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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Realtime automatic metal extraction of medical x-ray images for contrast improvement
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Realtime automatic metal extraction of medical x-ray images for contrast improvement

机译：实时自动提取医学X射线图像的金属以改善对比度

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This paper focuses on an approach for real-time metal extraction of x-ray images taken from modern x-ray machines like C-arms. Such machines are used for vessel diagnostics, surgical interventions, as well as cardiology, neurology and orthopedic examinations. They are very fast in taking images from different angles. For this reason, manual adjustment of contrast is infeasible and automatic adjustment algorithms have been applied to try to select the optimal radiation dose for contrast adjustment. Problems occur when metallic objects, e.g., a prosthesis or a screw, are in the absorption area of interest. In this case, the automatic adjustment mostly fails because the dark, metallic objects lead the algorithm to overdose the x-ray tube. This outshining effect results in overexposed images and bad contrast. To overcome this limitation, metallic objects have to be detected and extracted from images that are taken as input for the adjustment algorithm. In this paper, we present a real-time solution for extracting metallic objects of x-ray images. We will explore the characteristic features of metallic objects in x-ray images and their distinction from bone fragments which form the basis to find a successful way for object segmentation and classification. Subsequently, we will present our edge based real-time approach for successful and fast automatic segmentation and classification of metallic objects. Finally, experimental results on the effectiveness and performance of our approach based on a vast amount of input image data sets will be presented.
机译：本文重点介绍一种实时金属提取从现代X射线机（如C型臂）拍摄的X射线图像的方法。这种机器用于血管诊断，外科手术以及心脏病，神经病和骨科检查。它们从不同角度拍摄图像的速度非常快。由于这个原因，手动调整对比度是不可行的，并且已经应用​​自动调整算法来尝试选择用于对比度调整的最佳辐射剂量。当金属物体（例如假肢或螺钉）位于目标吸收区域时，会出现问题。在这种情况下，自动调整通常会失败，因为黑色的金属物体会导致算法过量使用X射线管。这种出奇的效果导致曝光过度的图像和不良的对比度。为了克服此限制，必须检测金属对象并从图像中提取金属对象，这些图像被用作调整算法的输入。在本文中，我们提出了一种提取X射线图像金属物体的实时解决方案。我们将探索X射线图像中金属物体的特征以及它们与骨骼碎片的区别，从而为寻找成功的物体分割和分类方法奠定了基础。随后，我们将介绍基于边缘的实时方法，以成功，快速地对金属对象进行自动分割和分类。最后，将介绍基于大量输入图像数据集的方法的有效性和性能的实验结果。

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