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.
翻译:由于组织变形、复杂的照相机运动和重要的三维(3D)解剖表面,图像像素可能具有非硬化变形,传统的摩西化方法无法实时对腹膜镜图像产生强大的效果。为了解决这个问题,本文件提出了一个新的二维(2D)非硬化同步本地化和绘图系统(SLAM)系统,该系统能够弥补像素变形并实时进行图像摩擦。2D非硬化SLAM系统的关键算法是预期最大化和双偏移算法,它能够实时产生与稀疏和噪音图像相匹配的光滑和浓厚的畸形场。为减少累积错误,提出了基于不确定性的循环关闭方法。为了实现实时性能,CPU和GPU平行计算技术都用于实验性磁性模型的精确度,并且用于实验性磁性模型的不精确性数据。