JACIII Vol.22 No.1 pp. 113-120
doi: 10.20965/jaciii.2018.p0113


Implanted Knee Joint Kinematics Recognition in Digital Radiograph Images Using Particle Filter

Kento Morita*, Manabu Nii*, Norikazu Ikoma**, Takatoshi Morooka***, Shinichi Yoshiya***, and Syoji Kobashi*

*University of Hyogo
2167 Shosha, Himeji 671-2280, Japan

**Nippon Institute of Technology
4-1 Gakuendai, Miyashiro-Machi, Minami Saitama-Gun, Saitama 345-8501, Japan

***Hyogo College of Medicine
1-1 Mukogawa-cho, Nishinomiya, Hyogo 663-8501, Japan

April 20, 2017
October 27, 2017
January 20, 2018
knee kinematics analysis, total knee arthroplasty, particle filter, 2-D/3-D image registration

Implanted knee kinematics recognition is required in total knee arthroplasty (TKA), which replaces damaged knee joint with artificial one. The 3-D kinematics of implanted knee in-vivo is used to quantify the knee function for diagnosis of TKA patients and to evaluate the design of TKA prosthesis and surgical techniques. There are some methods for the implanted knee kinematics estimation, however, those methods are classified into still image analysis. The discontinuous knee kinematics estimated by the still image analysis is not considered as the actual knee kinematics. This paper proposes an kinematics recognition method for implanted knee using particle filter. The proposed method estimates the 3-D pose/position parameters, which are varying in time, based on a priori knowledge of time evolution of the parameters represented by random walk models and utilizing similarity between acquired DR image frame and synthesized DR image based on hypothesized value of the parameters. The experimental results showed that the proposed method successfully estimated the 3-D implanted knee kinematics with an accuracy of 1.61 mm and 0.32°.

Cite this article as:
K. Morita, M. Nii, N. Ikoma, T. Morooka, S. Yoshiya, and S. Kobashi, “Implanted Knee Joint Kinematics Recognition in Digital Radiograph Images Using Particle Filter,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 113-120, 2018.
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