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International Conference on Information Technologies in Biomedicine

ITIB 2019: Information Technology in Biomedicine pp 314–322Cite as

Patella – Atlas Based Segmentation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1011))

Abstract

The main aim of this research is to present an automated image segmentation methodology being used in the field of medicine. The paper shows the automatic extraction of patella in the knee joint based on atlas segmentation. This method allows to build a feature vector that automates and streamlines known segmentation methods (e.g. fuzzy c-means (FCM), fuzzy connectedness (FC)). The described methodology has been implemented in MATLAB and tested on clinical computed tomography (CT) slices of the knee joint in transverse plane.

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References

  1. Aprovitola, A., Gallo, L.: Knee bone segmentation from MRI: a classification and literature review. Biocybern. Biomed. Eng. 36(2), 437–449 (2016)

    Article  Google Scholar 

  2. Bochenek, A., Reicher, M.: The Human Anatomy. PZWL, Warsaw (1990)

    Google Scholar 

  3. Kubicek, J., Penhaker, M., Augustynek, M., et al.: Segmentation of knee cartilage: a comprehensive review. J. Med. Imaging Health Inform. 8(3), 401–418 (2018)

    Article  Google Scholar 

  4. Minerny, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Med. Image Anal. 1(2), 91–108 (1996)

    Article  Google Scholar 

  5. Pham, D., Xu, C., Prince, J.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–37 (2000)

    Article  Google Scholar 

  6. Santiago, F., Calvo, R., Lopez, J., Alvarez, L., Garcia, M.: T2 mapping in patellar chondromalacia. Eur. Radiol. 83, 984–988 (2014)

    Article  Google Scholar 

  7. Sun, Y. Teo, E., Zhang, Q.: Discussions of knee joint segmentation. In: International Conference on Biomedical and Pharmaceutical Engineering-ICBPE (2006)

    Google Scholar 

  8. Tamez-Pena, J., Farber, G., Gonzales, P., Schreyer, E., Schneider, E., Totterman, S.: Unsupervised segmentation and quantification of anatomical knee features: data from osteartrithis initiative. IEEE Trans. Biomed. Eng. 59(4), 1177–1186 (2012)

    Article  Google Scholar 

  9. Udupa, J., Samarasekera, S.: Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Model. Image Process. 58, 246–261 (1996)

    Article  Google Scholar 

  10. Zarychta, P.: A new approach to knee joint arthroplasty. Comput. Med. Imaging Graph 65, 32–45 (2018)

    Article  Google Scholar 

  11. Zarychta, P.: Multi-step process in computer assisted diagnosis of posterior cruciate ligaments. Biocybern. Biomed. Eng. 36(4), 657–669 (2016)

    Article  Google Scholar 

  12. Zarychta, P.: ACL and PCL of the knee joint in the computer diagnostics. In: Napieralski A. (ed.) 21st International Conference Mixed Design of Integrated Circuits and Systems MIXDES, pp. 489–492 (2014)

    Google Scholar 

  13. Zarychta, P., Konik, H., Zarychta-Bargiela, A.: Computer assisted location of the lower limb mechanical axis. In: Pietka E., Kawa J. (eds.) Information Technologies in Biomedicine, Lecture Notes in Bioinformatics, vol. 7339, pp. 93–100 (2012)

    Google Scholar 

  14. Zarychta, P.: Cruciate ligaments of the knee joint in the computer analysis. In: Pietka, E., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Biomedicine, Advances in Intelligent and Soft Computing, vol. 283, pp. 71–80 (2014)

    Google Scholar 

  15. Zarychta P.: Automatic registration of the medical images T1- and T2-weighted MR knee images. In: Napieralski A. (ed.) International Conference Mixed Design of Integrated Circuits and Systems MIXDES, pp. 741–745 (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Silesian University of Technology, Faculty of Biomedical Engineering, Roosevlta 40, Zabrze, Poland

    Piotr Zarychta

Authors
  1. Piotr Zarychta
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Correspondence to Piotr Zarychta .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Wojciech Wieclawek

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Zarychta, P. (2019). Patella – Atlas Based Segmentation. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_28

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