Ji Songbai, Fan Xiaoyao, Roberts David W, Paulsen Keith D
Med Image Comput Comput Assist Interv. 2014;17(Pt 1):440-7. doi: 10.1007/978-3-319-10404-1_55.
Camera calibration is central to obtaining a quantitative image-to-physical-space mapping from stereo images acquired in the operating room (OR). A practical challenge for cameras mounted to the operating microscope is maintenance of image calibration as the surgeon's field-of-view is repeatedly changed (in terms of zoom and focal settings) throughout a procedure. Here, we present an efficient method for sustaining a quantitative image-to-physical space relationship for arbitrary image acquisition settings (S) without the need for camera re-calibration. Essentially, we warp images acquired at S into the equivalent data acquired at a reference setting, S(0), using deformation fields obtained with optical flow by successively imaging a simple phantom. Closed-form expressions for the distortions were derived from which 3D surface reconstruction was performed based on the single calibration at S(0). The accuracy of the reconstructed surface was 1.05 mm and 0.59 mm along and perpendicular to the optical axis of the operating microscope on average, respectively, for six phantom image pairs, and was 1.26 mm and 0.71 mm for images acquired with a total of 47 arbitrary settings during three clinical cases. The technique is presented in the context of stereovision; however, it may also be applicable to other types of video image acquisitions (e.g., endoscope) because it does not rely on any a priori knowledge about the camera system itself, suggesting the method is likely of considerable significance.
相机校准是从手术室（OR）采集的立体图像中获得定量图像到物理空间映射的核心。对于安装在手术显微镜上的相机而言，一个实际挑战是在整个手术过程中，随着外科医生的视野不断变化（在变焦和焦距设置方面），要维持图像校准。在此，我们提出一种高效方法，无需重新校准相机，就能针对任意图像采集设置（S）维持定量图像到物理空间的关系。本质上，我们使用通过连续对一个简单模型成像获得的光流变形场，将在设置S下采集的图像扭曲为在参考设置S(0)下采集的等效数据。推导出了畸变的闭式表达式，并基于在S(0)处的单次校准进行三维表面重建。对于六对模型图像，重建表面沿手术显微镜光轴方向和垂直于光轴方向的平均精度分别为1.05毫米和0.59毫米；在三个临床病例中，对于总共47个任意设置采集的图像，精度分别为1.26毫米和0.71毫米。该技术是在立体视觉背景下提出的；然而，它也可能适用于其他类型的视频图像采集（例如内窥镜），因为它不依赖于关于相机系统本身的任何先验知识，这表明该方法可能具有相当重要的意义。
