Bone Thickness Computation at Low Resolution IN-VIVO CT Images and Classification through ANN

Jayachitra*, M. Usha**
* PG Scholar, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.
** Assistant Professor, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.
Periodicity:January - March'2015
DOI : https://doi.org/10.26634/jip.2.1.3263

Abstract

Osteoporosis is a bone disease affecting the bone structure and strength and raising the risk of fractures. Osteoporosis is a bone condition that makes bones thinner and more fragile because of reduced bone density. Osteoporosis may be diagnosed directly through the use of a bone scan that measures bone mineral density (BMD). The micro-architectural quality of Trabecular bone is an important factor of bone quality for evaluating fracture risks under clinical conditions. A new algorithm is implemented for computing TB thickness at a low resolution which is achievable in IN-VIVO images. Here the authors have proposed intercept based algorithm to compute its thickness, and robustness to bone strength. Through this algorithm, the true axis of an object orthogonally intersects a minimum intercept line. By calculating centroids of an image, the thickness is calculated. Detected Image can be classified through artificial neural network classifier.

Keywords

Intercept Based Algorithm, Computed Tomography (CT), Trabecular Bone (TB) Thickness.

How to Cite this Article?

Jayachitra, S., and Usha, M. (2015). Bone Thickness Computation at Low Resolution IN-VIVO CT Images and Classification through ANN. i-manager’s Journal on Image Processing, 2(1), 14-18. https://doi.org/10.26634/jip.2.1.3263

References

[1]. S. Boonen and A. J. Singer, (2008). “Osteoporosis management: Impact of fracture type on cost and quality of life in patients at risk for fracture I,” Curr. Med. Res. Opin., Vol. 24, No. 6, pp. 1781–1788.
[2]. L. J. Melton, (1997). “Epidemiology of spinal osteoporosis,” Spine, Vol. 22, pp. 2S–11S.
[3]. M. Benito, B. Gomberg, F. W. Wehrli, R. H. Weening, B. Zemel, A. C. Wright, H. K. Song, A. Cucchiara, and P. J. Snyder, (2003). “Deterioration of trabecular architecture in hypogonadal men,” J. Clin. Endocrinol. Metab, Vol. 88, No. 4, pp. 1497–1502.
[4]. A. M. Parfitt, C. H. E. Mathews, A. R. Villanueva, M. Kleerekoper, B. Frame, and D. S. Rao, (1993). “Relationships between surface, volume, and thickness of iliac trabecular bone in aging and in osteoporosis. Implications for the microanatomic and cellular mechanisms of bone loss,” J. Clin. Invest., Vol. 72, No. 4, pp. 1396–1409.
[5]. P. Chavassieux, M. Arlot, and P. J. Meunier, (2001). “Clinical use of bone biopsy,” Osteoporosis, Vol. 2, pp. 501–509.
[6]. T. Hildebrand and P. R¨uegsegger, (1997). “A new method for the model independent assessment of thickness in three-dimensional images,” J. Microscopy, Vol. 185, No. 1, pp. 67–75.
[7]. P. K. Saha and F. W. Wehrli, (2004). “Measurement of trabecular bone thickness in the limited resolution regime of in vivoMRI by fuzzy distance transform,” IEEE Trans. Med. Imag., Vol. 23, No. 1, pp. 53–62.
[8]. A.Vesterby, H. J. Gundersen, and F. Melsen, (1989). “Star volume of marrow space and trabeculae of the first lumbar vertebra: Sampling efficiency and biological variation,” Bone, Vol. 10, No. 1, pp. 7–13.
[9]. P. I. Croucher, N. J. Garrahan, and J. E. Compston, (1996). “Assessment of cancellous bone structure: comparison of strut analysis, trabecular bone pattern factor, and marrow space star volume,” J. Bone Miner. Res., Vol. 11, No. 7, pp. 955–961.
[10]. R. Moreno,M. Borga, and O . Smedby, (2012). “Estimation of trabecular thickness in gray-scale images through granulometric analysis,” Proc. SPIE, Vol. 8314, pp. 831451-8314519.
[11]. P. K. Saha, F. W. Wehrli, and B. R. Gomberg, (2002). “Fuzzy distance transform: Theory, algorithms, and applications,” Comput. Vis. Imag. Understanding, Vol. 86, No. 3, pp. 171–190.
[12]. H, Breu, J. Gil, D. Kirkpatrick, and M. Werman, (1995). “Linear time Euclidean distance transform algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 17, No. 5, pp. 529–533.
[13]. C. J. Hernandez, G. S. Beaupre, T. S. Keller, and D. R. Carter, (2001). “The influence of bone volume fraction and ash fraction on bone strength and modulus,” Bone, Vol. 29, No. 1, pp. 74–78.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.