Abstract
Prediction of trabecular fracture zone is a very important element for assessing the fracture risk in patients. The assumption that failure always occurs in local bands, the so called ‘fracture zones’, with the remaining regions of the structure largely unaffected has been visually verified. Researchers agreed that the identification of the weakest link in the trabecular framework can lead to the prediction of the fracture zone and consequently of the failure event. In this paper, a decision support system (DSS) is proposed for the automatic identification of fracture zone. Initially, an automatic methodological approach based on image processing is applied for the automatic identification of trabecular bone fracture zone in micro-CT datasets, after mechanical testing. Then, a local analysis of the whole specimen is performed on order to compare the structure (Volumes of Interest -VOI) of the broken region to the unbroken one. As a result, for every VOI, 29 morphometrical parameters were computed and used as initial features to the proposed DSS. The DSS comprises of two main modules: the feature selection module and the classifier. The feature selection module is used for reducing the initial size of the input features’ subset (29 features) and for keeping the most informative features in order to increase the classification’s module performance. To this end, the Sequential Floating Forward Selection (SFFS) algorithm with Fuzzy C-Means evaluation criterion was implemented. For the classification, several classification algorithms including the Multilayer Perceptron (MLP), the Support Vector Machines (SVM), the Naïve Bayesian (NB), the k-Nearest Neighbor (KNN) and the k-Means (KM) have been used. Comparing the performance of these classification algorithms, the SFFS-SVM scheme provided the best performance scoring 98% in terms of overall classification accuracy.
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Tassani, S., Ohman, C., Baruffaldi, F., Baleani, M., Viceconti, M.: Volume to density relation in adult human bone tissue. J. Biomech. 44, 103–108 (2011)
Perilli, E., Baleani, M., Ohman, C., Fognani, R., Baruffaldi, F., Viceconti, M.: Dependence of mechanical compressive strength on local variations in microarchitecture in cancellous bone of proximal human femur. J. Biomech. 41, 438–446 (2008)
Tassani, S., Ohman, C., Baleani, M., Baruffaldi, F., Viceconti, M.: Anisotropy and inhomogeneity of the trabecular structure can describe the mechanical strength of osteoarthritic cancellous bone. J. Biomech. 43, 1160–1166 (2010)
Tassani, S., Asvestas, P.A., Matsopoulos, G.K., Baruffaldi, F.: Automatic Identification of Trabecular Bone Fracture. In: Bamidis, P.D., Pallikarakis, N. (eds.) MEDICON 2010. IFMBE Proceedings, vol. 29, pp. 296–299. Springer, Heidelberg (2010)
Tassani, S., Demenegas, F., Matsopoulos, G.K.: Local analysis of trabecular bone fracture. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 7454–7457 (2011)
Leroy, G., Chen, H.: Introduction to the special issue on decision support in medicine. Decision Support Systems 43, 1203–1206 (2007)
Palaniswami, M.: Computational Intelligence in Gait Research: A Perspective on Current Applications and Future Challenges. IEEE Transactions on Information Technology in Biomedicine 13(5), 687–702 (2009)
Mashor, M.Y., Jaafar, H.: Online sequential extreme learning machine for classification of mycobacterium tuberculosis in ziehl-neelsen stained tissue. In: 2012 International Conference on Biomedical Engineering, February 27-28, pp. 139–143 (2012)
Pena, E., Martínez, M.A.: Machine Learning Techniques as a Helpful Tool Toward Determination of Plaque Vulnerability. IEEE Transactions on Information Technology in Biomedicine 59(4), 1155–1161 (2012)
Madokoro, H., Otani, T., Kadowaki, S.: Experimental studies with a hybrid model of unsupervised neural networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), July 31-August 5, pp. 1659–1666 (2011)
