J Adv Comput Sci Appl 6(9):247–250

  112. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 6:681–685

    Google Scholar 

  113. Roberts MG, Graham J, Devlin H (2010) Improving the detection of osteoporosis from dental radiographs using active appearance models. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, Piscataway, pp 440–443

    Google Scholar 

  114. Roberts M, Cootes TF, Adams JE (2006) Vertebral morphometry: semiautomatic determination of detailed shape from dual-energy X-ray absorptiometry images using active appearance models. Investig Radiol 41(12):849–859

    Google Scholar 

  115. Sam M, Areeckal AS (2017) Early diagnosis of osteoporosis using active appearance model and metacarpal radiogrammetry. In: 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, Piscataway, pp 173–178

    Google Scholar 

  116. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Google Scholar 

  117. Kovaļovs, M. and Glazs, A., 2013. Trabecular bone segmentation by using an adaptive contour

  118. Su L, Fu X, Zhang X, Cheng X, Ma Y, Gan Y, Hu Q (2018) Delineation of carpal bones from hand X-ray images through prior model, and integration of region-based and boundary-based segmentations. IEEE Access 6:19993–20008

    Google Scholar 

  119. Southard TE, Southard KA (1996) Detection of simulated osteoporosis in maxillae using radiographic texture analysis. IEEE Trans Biomed Eng 43(2):123–132

    CAS  PubMed  Google Scholar 

  120. Pothuaud L, Benhamou CL, Porion P, Lespessailles E, Harba R, Levitz P (2000) Fractal dimension of trabecular bone projection texture is related to three-dimensional microarchitecture. J Bone Miner Res 15(4):691–699

    CAS  PubMed  Google Scholar 

  121. Benhamou CL, Lespessailles E, Jacquet G, Harba R, Jennane R, Loussot T, Tourliere D, Ohley W (1994) Fractal organization of trabecular bone images on calcaneus radiographs. J Bone Miner Res 9(12):1909–1918

    CAS  PubMed  Google Scholar 

  122. Jacquet G, Ohley WJ, Mont MA, Siffert R, Schmukler R (1990) Measurement of bone structure by use of fractal dimension. In: [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Piscataway, pp 1402–1403

    Google Scholar 

  123. Harrar K, Jennane R, Zaouchi K, Janvier T, Toumi H, Lespessailles E (2018) Oriented fractal analysis for improved bone microarchitecture characterization. Biomed Signal Process Control 39:474–485

    Google Scholar 

  124. Jennane R, Harba R, Lemineur G, Bretteil S, Estrade A, Benhamou CL (2007) Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection. Med Image Anal 11(1):91–98

    PubMed  Google Scholar 

  125. Roberts MG, Graham J, Devlin H (2013) Image texture in dental panoramic radiographs as a potential biomarker of osteoporosis. IEEE Trans Biomed Eng 60(9):2384–2392

    PubMed  Google Scholar 

  126. Harrar K, Jennane R (2015) Trabecular texture analysis using fractal metrics for bone fragility assessment. Structure 5:6

    Google Scholar 

  127. Kavitha MS, An SY, An CH, Huh KH, Yi WJ, Heo MS, Lee SS, Choi SC (2015) Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women. Oral Surg Oral Med Oral Pathol Oral Radiol 119(3):346–356

    PubMed  Google Scholar 

  128. Czyz M, Kapinas A, Holton J, Pyzik R, Boszczyk BM, Quraishi NA (2017) The computed tomography-based fractal analysis of trabecular bone structure may help in detecting decreased quality of bone before urgent spinal procedures. Spine J 17(8):1156–1162

    PubMed  Google Scholar 

  129. Tanaka T, Sakurai T, Kashima I (2001) Structuring of parameters for assessing vertebral bone strength by star volume analysis using a morphological filter. J Bone Miner Metab 19(3):150–158

    CAS  PubMed  Google Scholar 

  130. Boutry N, Cortet B, Dubois P, Marchandise X, Cotten A (2003) Trabecular bone structure of the calcaneus: preliminary in vivo MR imaging assessment in men with osteoporosis. Radiology 227(3):708–717

    PubMed  Google Scholar 

  131. White SC, Taguchi A, Kao D, Wu S, Yoon D, Suei Y, Nakamoto T, Tanimoto K (2005) Clinical and panoramic predictors of femur bone mineral density. Osteoporos Int 16(3):339–346

