Skip to main content

Advertisement

Springer Nature Link
Log in

Non-Linear Filtering Technique Used for Testing the Human Lumbar Spine FEA Model

  • Mobile & Wireless Health
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper, the objective is to generate a mesh model of a spine that simulates numerically the biomedical properties of two vertebrae (L4 and L5) of human spine and an inter vertebrae disc using Finite Element Analysis (FEA) technique. Here, different types of non-linear filters and different edge detection techniques are used to segment the edges and the results are compared. The result shows that median filter obtains improved segmented output results in terms of edge length density, average magnitude, final threshold, initial position, and fine-tuned image. The behaviour of spine FEA model is analysed in terms of various parameters like equivalent elastic strain, total deformation, maximum principal elastic strain, minimum principal elastic strain, shear elastic strain, normal elastic strain, and minimum and maximum principal stress, equivalent stress, shear stress and normal stress. These parameters are used to analyse the human spine model under different conditions and different angles using ANSYS simulation tool. Further, MATLAB is carried out to implement various filters and edge detectors on proposed spine model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ghosh, S., Raja'S, A., Chaudhary, V., & Dhillon, G. (2011). Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis. In Medical Imaging 2011: Computer-Aided Diagnosis (Vol. 7963, p. 796303). International Society for Optics and Photonics.

  2. Schoenfeld, A. J., Bono, C. M., McGuire, K. J., Warholic, N., and Harris, M. B., Computed tomography alone versus computed tomography and magnetic resonance imaging in the identification of occult injuries to the cervical spine: A meta-analysis. Journal of Trauma and Acute Care Surgery 68(1):109–114, 2010.

    Article  Google Scholar 

  3. Smyth, P. P., Taylor, C. J., and Adams, J. E., Automatic measurement of vertebral shape using active shape models. Image and Vision Computing 15(8):575–581, 1997.

    Article  Google Scholar 

  4. Parthasarathy, P., & Vivekanandan, S. (2018). A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Informatics in Medicine Unlocked.

  5. Mastmeyer, A., Engelke, K., Fuchs, C., and Kalender, W. A., A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Medical image analysis 10(4):560–577, 2006.

    Article  PubMed  Google Scholar 

  6. Michopoulou, S. K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., and Todd-Pokropek, A., Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE transactions on Biomedical Engineering 56(9):2225–2231, 2009.

    Article  PubMed  Google Scholar 

  7. Sundarasekar, R., Thanjaivadivel, M., Manogaran, G., Kumar, P. M., Varatharajan, R., Chilamkurti, N., and Hsu, C. H., Internet of things with maximal overlap discrete wavelet transform for remote health monitoring of abnormal ECG signals. Journal of medical systems 42(11):228, 2018.

    Article  PubMed  Google Scholar 

  8. Kumar, P. M., Lokesh, S., Varatharajan, R., Babu, G. C., and Parthasarathy, P., Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Future Generation Computer Systems 86:527–534, 2018.

    Article  Google Scholar 

  9. Kumar, P. M., Devi, U., Manogaran, G., Sundarasekar, R., Chilamkurti, N., and Varatharajan, R., Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Computer Networks 144:154–162, 2018.

    Article  Google Scholar 

  10. Vijayakumar, V., Priyan, M. K., Ushadevi, G., Varatharajan, R., Manogaran, G., & Tarare, P. V. (2018). E-Health Cloud Security Using Timing Enabled Proxy Re-Encryption. Mobile Networks and Applications, 1–12.

  11. Parthasarathy, P., and Vivekanandan, S., Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health information science and systems 6:1–6, 2018.

    Article  Google Scholar 

  12. Mathan, K., Kumar, P. M., Panchatcharam, P., Manogaran, G., & Varadharajan, R. (2018). A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Automation for Embedded Systems, 1–18.

  13. Priya, S., Varatharajan, R., Manogaran, G., Sundarasekar, R., & Kumar, P. M. (2018). Paillier homomorphic cryptosystem with poker shuffling transformation based water marking method for the secured transmission of digital medical images. Personal and Ubiquitous Computing, 1–11.

  14. Varatharajan, R., Preethi, A. P., Manogaran, G., Kumar, P. M., & Sundarasekar, R. (2018). Stealthy attack detection in multi-channel multi-radio wireless networks. Multimedia Tools and Applications, 1–24.

  15. Manogaran, G., Shakeel, P. M., Hassanein, A. S., Priyan, M. K., & Gokulnath, C. (2018). Machine-Learning Approach Based Gamma Distribution for Brian Abnormalities Detection and Data Sample Imbalance Analysis. IEEE Access.

  16. Biswas, S., and Hazra, R., Robust edge detection based on modified Moore-neighbor. Optik 168:931–943, 2018.

    Article  Google Scholar 

  17. Hua, Z., and Zhou, Y., Design of image cipher using block-based scrambling and image filtering. Information Sciences 396:97–113, 2017.

