基于视觉模型的c臂系统标定与图像校正

Shaojie Lv, Cai Meng, F. Zhou, Bo Liu, Xiaojun Zhou
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引用次数: 6

摘要

在传统方法中,c臂系统的标定是基于c臂图像的校正结果进行的。在校正图像时,采用高阶多项式对相关畸变像素与理想图像像素进行拟合。然而,精确的理想图像不容易建立,严重制约了校正的精度。本文提出了一种不依赖于理想图像,将校正和定标作为一个过程进行整合的基于视觉模型的方法。在该方法中,使用Tsai的校准方法计算c臂系统的内部和外部参数,并在图像的中心区域进行标记。然后,采用非线性优化方法,得到包含所有标记的更精确的c臂成像模型。最后,利用内部参数的畸变参数对畸变图像进行校正。为了验证该方法,对已知的点间距离进行了重构,实验结果表明,该方法的最大误差小于1mm。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Model Based C-Arm System Calibration and Image Correction
In traditional methods, the C-Arm system calibration was performed based on the result of C-Arm image correction. When correcting images, high-order polynomial was used to fit relating distorted pixels to ideal image pixel. However accurate ideal image could not be established easily, that heavily restricted the precision of correction. In this paper, a novel method is presented based on visual model which does not depend on ideal image and integrates correction and calibration as one process. In this method, the Tsai's calibration method is used to calculate the internal and external parameters of C-Arm system with markers in the center region of the image. Then, nonlinear optimization is applied to get more precise C-Arm imaging model with all markers. Finally, the distortion image is corrected using the distortion parameters of internal parameters. To verify this method, known distances between points are reconstructed and the experimental results show that the maximum error is less than 1mm.
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