    PubMed  Google Scholar 

  132. Dalle Carbonare L, Valenti MT, Bertoldo F, Zanatta M, Zenari S, Realdi G, Cascio VL, Giannini S (2005) Bone microarchitecture evaluated by histomorphometry. Micron 36(7–8):609–616

    CAS  PubMed  Google Scholar 

  133. Schmah T, Marwan N, Thomsen JS, Saparin P (2011) Long range node-strut analysis of trabecular bone microarchitecture. Med Phys 38(9):5003–5011

    PubMed  Google Scholar 

  134. Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, Park W (2017) Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Rad 46(7):20170006

    Google Scholar 

  135. Materka A, Strzelecki M (1998) Texture analysis methods–a review. Technical university of lodz, institute of electronics, COST B11 report, Brussels, pp 9–11

    Google Scholar 

  136. Soh LK, Tsatsoulis C (1999) Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37(2):780–795

    Google Scholar 

  137. Alam FI, Faruqui RU (2011) Optimized calculations of haralick texture features. Eur J Sci Res 50(4):543–553

    Google Scholar 

  138. Korchiyne R, Farssi SM, Sbihi A, Touahni R ,Alaoui MT 2014 A combined method of fractal and GLCM features for MRI and CT scan images classification. arXiv preprint arXiv:1409.4559

  139. Shirvaikar M, Huang N, Dong XN (2016) The measurement of bone quality using gray level co-occurrence matrix textural features. J Med Imaging Health Inform 6(6):1357–1362

    PubMed  PubMed Central  Google Scholar 

  140. Areeckal AS, Kamath J, Zawadynski S, Kocher M (2018) Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data. Comput Med Imaging Graph 68:25–39

    PubMed  Google Scholar 

  141. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804

    Google Scholar 

  142. Mishra AK, Kim D, Andayana I (2011) Development of three dimensional binary patterns for local bone structure analysis. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, Piscataway, pp 1006–1008

    Google Scholar 

  143. Serra J (1979) Biomedical image analysis by mathematical morphology (author’s transl). Pathologie-biologie 27(4):205–207

    CAS  PubMed  Google Scholar 

  144. Veenland JF, Grashuis JL, Weinans H, Ding M, Vrooman HA (2002) Suitability of texture features to assess changes in trabecular bone architecture. Pattern Recogn Lett 23(4):395–403

    Google Scholar 

  145. Sevestre-Ghalila S, Benazza-Benyahia A, Ricordeau A, Mellouli N, Chappard C, Benhamou CL (2004) Texture image analysis for osteoporosis detection with morphological tools. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin, pp 87–94

    Google Scholar 

  146. Laws KI (1980) Textured image segmentation (no. USCIPI-940). University of Southern California Los Angeles Image Processing INST

  147. Singh M, Nagrath A, Maini PS (1970) Changes in trabecular pattern of the upper end of the femur as an index of osteoporosis. JBJS 52(3):457–467

    CAS  Google Scholar 

  148. Pramudito JT, Soegijoko S, Mengko TR, Muchtadi FI, Wachjudi RG (2007) Trabecular pattern analysis of proximal femur radiographs for osteoporosis detection. J Biomed Pharm Eng 1(1):45–51

    Google Scholar 

  149. Saville PD (1973) The syndrome of spinal osteoporosis. Best Pract Res Clin Endocrinol Metab 2(2):177–185

    CAS  Google Scholar 

  150. Jhamaria NL, Lal KB, Udawat M, Banerji P, Kabra SG (1983) The trabecular pattern of the calcaneum as an index of osteoporosis. J Bone Joint Surg Br Vol 65(2):195–198

    CAS  Google Scholar 

  151. Pande KC, Pande SK, de Takats D, McCloskey EV (2005) Modified calcaneal index: a new screening tool for osteoporosis based on plain radiographs of the calcaneum. J Orthop Surg 13(1):27–33

    CAS  Google Scholar 

  152. Nguyen C, Schlesinger KJ, James TW, James KM, Sah RL, Masuda K, Carlson JM (2018) Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone. R Soc Open Sci 5(8):180563

    PubMed  PubMed Central  Google Scholar 

  153. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Google Scholar 

  154. Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15

    PubMed  Google Scholar 

  155. Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J (2013) Artificial neural networks in medical diagnosis

