腹腔镜图像的实时的非刚性拼接
Real-time Nonrigid Mosaicking of Laparoscopy Images
摘要：
扩大腹腔镜图像视野的能力可以帮助外科医生更好地了解解剖背景。然而，由于组织变形、复杂的摄像机运动和显著的三维（3D）解剖表面，图像像素可能具有非刚性变形，并且传统的镶嵌方法不能鲁棒地用于实时的【形】腹腔镜图像。为了解决这一问题，提出了一种新的二维非刚性同时定位与地图构建（SLAM）系统，该系统能够补偿像素的变形并实时的【形】进行图像拼接。该二维非刚性SLAM系统的核心算法是期望最大化和对偶四元数（EMDQ）算法，该算法能够实时的【形】从稀疏和含噪的图像特征匹配中生成平滑和稠密的变形场。提出了一种基于不确定度的闭环控制方法，以减小累积误差。为了达到实时的【形】性能，CPU和GPU并行计算技术被用于所有像素的密集镶嵌。在\textit{in vivo}和合成数据上的实验结果证明了该方法的可行性和准确性。
英文摘要：
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.
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