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Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

机译:基于标尺的自动C型臂图像拼接没有重叠约束

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摘要

In this paper, we propose a new method for stitching multiple fluoroscopic images taken by a C-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we “paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.
机译:在本文中,我们提出了一种新的方法来缝合C臂仪器拍摄的多个透视图像。我们在获取图像时使用带有编号刻度的X射线可透标尺,并且图像拼接基于检测图像中的标尺部分并将其与虚拟标尺的相应部分进行匹配。为了实现这一目标,我们首先检测尺子和数字上的规则间隔刻度。标记完刻度后,对于每张图像,我们在标尺上都有位置和每个刻度的相关编号。然后,我们使用虚拟标尺初始化全景X射线图像,并通过将检测到的标尺部分在原始图像上对齐到全景图像上虚拟标尺的相应部分来“粘贴”每个图像。我们的方法基于标尺匹配,但不需要匹配成对图像中的相似特征点,因此,我们不一定需要图像之间有重叠。我们在八种不同的X射线图像数据集(包括长骨头和完整的脊柱)上测试了我们的方法。定性和定量实验表明我们的方法取得了很好的结果。

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