Real-time Nonrigid Mosaicking of Laparoscopy Images
This addresses the need for surgeons to have an extended field of view during laparoscopy, though it appears incremental as it builds on existing SLAM and mosaicking techniques for a specific medical application.
The paper tackled the problem of real-time mosaicking of laparoscopy images under non-rigid tissue deformation by proposing a novel 2D non-rigid SLAM system with an EMDQ algorithm, achieving real-time performance with accuracy demonstrated on in vivo and synthetic data.
The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on \textit{in vivo} and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.