Excellence in Research and Innovation for Humanity

International Science Index

Select areas to restrict search in scientific publication database:
Trabecular Bone Radiograph Characterization Using Fractal, Multifractal Analysis and SVM Classifier
Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.
Digital Article Identifier (DAI):


[1] https://www.nlm.nih.gov/medlineplus/osteoporosis.htm. Accessed on: 08/10/2017.
[2] http://www.passeportsante.net/fr/Maux/Problemes/Fiche.aspx?doc=osteoporose_pm. Accessed on: 15/09/2017.
[3] A. Consensus. Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Am J Med, 94(6):646–50, 1993.
[4] J. S. Bauer and T. M. Link. Advances in osteoporosis imaging. European journal of radiology, 71(3):440–449, 2009.
[5] E. Lespessailles, C. Chappard, N. Bonnet, and C. L. Benhamou. Imaging techniques for evaluating bone microarchitecture. Joint Bone Spine, 73(3):254–261, 2006.
[6] K. Harrar, L. Hamami, E. Lespessailles, and R. Jennane. Piecewise whittle estimator for trabecular bone radiograph characterization. Biomedical Signal Processing and Control, 8(6):657–666, 2013.
[7] Tafraouti, A., El Hassouni, M., Toumi, H., Lespessailles, E., & Jennane, R. (2014, November). Osteoporosis Diagnosis Using Fractal Analysis and Support Vector Machine. In Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on (pp. 73-77). IEEE.
[8] Benhamou C, Lespesailles E, Jacquet G, Harba R, Jennane R, Loussot T, et al. Fractal organization of trabecular bone images on calcaneus radiographs. J Bone Miner Res 1994;9:1909–18.
[9] Khosrovi P, Kahn A, Genant H, Majumdar S. Characterization of trabecular bone structure from radiographs using fractal analysis. Sixteenth Annual Meeting of the American Society for Bone and Mineral Research. Kansas City, Missouri; 1994: S156.
[10] D. L. Ruderman and W. Bialek. Statistics of natural images: Scaling in the woods. Physical review letters, 73(6):814– 817, 1994.
[11] D. L. Ruderman. Origins of scaling in natural images. Vision research, 37(23):3385–3398, 1997.
[12] J. L. Vehel. Introduction to the multifractal analysis of images, 1998.
[13] Chaudhuri, B. B., & Sarkar, N. (1995). Texture segmentation using fractal dimension. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 17(1), 72-77. http://dx.doi.org/10.1109/34.368149
[14] C. Tricot. A model for rough surfaces. Composites science and technology, 63(8):1089–1096, 2003.
[15] E. Lespessailles, C. Gadois, I. Kousignian, J. Neveu, P. Fardellone, S. Kolta, C. Roux, J. Do-Huu, and C. Benhamou. Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study. Osteoporosis international, 19(7):1019–1028, 2008.
[16] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 1st ed. Cambridge University Press, 2000.
[17] C.C. Chang, C.J. Lin, LIBSVM: A library for support vector machines. 2001. http://www.csie.ntu.edu.tw/∼cjlin/libsvm. Accessed on: 20/09/2017.
[18] S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, K. R. K. Murthy, Improvements to platt’s SMO algorithm for SVM classifier design, Neural Comput.13 (2001) 637–649.
[19] R. Jennane, J. Touvier, M. Bergounioux, and E. Lespessailles. A variational model for trabecular bone radiograph characterization. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, pages 1283–1286. IEEE, 2014.
Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007