  156. Carballido-Gamio J, Phan C, Link TM, Majumdar S (2006) Characterization of trabecular bone structure from high-resolution magnetic resonance images using fuzzy logic. Magn Reson Imaging 24(8):1023–1029

    PubMed  Google Scholar 

  157. Pothuaud L, Carceller P, Hans D (2008) Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: applications in the study of human trabecular bone microarchitecture. Bone 42(4):775–787

    PubMed  Google Scholar 

  158. Reshmalakshmi C, Sasikumar M (2017) Trabecular bone quality metric from X-ray images for osteoporosis detection. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, Piscataway, pp 1694–1697

    Google Scholar 

  159. Reshmalakshmi C, Sasikumar M (2016) Fuzzy inference system for osteoporosis detection. In: 2016 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, Piscataway, pp 675–681

    Google Scholar 

  160. Herumurti D, Arifin AZ, Sulaeman R, Asano A, Taguchi A, Nakamoto T, Uchimura K (2010) Weighted fuzzy ARTMAP for osteoporosis detection. Korean Institute of Electronics Engineers Other Publications, pp 89–95

  161. Aufort G, Jennane R, Harba R, Benhamou CL (2006) Hybrid skeleton graph analysis of disordered porous media. Application to trabecular bone. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol 2. IEEE, Piscataway, p II-II

    Google Scholar 

  162. Lee JH, Hwang YN, Park SY, Kim SM (2014) Diagnosis of osteoporosis by quantification of trabecular microarchitectures from hip radiographs using artificial neural networks. In: Bio-inspired computing-theories and applications. Springer, Berlin, pp 247–250

    Google Scholar 

  163. Vishnu T, Saranya K, Arunkumar R , Devi MG (2015).Efficient and early detection of osteoporosis using trabecular region. In Green engineering and technologies (IC-GET), 2015 Online International Conference on (pp. 1-5). Piscataway: IEEE

  164. Yu X, Ye C, Xiang L (2016) Application of artificial neural network in the diagnostic system of osteoporosis. Neurocomputing 214:376–381

    Google Scholar 

  165. Singh A, Dutta MK, Jennane R, Lespessailles E (2017) Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput Biol Med 91:148–158

    PubMed  Google Scholar 

  166. Mohamed EI, Meshref RA, Abdel-Mageed SM, Moustafa MH, Badawi MI, Darwish SH (2019) A novel morphological analysis of DXA-DICOM images by artificial neural networks for estimating bone mineral density in health and disease. J Clin Densitom 22(3):382–390

  167. Areeckal AS, Jayasheelan N, Kamath J, Zawadynski S, Kocher M (2018) Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population. Osteoporos Int 29(3):665–673

    CAS  PubMed  Google Scholar 

  168. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    PubMed  Google Scholar 

  169. Hatano K, Murakami S, Lu H, Tan JK, Kim H, Aoki T, (2017) Classification of osteoporosis from phalanges CR images based on DCNN. In Control, automation and systems (ICCAS), 2017 17th International Conference on (pp. 1593-1596). Piscataway: IEEE

  170. Tomita N, Cheung YY, Hassanpour S (2018) Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 98:8–15

    PubMed  Google Scholar 

  171. Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ (2019) Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Rad 48(1):20170344

    Google Scholar 

  172. Deniz CM, Xiang S, Hallyburton RS, Welbeck A, Babb JS, Honig S, Cho K, Chang G (2018) Segmentation of the proximal femur from MR images using deep convolutional neural networks. Sci Rep 8(1):16485

    PubMed  PubMed Central  Google Scholar 

  173. Sugita H, Oka M, Toguchida J, Nakamura T, Ueo T, Hayami T (1999) Anisotropy of osteoporotic cancellous bone. Bone 24(5):513–516

    CAS  PubMed  Google Scholar 

  174. Chappard C, Brunet-Imbault B, Lemineur G, Giraudeau B, Basillais A, Harba R, Benhamou CL (2005) Anisotropy changes in post-menopausal osteoporosis: characterization by a new index applied to trabecular bone radiographic images. Osteoporos Int 16(10):1193–1202

    PubMed  Google Scholar 

  175. Brunet-Imbault B, Lemineur G, Chappard C, Harba R, Benhamou CL (2005) A new anisotropy index on trabecular bone radiographic images using the fast Fourier transform. BMC Med Imaging 5(1):4

