title: Robust feature tracking on the beating heart for a robotic-guided endoscope.
abstract: BACKGROUND
  Visualization during minimally invasive bypass surgery on the beating heart can be enhanced by using a robotic-guided endoscope and visual servoing from the endoscopic images. In order to achieve these objectives, this work has focused on developing and testing algorithms for accurate, robust and real-time motion tracking of features on the beating heart, using marker-less approaches and an uncalibrated endoscope.
  
  
  METHODS
  Lucas-Kanade pyramidal optical flow-based algorithms and speeded-up robust features (SURF)-based methods have been extensively evaluated, using a range of developed metrics, in order to quantify accuracy, robustness and drift under a variety of circumstances. Three sets of experiments are reported: the first set compared the two tracking methods, using a beating-heart phantom and a static endoscope; the second set evaluated the methods when images were taken using a moving robotic-guided endoscope; and finally, the Lucas-Kanade optical flow algorithm was extensively tested in a visual servoing application, using a robotic endoscope.
  
  
  RESULTS
  The combination of a Lucas-Kanade tracking algorithm and a SURF-based feature detection method gave the best performance in terms of accuracy and robustness of tracking, while preserving real-time computation requirements. The optimal parameters consist of a window size of 51 × 51 pixels and an interframe motion threshold of 20 pixels. Feature tracking was successfully integrated into uncalibrated visual servoing or a robotic-guided endoscope.
  
  
  CONCLUSIONS
  Robust feature tracking on a beating heart with endoscopic video can be achieved in real-time and may facilitate robotically-assisted, minimally invasive bypass surgery and conventional laparoscopic surgery.
authors: Philips Research North America, Briarcliff Manor, NY, USA. haytham.elhawary@philips.com
authors: Haytham
authors: H
authors: Elhawary
authors: Aleksandra
authors: A
authors: Popovic
mesh_terms: D001185:Artificial Intelligence; D001026:Coronary Artery Bypass / Q000295:instrumentation / Q000379:methods*; D004724:Endoscopy / Q000295:instrumentation / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000379:methods*