International Journal of Computer Theory and Engineering

Abstract

Laparoscopic surgery becomes increasingly popular due to high benefits to both surgeon and patients. In this paper, we propose the adaptive mean-shift Kalman tracking algorithm based on the mean-shift algorithm and the Kalman filter for tracking a laparoscopic instrument in laparoscopic surgery. An iterative update of the target candidate in the mean-shift process can improve the tracking performance over a typical mean-shift algorithm. In addition, the Kalman filter is employed to enhance the chance of tracking accuracy, especially when the object disappears from the scene. In this study, we tested the tracking performance of our proposed algorithm by using the different situations from simulated videos. Our experimental results show that the proposed algorithm can locate the target object correctly even when the size and the shape of the target have been changed. In the difficult situation when the target is hiding behind an obstacle, this algorithm can still track the target object correctly after it becomes apparent. Therefore, this proposed algorithm can be used for locating the tip of the laparoscopic instrument in real laparoscopic surgery.