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Automatic recognition of bone for x-ray bone densitometry

机译：自动识别骨骼以进行X射线骨密度测定

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Abstract: We described a method for automatically identifying and separating pixels representing bone from those representing soft tissue in a dual- energy point-scanned projection radiograph of the abdomen. In order to achieve stable quantitative measurement of projected bone mineral density, a calibration using sample bone in regions containing only soft tissue must be performed. In addition, the projected area of bone must be measured. We show that, using an image with a realistically low noise, the histogram of pixel values exhibits a well-defined peak corresponding to the soft tissue region. A threshold at a fixed multiple of the calibration segment value readily separates bone from soft tissue in a wide variety of patient studies. Our technique, which is employed in the Hologic QDR-1000 Bone Densitometer, is rapid, robust, and significantly simpler than a conventional artificial intelligence approach using edge-detection to define objects and expert systems to recognize them.!
机译：摘要：我们描述了一种在腹部双能量点扫描投影射线照相中自动识别和分离代表骨骼的像素和代表软组织的像素的方法。为了实现对预计的骨矿物质密度的稳定定量测量，必须在仅包含软组织的区域中使用样本骨骼进行校准。另外，必须测量骨骼的投影面积。我们表明，使用噪声极低的图像，像素值的直方图会显示一个与软组织区域相对应的明确定义的峰。在各种患者研究中，固定段校准值的倍数处的阈值很容易使骨骼与软组织分离。我们的技术已在Hologic QDR-1000骨密度仪中使用，它比传统的使用边缘检测来定义对象和专家系统来识别对象的人工智能方法更加快速，强大和简单。

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