    PubMed  PubMed Central  Google Scholar 

  176. Sapthagirivasan V, Anburajan M, Mahadevan V (2013) Bone trabecular analysis of femur radiographs for the assessment of osteoporosis using DWT and DXA. Int J Comput Theory Eng 5(4):616

    Google Scholar 

  177. Gaidel A, Khramov A (2015) Application of texture analysis for automated osteoporosis diagnostics by plain hip radiography. Pattern Recog Image Anal 25(2):301–305

    Google Scholar 

  178. Barnett E, Nordin BEC (1960) The radiological diagnosis of osteoporosis: a new approach. Clin Radiol 11(3):166–174

    CAS  PubMed  Google Scholar 

  179. Thodberg HH, Van Rijn RR, Tanaka T, Martin DD, Kreiborg S (2010) A paediatric bone index derived by automated radiogrammetry. Osteoporos Int 21(8):1391–1400

    CAS  PubMed  Google Scholar 

  180. Lahari MS, Anburajan M (2011) Finite element analysis of femur in the evaluation of Osteoporosis. In: 2011 3rd International Conference on Electronics Computer Technology, vol 3. IEEE, Piscataway, pp 415–419

    Google Scholar 

  181. Imai K (2015) Computed tomography-based finite element analysis to assess fracture risk and osteoporosis treatment. World J Exp Med 5(3):182

    PubMed  PubMed Central  Google Scholar 

  182. Ramteke RJ, Monali YK (2012) Automatic medical image classification and abnormality detection using k-nearest neighbour. Int J Adv Comput Res 2(4):190–196

    Google Scholar 

  183. Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Trans Inf Technol Biomed 15(4):640–646

    PubMed  Google Scholar 

  184. Sapthagirivasan V, Anburajan M (2013) Diagnosis of osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: an investigation panorama with DXA. Comput Biol Med 43(11):1910–1919

    CAS  PubMed  Google Scholar 

  185. Kavitha MS, Asano A, Taguchi A, Heo MS (2013) The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis. Imaging Sci Dent 43(3):153–161

    PubMed  PubMed Central  Google Scholar 

  186. Wang Y, Yao J, Burns JE, Summers R (2016) Osteoporotic and neoplastic compression fracture classification on longitudinal CT. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, Piscataway, pp 1181–1184

    Google Scholar 

  187. Kavitha MS, Asano A, Taguchi A, Kurita T, Sanada M (2012) Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system. BMC Med Imaging 12(1):1

    CAS  PubMed  PubMed Central  Google Scholar 

  188. Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 8(8):630–632

    Google Scholar 

  189. Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):85

    PubMed  Google Scholar 

  190. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115

    CAS  PubMed  PubMed Central  Google Scholar 

  191. Brahim A, Jennane R, Riad R, Janvier T, Khedher L, Toumi H, Lespessailles E (2019) A decision support tool for early detection of knee osteoarthritis using x-ray imaging and machine learning: data from the osteoarthritis initiative. Comput Med Imaging Graph 73:11–18

    PubMed  Google Scholar 

  192. Haque S, Lau A, Beattie K, Adachi JD (2018) Novel imaging modalities in Osteoporosis diagnosis and risk stratification. Curr Treat Opt Rheumatol 4(2):133–141

    Google Scholar 

  193. Tu KN, Lie JD, Wan CKV, Cameron M, Austel AG, Nguyen JK, Van K, Hyun D (2018) Osteoporosis: a review of treatment options. Pharm Ther 43(2):92

    Google Scholar 

  194. Kanis JA (2019) Diagnosis and clinical aspects of osteoporosis. In: Pocket reference to osteoporosis. Springer, Cham, pp 11–20

    Google Scholar 

  195. Marques A, Ferreira RJ, Santos E, Loza E, Carmona L, da Silva JAP (2015) The accuracy of osteoporotic fracture risk prediction tools: a systematic review and meta-analysis. Ann Rheum Dis 74(11):1958–1967

    PubMed  Google Scholar 

  196. Rocha VM, Gaspar HA, Oliveira CFD (2018) Fracture risk assessment in home care patients using the FRAX® tool. Einstein (São Paulo) 16(3):eAO4236