Phlypo, R., Congedo, M.: SVM feature selection for multidimensional EEG data. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 22-27, pp. 781–784 (2011)
Christopher, J.J., Ramakrishnan, S.: Assessment and classification of mechanical strength components of human femur trabecular bone using texture analysis and neural network. J. Med. Syst. 32, 117–122 (2008)
Ohman, C., Baleani, M., Perilli, E., Dall’Ara, E., Tassani, S., Baruffaldi, F., Viceconti, M.: Mechanical testing of cancellous bone from the femoral head: experimental errors due to off-axis measurements. J. Biomech. 40, 2426–2433 (2007)
Matsopoulos, G.K., Delibasis, K.K., Mouravliansky, N.A., Asvestas, P.A., Nikita, K.S., Kouloulias, V.E., Uzunoglu, N.K.: CT-MRI automatic surface-based registration schemes combining global and local optimization techniques. Technol. Health Care 11, 219–232 (2003)
van den Elsen, P.A., Pol, E.J.D., Viergever, M.A.: Medical image matching-a review with classification. IEEE Engineering in Medicine and Biology Magazine 12, 26–39 (1993)
Tassani, S., Demenegas, F., Matsopoulos, G.K.: Morphometry of trabecular bone fracture: preliminary study. In: XXIIIrd Congress of the International Society of Biomechanics, July 3-7, p. 69. International Society of Biomechanics, Brussels (2011)
Turner, C.H., Cowin, S.C., Rho, J.Y., Ashman, R.B., Rice, J.C.: The fabric dependence of the orthotropic elastic constants of cancellous bone. J. Biomech. 23, 549–561 (1990)
Hildebrand, T., Ruegsegger, P.: A new method for the model-independent assessment of thickness in three-dimensional images. Journal of Microscopy 185, 67 (1997)
Hildebrand, T., Ruegsegger, P.: Quantification of Bone Microarchitecture with the Structure Model Index. Comput. Methods Biomech. Biomed. Engin. 1, 15–23 (1997)
Odgaard, A., Gundersen, H.J.: Quantification of connectivity in cancellous bone, with special emphasis on 3-D reconstructions. Bone 14, 173–182 (1993)
Cowin, S.C.: The relationship between the elasticity tensor and the fabric tensor. Mechanics of Materials 4, 137 (1985)
Odgaard, A.: Three-dimensional methods for quantification of cancellous bone architecture. Bone 20, 315–328 (1997)
Harrigan, T.P., Mann, R.W.: Characterization of microstructural anisotropy in orthotropic materials using a second rank tensor. Journal of Materials Science 19, 761 (1984)
Whitehouse, W.J.: The quantitative morphology of anisotropic trabecular bone. J. Microsc. 101, 153–168 (1974)
Tassani, S., Particelli, F., Perilli, E., Traina, F., Baruffaldi, F., Viceconti, M.: Dependence of trabecular structure on bone quantity: a comparison between osteoarthritic and non-pathological bone. Clin. Biomech. (Bristol, Avon) 26, 632–639 (2011)
Goulet, R.W., Goldstein, S.A., Ciarelli, M.J., Kuhn, J.L., Brown, M.B., Feldkamp, L.A.: The relationship between the structural and orthogonal compressive properties of trabecular bone. J. Biomech. 27, 375–389 (1994)
Majumdar, S., Kothari, M., Augat, P., Newitt, D.C., Link, T.M., Lin, J.C., Lang, T., Lu, Y., Genant, H.K.: High-resolution magnetic resonance imaging: three-dimensional trabecular bone architecture and biomechanical properties. Bone 22, 445–454 (1998)
Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)
Nock, R., Nielsen, F.: On Weighting Clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(8), 1–13 (2006)
Rosenblatt, F.: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington, DC (1961)
Cortes, C., Vapnik, V.N.: Support-Vector Networks. Machine Learning 20 (1995)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier Inc. (2009)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137 (1982)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, London (1982)
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Korfiatis, V., Tassani, S., Matsopoulos, G.K. (2012). A Decision Support System for the Prediction of the Trabecular Fracture Zone. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_15
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