Skip to main content
SpringerLink
Log in

Segmentation of Bone Tissue from CT Images

  • Conference paper
  • First Online:
Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

  • 516 Accesses

Abstract

Segmentation of the bone structures in computed tomography (CT) is crucial for research as it plays a substantial role in surgical planning, disease diagnosis, identification of organs and tissues, and analysis of fractures and bone densities. Manual segmentation of bones could be tedious and not suggested as there could be human bias present. In this paper, we evaluate some existing approaches for bone segmentation and present a method for segmenting bone tissues from CT images. In this approach, the CT image is first enhanced to remove the artifacts surrounding the bone. Subsequently, the image is binarized and outliers are removed to get the bone regions. The proposed method has a Dice index of 0.9321, Jaccard index (IoU) of 0.8729, a precision of 0.9004, and a recall of 0.9662.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–37 (2000). https://doi.org/10.1146/annurev.bioeng.2.1.315

    Article  Google Scholar 

  2. Tomazevic, M., Kreuh, D., Kristan, A., Puketa, V., Cimerman, M.: Preoperative planning program tool in treatment of articular fractures process of segmentation procedure. In: Bamidis, P.D., Pallikarakis, N. (eds.) XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. IFMBE Proceedings, vol. 29. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13039-7_108

  3. Tassani, S., Matsopoulos, G.K., Baruffaldi, F.: 3D identification of trabecular bone fracture zone using an automatic image registration scheme: a validation study. J. Biomech. 45(11), 2035–2040 (2012)

    Google Scholar 

  4. He, Y., Shi, C., Liu, J., Shi, D.: A segmentation algorithm of the cortex bone and trabecular bone in Proximal Humerus based on CT images. In: 2017 23rd International Conference on Automation and Computing (ICAC), pp. 1–4 (2017). https://doi.org/10.23919/IConAC.2017.8082093

  5. Paulano, F., Jiménez, J.J., Pulido, R.: 3D segmentation and labeling of fractured bone from CT images. Vis. Comput. 30, 939–948 (2014). https://doi.org/10.1007/s00371-014-0963-0

    Article  Google Scholar 

  6. Fornaro, J., Székely, G., Harders, M.: Semi-automatic segmentation of fractured Pelvic bones for surgical planning. In: Bello, F., Cotin, S. (eds.) Biomedical Simulation. ISBMS 2010. Lecture Notes in Computer Science, vol. 5958. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11615-5_9

  7. Kyle Justice, R., Stokely, E.M., Strobel, J.S., Ideker M.D.R.E., Smith, W.M.: Medical image segmentation using 3D seeded region growing. In: Proceedings of SPIE 3034, Medical Imaging 1997: Image Processing (1997). https://doi.org/10.1117/12.274179

  8. Kaminsky, J., Klinge, P., Rodt, T., Bokemeyer, M., Luedemann, W., Samii, M.: Specially adapted interactive tools for an improved 3D-segmentation of the spine. Comput. Med. Imag. Graph. 28(3), 119–27 (2004). https://doi.org/10.1016/j.compmedimag.2003.12.001

  9. Lee, P.-Y., Lai, J.-Y., Hu, Y.-S., Huang, C.-Y., Tsai, Y.-C., Ueng, W.-D.: Virtual 3D planning of pelvic fracture reduction and implant placement. Biomed. Eng. Appl. Basis Commun. 24, 245–262 (2012). https://doi.org/10.1142/S101623721250007X

  10. Huang, C.-Y., Luo, L.-J., Lee, P.-Y., Lai, J.-Y., Wang, W.-T.: Efficient segmentation algorithm for 3D bone models construction on medical images. J. Med. Biol. Eng. (2011). https://doi.org/10.5405/jmbe.734

  11. Pérez-Carrasco, J.A., Acha-Piñero, B., Serrano, C.: Segmentation of bone structures in 3D CT images based on continuous max-flow optimization. In: Progress in Biomedical Optics and Imaging—Proceedings of SPIE (2015). https://doi.org/10.1117/12.2082139

  12. Klein, A., Warszawski, J., Hillengaß, J.: Automatic bone segmentation in whole-body CT images. Int. J. CARS 14, 21–29 (2019). https://doi.org/10.1007/s11548-018-1883-7

    Article  Google Scholar 

  13. Zhang, J., Qian, W., Xu, D., Pu, Y.: DFM-Net: a contextual inference network for T2-weighted image segmentation of the pelvis. In: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021). SPIE 12083 (2021). https://doi.org/10.1117/12.2623470

  14. Xiong, X., Smith, B.J., Graves, S.A., Sunderland, J.J., Graham, M.M., Gross, B.A., Buatti, J.M., Beichel, R.R.: Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN. Med. Phys. 49(3), 1585–1598 (2022). https://doi.org/10.1002/mp.15440

    Article  Google Scholar 

  15. Gangwar, T., Calder, J., Takahashi, T., Bechtold, J., Schillinger, D.: Robust variational segmentation of 3D bone CT data with thin cartilage interfaces. Med. Image Anal. 47 (2018). https://doi.org/10.1016/j.media.2018.04.003

  16. Sebastian, T.B., Tek, H., Crisco, J.J., Kimia, B.B.: Segmentation of carpal bones from CT images using skeletally coupled deformable models. Med. Image Anal. 7(1), 21–45 (2003). https://doi.org/10.1016/s1361-8415(02)00065-8

    Article  Google Scholar 

  17. Shadid, W., Willis, A.: Bone fragment segmentation from 3D CT imagery using the probabilistic watershed transform. Proc. IEEE Southeastcon 2013, 1–8 (2013). https://doi.org/10.1109/SECON.2013.6567509

    Article  Google Scholar 

  18. Moghari, M.H., Abolmaesumi, P.: Global registration of multiple bone fragments using statistical atlas models: feasibility experiments. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. 2008, 5374–7 (2008). https://doi.org/10.1109/IEMBS.2008.4650429

    Article  Google Scholar 

  19. USevillabonemuscle Dataset. http://grupo.us.es/grupobip/research/research-topics/segmentation-of-abdominal-organs-and-tumors/

  20. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. 43(3), 60 (2019). https://doi.org/10.1007/s10916-019-1176-x

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by JSPS Grant-in-Aid for Scientific Research (C) (20K11873) and Chubu University Grant.

Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    Shrish Kumar Singhal, Bibek Goswami, Yuji Iwahori & M. K. Bhuyan

  2. Department of Computer Science, Chubu University, Kasugai, 487-8501, Japan

    Yuji Iwahori & M. K. Bhuyan

  3. Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, 464-8681, Japan

    Akira Ouchi & Yasuhiro Shimizu

Authors
  1. Shrish Kumar Singhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bibek Goswami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuji Iwahori
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. K. Bhuyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Akira Ouchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yasuhiro Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shrish Kumar Singhal .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singhal, S.K., Goswami, B., Iwahori, Y., Bhuyan, M.K., Ouchi, A., Shimizu, Y. (2023). Segmentation of Bone Tissue from CT Images. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_19

Download citation

Publish with us

Policies and ethics

Search

Navigation