  197. Yu W, Ying Q, Guan W, Lin Q, Zhang Z, Chen J, Engelke K, Hsieh E (2019) Impact of reference point selection on DXA-based measurement of forearm bone mineral density. Arch Osteoporos 14(1):107

    PubMed  Google Scholar 

  198. The influence of osteoporotic bone structures of the pelvic-hip complex on stress distribution under impact load

  199. Trabecular bone score and hip structural analysis in patients with atypical femur fractures

  200. Complex variation of trabecular bone structure in the proximal humerus and femur of five modern human populations

  201. Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA

  202. Comparison of non-invasive assessments of strength of the proximal femur

  203. Beck T (2003) Measuring the structural strength of bones with dual-energy X-ray absorptiometry: principles, technical limitations, and future possibilities. Osteoporos Int 14(5):81–88

    Google Scholar 

  204. Derkatch S, Kirby C, Kimelman D, Jozani MJ, Davidson JM, Leslie WD (2019) Identification of vertebral fractures by convolutional neural networks to predict nonvertebral and hip fractures: a registry-based cohort study of dual X-ray absorptiometry. Radiology 293(2):405–411

    PubMed  Google Scholar 

  205. Hussain D, Han SM (2019) Computer-aided osteoporosis detection from DXA imaging. Comput Methods Prog Biomed 173:87–107

    Google Scholar 

  206. Didier H, Barthe N, Boutroy S, Pothuaud L, Winzenrieth R, Krieg M (2011) Correlations between trabecular bone score, measured using anteroposterior dual energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J Clin Densitom 14(3):302–312

    Google Scholar 

  207. Winzenrieth R, Piveteau T, Hans D (2011) Assessment of correlations between 3D μCT microarchitecture parameters and TBS: effects of resolution and correlation with TBS DXA measurements. J Clin Densitom 14(2):169

    Google Scholar 

  208. Tang C, Zhang W, Li H, Li L, Li Z, Cai A, Wang L, Shi D, Yan B 2019 CNN-based automatic detection of bone conditions via diagnostic CT images for osteoporosis screening. arXiv preprint arXiv:1910.06777

  209. Li YL, Wong KH, Law MWM, Fang BXH, Lau VWH, Vardhanabuti VV, Lee VKH, Cheng AKC, Ho WY, Lam WWM (2018) Opportunistic screening for osteoporosis in abdominal computed tomography for Chinese population. Arch Osteoporos 13(1):76

    PubMed  Google Scholar 

  210. Khojastepour L, Hasani M, Ghasemi M, Mehdizadeh AR, Tajeripour F (2019) Mandibular trabecular bone analysis using local binary pattern for osteoporosis diagnosis. J Biomed Phys Eng 9(1):81

    CAS  PubMed  PubMed Central  Google Scholar 

  211. Omiotek Z, Dzierżak R, Uhlig S (2019) Fractal analysis of the computed tomography images of vertebrae on the thoraco-lumbar region in diagnosing osteoporotic bone damage. Proc Inst Mech Eng H J Eng Med 233(12):1269–1281

    Google Scholar 

  212. Krishna TG, Sunitha KVN, Mishra S (2018) Detection and classification of brain tumor from MRI medical image using wavelet transform and PSO based LLRBFNN algorithm. Int J Comput Sci Eng 6(1)

  213. Sharif M, Amin J, Raza M, Yasmin M, Satapathy SC (2020) An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recogn Lett 129:150–157

  214. Babu KS, Sabut S, Nithya DK, (2018) Efficient detection and classification of diabetic foot ulcer tissue using PSO technique

  215. Wang Y, Meng X, Zhu L (2018) Cell group recognition method based on adaptive mutation PSO-SVM. Cells 7(9):135

    CAS  PubMed Central  Google Scholar 

  216. Gopika GS, Shanthini J, Karthik S (2018) Hybrid approach for the brain tumors detection & segmentation using artificial bee colony optimization with FCM. In: 2018 International Conference on Soft-computing and Network Security (ICSNS). IEEE, Piscataway, pp 1–5

    Google Scholar 

  217. Chen CJ (2017) Image segmentation for lung lesions using ant colony optimization classifier in chest CT. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Springer, Cham, pp 283–289

    Google Scholar 

  218. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570–584

    PubMed  PubMed Central  Google Scholar 

  219. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV (2018) An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9252–9260