    Article  Google Scholar 

  18. Roberts, M. G., Cootes, T. F., Pacheco, E., Oh, T., and Adams, J. E., Segmentation of lumbar vertebrae using part-based graphs and active appearance models. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer, 2009, 1017–1024.

    Google Scholar 

  19. Parthasarathy, P., and Vivekanandan, S., Urate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: A comprehensive review. Health information science and systems 6(1):19, 2018.

    Article  PubMed  Google Scholar 

  20. Raja'S, A., Corso, J. J., and Chaudhary, V., Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model. IEEE transactions on medical imaging 30(1):1–10, 2011.

    Article  Google Scholar 

  21. Parthasarathy, P. (2018). Synthesis and UV detection characteristics of TiO2 thin film prepared through sol gel route. In IOP Conference Series: Materials Science and Engineering (Vol. 360, No. 1, p. 012056). IOP Publishing.

  22. Basha, A. A., Vivekanandan, S., and Parthasarathy, P., Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: A review. Health information science and systems 6(1):17, 2018.

    Article  PubMed  Google Scholar 

  23. Varadharajan, R., Priyan, M. K., Panchatcharam, P., Vivekanandan, S., and Gunasekaran, M., A new approach for prediction of lung carcinoma using back propogation neural network with decision tree classifiers. Journal of Ambient Intelligence and Humanized Computing:1–12, 2018.

  24. Corso, J. J., Raja’S, A., & Chaudhary, V. (2008). Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 202–210). Springer, Berlin, Heidelberg.

  25. Zhan, Y., Maneesh, D., Harder, M., and Zhou, X. S., Robust MR spine detection using hierarchical learning and local articulated model. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer, 2012, 141–148.

    Google Scholar 

  26. Parthasarathy, P., & Vivekanandan, S. (2018). A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. International Journal of Computers and Applications, 1–11.

  27. Parthasarathy, P., and Vivekanandan, S., A comprehensive review on thin film-based nano-biosensor for uric acid determination: Arthritis diagnosis. World Review of Science, Technology and Sustainable Development 14(1):52–71, 2018.

    Article  Google Scholar 

  28. Štern, D., Likar, B., Pernuš, F., and Vrtovec, T., Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine. Physics in Medicine & Biology 55(1):247, 2009.

    Article  Google Scholar 

  29. Huang, S. H., Chu, Y. H., Lai, S. H., and Novak, C. L., Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE transactions on medical imaging 28(10):1595–1605, 2009.

    Article  PubMed  Google Scholar 

  30. Wang, Z., Zhen, X., Tay, K., Osman, S., Romano, W., and Li, S., Regression segmentation for $ M^{3} $ spinal images. IEEE transactions on medical imaging 34(8):1640–1648, 2015.

    Article  PubMed  Google Scholar 

  31. Goel, V. K., and Nyman, E., Computational modeling and finite element analysis. Spine 41:S6–S7, 2016.

    Article  PubMed  Google Scholar 

  32. Lujan, A. E., Balter, J. M., and Ten Haken, R. K., A method for incorporating organ motion due to breathing into 3D dose calculations in the liver: Sensitivity to variations in motion. Medical physics 30(10):2643–2649, 2003.

    Article  PubMed  Google Scholar 

  33. Eom, J., Xu, X. G., De, S., and Shi, C., Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis. Medical physics 37(8):4389–4400, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gustafson, H. M., Cripton, P. A., Ferguson, S. J., and Helgason, B., Comparison of specimen-specific vertebral body finite element models with experimental digital image correlation measurements. Journal of the mechanical behavior of biomedical materials 65:801–807, 2017.

    Article  PubMed  Google Scholar 

  35. Wang, G., Liu, Y., Xiong, W., and Li, Y., An improved non-local means filter for color image denoising. Optik 173:157–173, 2018.

    Article  Google Scholar 

  36. Lokesh, S., Kumar, P. M., Devi, M. R., Parthasarathy, P., and Gokulnath, C., An automatic tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map. Neural Computing and Applications:1–11, 2018.

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Muthayammal Engineering College, Rasipuram, India

    E. Punarselvam

  2. Department of Mechanical Engineering, Muthayammal Engineering College, Rasipuram, India

    P. Suresh

Authors
  1. E. Punarselvam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Punarselvam.

Ethics declarations

Conflict of interests

The authors declare that this article content has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the author.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Mobile & Wireless Health

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Punarselvam, E., Suresh, P. Non-Linear Filtering Technique Used for Testing the Human Lumbar Spine FEA Model. J Med Syst 43, 34 (2019). https://doi.org/10.1007/s10916-018-1148-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-1148-6

Keywords