Assessment and Classification of Mechanical Strength Components of Human Femur Trabecular Bone Using Texture Analysis and Neural Network (original) (raw)

References

  1. Chinander, M. R., Giger, M. L., Martell, J. M., Jiang, C., and Favus, M. J., Computerized radiographic texture measures for characterizing bone strength: A simulated clinical setup using femoral neck specimens. Med. Phys. 26:2295–2300, 1999
    Article Google Scholar
  2. Erben, R. G., Trabecular and endocortical bone surfaces in the rat: Modeling or remodeling?. Anat. Rec. 246:39–46, 1996
    Article Google Scholar
  3. Harrigan, R. P., and Mann, R. W., Characterization of microstructural anisotropy in orthotropic materials using a second rank tensor. J. Mater. Sci. 19:761–767, 1984
    Article Google Scholar
  4. Tanaka, T., Sakurai T. and Kashima I., Structuring of parameters for assessing vertebral bone strength by star volume analysis using a morphological filter. J. Bone Miner. Metab. 19:150–158, 2001
    Article Google Scholar
  5. Weinstein, R. S., and Majumdar, S., Fractal geometry and vertebral compression fractures. J. Bone Miner. Metab. 9:1797–1802, 1994
    Google Scholar
  6. Ouyang, X., Majumdar, S., Link, T. M., Augat, P., Lu, Y., and Lin, J. C., Radiographic assessment of trabecular structure: Correlation with biomedical strength and comparison with BMD. Orthopaedic Research Society 13:208–235, 1997
    Google Scholar
  7. Link, T. M., Majumdar, S., Konermann, W., Meier, N., Lin, J. C., Newitt, D., Ouyang X., Peters, P. E., and Genant, H. K., Texture analysis of direct magnification radiographs of vertebral specimens: Correlation with bone mineral density and biomechanical properties. Acad. Radiol. 4:167–176, 1997
    Article Google Scholar
  8. Caligiuri, P., Giger, M. L., and Favus, M. J., Multifractal radiographic analysis of osteoporosis. Med. Phys. 21:503–508, 1994
    Article Google Scholar
  9. Cann, C., Genant, H., Kolb, F., and Ettinger, B., Quantitative computed tomography for the prediction of vertebral bone fracture risk. Bone 6:1–7, 1985
    Article Google Scholar
  10. Gordon, C. L., Webber, C. E., Christoforou, N., and Nahmias, C., In vivo assessment of trabecular bone structure at the distal radius from high-resolution magnetic resonance images. Med. Phys. 24:585–593, 1997
    Article Google Scholar
  11. Antich, P., Anderson, J., Ashman, R., Dowdey, J., Gonzales, J., Murray, R., Zewekh, J., and Pak, C., Measurement of mechanical properties of bone material in vitro by ultrasound reflection : Methodology and comparison with ultrasound transmission. J. Bone Miner. Metab. 6:417–426, 1991
    Google Scholar
  12. Mulder, L., Vanruijven, L. J., Koolstra, J. H., and Van Eijden, T. M. G. J., The influence of mineralization on intratrabecular stress and strain distribution in developing trabecular bone. Ann. Biomed. Eng. 35:1668–1677, 2007
    Article Google Scholar
  13. Benardos, P. G., and Vosniakos G.-C., Optimizing feed forward artificial neural network architecture. Artif. Intell. 20:365–382, 2007
    Google Scholar
  14. Dokur, Z., and Olmez, T., ECG beat classification by a novel hybrid neural network. Comput. Methods Programs Biomed. 66:167–181, 2001
    Article Google Scholar
  15. Gurney, J. W., and Swensen, S. J., Solitary pulmonary nodules: Determining the likelihood of malignancy with neural network analysis. Radiology, 196(3):823–829, 1995
    Google Scholar
  16. Perchiazzi, G., Hogman, M., Rylander, C., Giuliani, R., Fiore, T., and Hedenstierna G., Assessment of respiratory system mechanics by artificial neural networks: An exploratory study. J. Appl. Physiol. 90:1817–1824, 2001
    Article Google Scholar
  17. Mahesh, V., and Ramakrishnan S., Neural network based classification and analysis of human respiratory mechanics using spirometric measurements. Journal of Mechanics in Medicine and Biology 7:151–161, 2007
    Article Google Scholar
  18. Gregory, J. S., Junold, R. M., Undrill, P. E., and Aspden R. M., Analysis of trabecular bone structure using Fourier transforms and neural networks. IEEE Trans. Inf. Technol. Biomed. 3:289–294, 1999
    Article Google Scholar
  19. Jakubas-Przewlocka, J., Sawicki, A., and Przewlocki, P., Assessment of trabecular bone structure in postmenopausal and senile osteoporosis in women by image analysis. Scand. J. Rheumatol. 32:295–299, 2003
    Article Google Scholar
  20. Singh, M., Nagrath, A. R., and Maini, P. S., Changes in trabecular pattern of the upper end of the femur as an index of osteoporosis. J. Bone Jt. Surg. 52:457–467, 1970
    Google Scholar
  21. Lee, J., Blain, S., Casas, M., Kenny, J., Berall, G., and Chau, T., A radial basis classifier for the automatic detection of aspiration in children. Journal of Neuro Engineering and Rehabilitation, 3:14, 2006
    Article Google Scholar
  22. Mahesh, V., and Ramakrishnan, S., Assessment and classification of normal and restrictive respiratory conditions through pulmonary function test and neural network. J. Med. Eng. Technol. 31:300–304, 2007
    Article Google Scholar
  23. Mueller, G., and Russell, R. G. G., Osteoporosis: Pathogenesis and clinical intervention. Biochem. Soc. Trans. 31:1–5, 2003
    Article Google Scholar
  24. Ulrich, D., van Rietbergen, B., Laib, A., and Ruegsegger, P., The ability of three-dimensional structural indices to reflect mechanical aspects of trabecular bone. Bone, 25:55–60, 1999
    Article Google Scholar
  25. Newitt, D. C., Majumdar, S., van Rietbergen, B., von Ingersleben, G., Harris, S. T., Genant, H. K., Chesnut, C., Garnero, P., and MacDonald, B., In vivo assessment of architecture and micro-finite element analysis derived indices of mechanical properties of trabecular bone in the radius. Osteoporos. Int. 13:6–17, 2002
    Article Google Scholar
  26. Smyth, P. P., Adams, J. E., Whitehouse, R. W., and Taylor, C. J., Application of computer texture analysis to the Singh index. Br. J. Radiol. 70:242–247, 1997
    Google Scholar
  27. Borah, B., Gross, G. J., Dufresne, T. E., Smith, T. S., Cockman, M. D., Chmielewski, P. A., Lundy, M. W., Hartke, J. R., and Sod, E. W., Three-dimensional microimaging (MRmicrol and microCT), finite element modeling and rapid prototyping provide unique insights into bone architecture in osteoporosis. Anat. Rec. 265:101–110, 2001
    Article Google Scholar
  28. Lin, J. C., Grampp, S., Link, T., Kothari, M., Newitt, D. C., Felsenberg, D., and Majumdar, S., Fractal analysis of proximal femur radiographs: Correlation with biomechanical properties and bone mineral density. Osteoporos. Int. 9, 516–524, 1999.
    Google Scholar

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