    Google Scholar 

  220. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30(4):449–459

    PubMed  PubMed Central  Google Scholar 

  221. Ng HP, Ong SH, Foong KWC, Goh PS, Nowinski WL (2006) Medical image segmentation using k-means clustering and improved watershed algorithm. In: 2006 IEEE Southwest Symposium on Image Analysis and Interpretation. IEEE, Piscataway, pp 61–65

    Google Scholar 

  222. Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ (2018) Current applications and future impact of machine learning in radiology. Radiology 288(2):318–328

    PubMed  Google Scholar 

  223. Wang X, Peng Y, Lu L, Lu Z, Summers RM (2018) Tienet: text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9049–9058

    Google Scholar 

  224. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321–331

    Google Scholar 

  225. Madani A, Moradi M, Karargyris A, Syeda-Mahmood T (2018) Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, Piscataway, pp 1038–1042

    Google Scholar 

  226. Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T (2018) NiftyNet: a deep-learning platform for medical imaging. Comput Methods Prog Biomed 158:113–122

    Google Scholar 

  227. Ai H (2019) Medical tumor image classification algorithm and its application in breast cancer. Investig Clin 60(5)

  228. Zhang J, Xie Y, Wu Q, Xia Y (2019) Medical image classification using synergic deep learning. Med Image Anal 54:10–19

    PubMed  Google Scholar 

  229. Kuo TR, Chen CH (2017) Bone biomarker for the clinical assessment of osteoporosis: recent developments and future perspectives. Biomark Res 5(1):18

    PubMed  PubMed Central  Google Scholar 

  230. Wani IM, Arora S (2020) Deep neural networks for diagnosis of osteoporosis: a review. In: Proceedings of ICRIC 2019. Springer, Cham, pp 65–78

    Google Scholar 

  231. Kanis JA, Hans D, Cooper C, Baim S, Bilezikian JP, Binkley N, Cauley JA, Compston JE, Dawson-Hughes B, Fuleihan GEH, Johansson H (2011) Interpretation and use of FRAX in clinical practice. Osteoporos Int 22(9):2395

    CAS  PubMed  Google Scholar 

  232. Albertsson DM, Mellström D, Petersson C, Eggertsen R (2007) Validation of a 4-item score predicting hip fracture and mortality risk among elderly women. Ann Fam Med 5(1):48–56

    PubMed  PubMed Central  Google Scholar 

  233. Ettinger B, Hillier TA, Pressman A, Che M, Hanley DA (2005) Simple computer model for calculating and reporting 5-year osteoporotic fracture risk in postmenopausal women. J Women's Health 14(2):159–171

    Google Scholar 

  234. Lo JC, Pressman AR, Chandra M, Ettinger B (2011) Fracture risk tool validation in an integrated healthcare delivery system. Am J Manag Care 17(3):188–194

    PubMed  Google Scholar 

  235. Tanaka S, Yoshimura N, Kuroda T, Hosoi T, Saito M, Shiraki M (2010) The Fracture and Immobilization Score (FRISC) for risk assessment of osteoporotic fracture and immobilization in postmenopausal women―a joint analysis of the Nagano, Miyama, and Taiji cohorts. Bone 47(6):1064–1070

    PubMed  Google Scholar 

  236. Henry MJ, Pasco JA, Sanders KM, Nicholson GC, Kotowicz MA (2006) Fracture risk (FRISK) score: Geelong osteoporosis study. Radiology 241(1):190–196

    PubMed  Google Scholar 

  237. Kanis JA, Odén A, Johnell O, Johansson H, De Laet C, Brown J, Burckhardt P, Cooper C, Christiansen C, Cummings S, Eisman JA (2007) The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int 18(8):1033–1046

    CAS  PubMed  Google Scholar 

  238. Sambrook PN, Flahive J, Hooven FH, Boonen S, Chapurlat R, Lindsay R, Nguyen TV, Díez-Perez A, Pfeilschifter J, Greenspan SL, Hosmer D (2011) Predicting fractures in an international cohort using risk factor algorithms without BMD. J Bone Miner Res 26(11):2770–2777

    PubMed  Google Scholar 

  239. Hundrup YA, Jacobsen RK, Andreasen AH, Davidsen M, Obel EB, Abrahamsen B (2010) Validation of a 5-year risk score of hip fracture in postmenopausal women. The Danish Nurse Cohort Study. Osteoporos Int 21(12):2135–2142

