Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36211
Title: 3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model
Authors: Yang, B
Wong, WK 
Liu, C
Poignet, P
Keywords: Robotic surgery
Visual tracking
Motion compensation
Stereo vision
Kernel function
Issue Date: 2014
Publisher: Elsevier
Source: Pattern recognition, 2014, v. 47, no. 9, p. 2962-2973 How to cite?
Journal: Pattern recognition 
Abstract: Visual tracking techniques based on stereo endoscope are developed to measure tissue motion in robot-assisted minimally invasive surgery. However, accurate 3D tracking of tissue surfaces remains challenging due to complicated deformation, poor imaging conditions, specular reflections and other dynamic effects during surgery. This study employs a robust and efficient 3D tracking scheme with two independent recursive processes, namely kernel-based inter-frame motion estimation and model-based intra-frame 3D matching. In the first process, target region is represented in joint spatial-color space for robust estimation. By defining a probabilistic similarity measure, a mean-shift-based iterative algorithm is derived for location of the target region in a new image. In the second process, the thin-plate spline model is used to fit the 3D shape of tissue surfaces around the target region. An iterative algorithm based on an efficient second-order minimization technique is derived to compute optimal model parameters. The two processes can be computed in parallel. Their outputs are combined to recover 3D information about the target region. The performance of the proposed method is validated using phantom heart videos and in vivo videos acquired by the daVinci (R) surgical robotic platform and a synthesized data set with known ground truth.
URI: http://hdl.handle.net/10397/36211
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2014.03.020
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