Abstract
Locating the hip joint center (HJC) from X-ray images is frequently required for the evaluation of hip dysplasia. Existing state-of-the-art methods focus on developing functional methods or regression equations with some radiographic landmarks. Such developments employ shallow networks or single equations to locate the HJC, and little attention has been given to deep stacked networks. In addition, existing methods ignore the connections between static and dynamic landmarks, and their prediction capacity is limited. This paper proposes an innovative hybrid framework for HJC identification. The proposed method is based on fast deep stacked network (FDSN) and dynamic registration graph with four improvements: (1) an anatomical landmark extraction module obtains comprehensive prominent bony landmarks from multipose X-ray images; (2) an attribute optimization module based on grey relational analysis (GRA) guides the network to focus on useful external anatomical landmarks; (3) a multiverse optimizer (MVO) module appended to the framework automatically and efficiently determines the optimal model parameters; and (4) the dynamic fitting and two-step registration approach are integrated into the model to further improve the accuracy of HJC localization. By integrating the above improvements in series, the models’ performances are gradually enhanced. Experimental results show that our model achieves superior results to existing HJC prediction approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harrington, M.E., Zavatsky, A.B., Lawson, S.E.M., Yuan, Z., Theologis, T.N.: Prediction of the hip joint centre in adults, children, and patients with cerebral palsy based on magnetic resonance imaging. J. Biomech. 40(3), 595–602 (2007)
Mose, K.: Methods of measuring in Legg-Calvé-Perthes disease with special regard to the prognosis. Clin. Orthop. Relat. Res. 150, 103–109 (1980)
Cuomo, A.V., Moseley, C.F., Fedorak, G.T.: A practical approach to determining the center of the femoral head in subluxated and dislocated hips. J. Pediatr. Orthop. 35(6), 556–560 (2015)
Piazza, S.J., Erdemir, A., Okita, N., Cavanagh, P.R.: Assessment of the functional method of hip joint center location subject to reduced range of hip motion. J. Biomech. 37(3), 349–356 (2004)
Camomilla, V., Cereatti, A., Vannozzi, G., Cappozzo, A.: An optimized protocol for hip joint centre determination using the functional method. J. Biomech. 39(6), 1096–1106 (2006)
Silaghi, M.-C., Plänkers, R., Boulic, R., Fua, P., Thalmann, D.: Local and global skeleton fitting techniques for optical motion capture. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) CAPTECH 1998. LNCS (LNAI), vol. 1537, pp. 26–40. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-49384-0_3
Gamage, S.S.H.U., Lasenby, J.: New least squares solutions for estimating the average centre of rotation and the axis of rotation. J. Biomech. 35(1), 87–93 (2002)
Halvorsen, K., Lesser, M., Lundberg, A.: A new method for estimating the axis of rotation and the center of rotation. J. Biomech. 32(11), 1221–1227 (1999)
Assi, A., et al.: Validation of hip joint center localization methods during gait analysis using 3D EOS imaging in typically developing and cerebral palsy children. Gait Posture 48, 30–35 (2016)
Sangeux, M., Pillet, H., Skalli, W.: Which method of hip joint centre localisation should be used in gait analysis? Gait Posture 40(1), 20–25 (2014)
Peters, A., Baker, R., Morris, M.E., Sangeux, M.: A comparison of hip joint centre localisation techniques with 3-DUS for clinical gait analysis in children with cerebral palsy. Gait Posture 36(2), 282–286 (2012)
Miller, E.J., Kaufman, K.R.: Verification of an improved hip joint center prediction method. Gait Posture 59, 174–176 (2018)
Sangeux, M.: On the implementation of predictive methods to locate the hip joint centres. Gait Posture 42(3), 402–405 (2015)
Sangeux, M., Peters, A., Baker, R.: Hip joint centre localization: evaluation on normal subjects in the context of gait analysis. Gait Posture 34(3), 324–328 (2011)
Bombaci, H., Simsek, B., Soyarslan, M., Murat Yildirim, M.: Determination of the hip rotation centre from landmarks in pelvic radiograph. Acta Orthop. Traumatol. Turc. 51(6), 470–473 (2017)
Wang, X., et al.: Obstructive sleep apnea detection using ecg-sensor with convolutional neural networks. Multimedia Tools Appl. 79(23), 15813–15827 (2020)
Shi, W., Liu, S., Jiang, F., Zhao, D., Tian, Z.: Anchored neighborhood deep network for single-image super-resolution. EURASIP J. Image Video Process. 2018(1), 34 (2018)
Jiang, F., et al.: Medical image semantic segmentation based on deep learning. Neural Comput. Appl. 29(5), 1257–1265 (2018)
Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2015). https://doi.org/10.1007/s00521-015-1870-7
Sun, G., Xin, G., Xiao, Y., Zheng, Z.: Grey relational analysis between hesitant fuzzy sets with applications to pattern recognition. Exp. Syst. Appl. 92(9), 521–532 (2018)
Huang, G., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybernet. Part B (Cybernet.) 42(2), 513–529 (2012)
da Silva, B.L.S., Inaba, F.K., Salles, E.O.T., Ciarelli, P.M.: Fast deep stacked networks based on extreme learning machine applied to regression problems. Neural Netw. 131, 14–28 (2020)
Goshtasby, A.: Image registration by local approximation methods. Image Vis. Comput. 6(4), 255–261 (1988)
Yuan-Chu, C., Wei-Min, Q., Wei-You, C.: Dynamic properties of Elman and modified Elman neural network. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 637–640 (2002)
Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Comput. 3(2), 246–257 (1991)
Deng, W., Zheng, Q., Chen, L.: Regularized extreme learning machine. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining, pp. 389–395 (2009)
Zhou, H., Huang, G., Lin, Z., Wang, H., Soh, Y.C.: Stacked extreme learning machines. IEEE Trans. Cybernet. 45(9), 2013–2025 (2015)
Li, D.: A tutorial survey of architectures, algorithms, and applications for deep learning. Apsipa Trans. Signal Inf. Process. 3, e2 (2014)
Fujii, M., Nakamura, T., Hara, T., Nakashima, Y.: Is Ranawat triangle method accurate in estimating hip joint center in Japanese population? J. Orthop. Sci. 26(2), 219–224 (2021)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No.61772556), National Key R&D Program of China (No.2018YFB1107100, No.2016 YFC1100600), Postgraduate Research and Innovation Project of Hunan (No.CX20200321) and Fundamental Research Funds for the Central Universities of Central South University (2020zzts140).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, F., Liao, S., Wu, R., Liu, S., Zhao, Y., Shen, X. (2021). Radiological Identification of Hip Joint Centers from X-ray Images Using Fast Deep Stacked Network and Dynamic Registration Graph. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-86365-4_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86364-7
Online ISBN: 978-3-030-86365-4
eBook Packages: Computer ScienceComputer Science (R0)