    CAS  PubMed  Google Scholar 

  240. Sela EI, Pulungan R, Widyaningrum R, Shantiningsih RR (2019) Method for automated selection of the trabecular area in digital periapical radiographic images using morphological operations. Healthc Inform Res 25(3):193–200

    PubMed  PubMed Central  Google Scholar 

  241. Kawashima Y, Fujita A, Buch K, Li B, Qureshi MM, Chapman MN, Sakai O (2019) Using texture analysis of head CT images to differentiate osteoporosis from normal bone density. Eur J Radiol 116:212–218

    PubMed  Google Scholar 

  242. Yousfi L, Houam L, Boukrouche A, Lespessailles E, Ros F, Jennane R (2019) Texture analysis and genetic algorithms for osteoporosis diagnosis. International Journal of Pattern Recognition and Artificial Intelligence, p 2057002

  243. https://www.iofbonehealth.org/sites/default/files/media/PDFs/Regional%20Audits/2012-Latin_America_Audit_0_0.pdf

  244. Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J. and Greenspan, H., 2018. Synthetic data augmentation using GAN for improved liver lesion classification. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 289-293). IEEE

  245. Liu X, Deng Z, Yang Y (2019) Recent progress in semantic image segmentation. Artif Intell Rev 52(2):1089–1106

    Google Scholar 

  246. Vasilakos AV, Tang Y, Yao Y (2016) Neural networks for computer-aided diagnosis in medicine: a review. Neurocomputing 216:700–708

    Google Scholar 

  247. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Medical image analysis, 42, pp.60-88.254 Nayak, S., Roberts, M.S. and Greenspan, S.L., 2011. Cost-effectiveness of different screening strategies for osteoporosis in postmenopausal women. Ann Intern Med 155(11):751–761

    Google Scholar 

  248. Wani IM, Arora S (2020) A review on using dental images as a screening tool for osteoporosis. In: Proceeding of International Conference on Computational Science and Applications. Springer, Singapore, pp 215–225

    Google Scholar 

  249. Shankar N, Babu SS, Viswanathan C (2019) Bone trabecular analysis of proximal femur radiographs for the detection of osteoporosis using anisotropic Morlet wavelet transform. Clust Comput 22(6):14513–14523

    Google Scholar 

  250. Terzini M, Aldieri A, Rinaudo L, Osella G, Audenino AL, Bignardi C (2019) DXA-derived finite element models to enhance the hip fracture risk prediction. In: Annual Meeting of the Italian Chapter of the European Society of Biomechanics (ESB-ITA) tenutosi a Bologna (IT) nel

    Google Scholar 

  251. Yeshua T, Rebibo S, Jacobson K, Safran O, Liebergall M, Leichter I (2019) An informative machine-learning tool for diagnosis of osteoporosis using routine femoral neck radiographs. In: InSITE 2019: Informing Science+ IT Education Conferences: Jerusalem, pp 233–237

    Google Scholar 

  252. Dagan N, Elnekave E, Barda N, Bregman-Amitai O, Bar A, Orlovsky M, Bachmat E, Balicer RD (2020) Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med 26(1):77–82

    CAS  PubMed  Google Scholar 

  253. Krishnaraj A, Barrett S, Bregman-Amitai O, Cohen-Sfady M, Bar A, Chettrit D, Orlovsky M, Elnekave E (2019) Simulating dual-energy X-ray absorptiometry in CT using deep-learning segmentation cascade. J Am Coll Radiol 16(10):1473–1479

    PubMed  Google Scholar 

  254. Liu J, Wang J, Ruan W, Lin C, Chen D (2020) Diagnostic and gradation model of osteoporosis based on improved deep U-net network. J Med Syst 44(1):15

    Google Scholar 

  255. Thakor NV, Eisenman LN (1989) Three-dimensional computer model of the heart: fibrillation induced by extrastimulation. Comput Biomed Res 22(6):532–545

    CAS  PubMed  Google Scholar 

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    Insha Majeed Wani & Sakshi Arora

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Wani, I.M., Arora, S. Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey. Med Biol Eng Comput 58, 1873–1917 (2020). https://doi.org/10.1007/s11517-020-02171-3

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