Browsing by Author "Chen, Chun-Ju"

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Autonomous and Stable Tracking of Endoscope Instrument Tools with Monocular Camera
(2012)
Song, Kai-Tai
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Chen, Chun-Ju
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電機工程學系
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Department of Electrical and Computer Engineering
With an autonomous tool tracking system, surgeons do not need to handle the endoscope during surgery. This paper aims to develop an image tracking system with a stable view for minimally invasive surgery (MIS) by using monocular endoscope. In order to provide a stable view for the surgery, we propose to set a buffer zone in the center of image frame for the robotic camera holder to track the endoscope instruments. If endoscope instruments are inside the buffer zone, the endoscope robot will keep still. However, if one or both of them move away from the buffer zone, the robot will start to track the instrument. Furthermore, by calculating the distance between two instruments in the image plane, the robotic camera holder can track the depth to the instruments. The robot will therefore zoom in if the distance is too short. A safe distance from the camera lens to instrument tools can therefore be maintained during the image tracking. Preliminary experimental results show that the proposed design achieves satisfactory tracking accuracy in real time and provides a stable view simultaneously.
Image Tracking of Laparoscopic Instrument Using Spiking Neural Networks
(2013-01-01)
Chen, Chun-Ju
;
Huang, Wayne Shin-Wei
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Song, Kai-Tai
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電控工程研究所
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Institute of Electrical and Control Engineering
Minimally Invasive Surgery (MIS) has become more and more popular in recent years. An endoscopic image tracking system will assist surgeons to adjust the field of view autonomously in MIS. In this paper, we propose a novel image tracking algorithm based on natural features of surgical instruments. We suggest to use texture and geometric features in laparoscopic instrument imagery and to adopt a spiking neural network approach for object detection; considering color will be affected by lighting and the white balance conditions in the endoscope imagery. To enhance tracking performance, we further design a Kalman filter to combine with the neuro-based tracker. The instrument can be detected more robustly despite of deformation of the instrument image during surgery. A laparoscopic video has been tested to verify the developed methods. Experimental results show that two instruments can be distinguished and tracked simultaneously in the surgical video.