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Automatic recognition of bone for x-ray bone densitometry

机译：自动识别X射线骨密度的骨骼

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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：我们描述了一种用于自动识别和分离代表骨骼的像素的像素，其在腹部的双能点扫描投影射线照片中代表软组织。为了实现稳定的突出骨密度定量测量，必须进行使用仅含有软组织的区域中使用样品骨的校准。此外，必须测量骨骼的投影区域。我们表明，使用具有现实噪声低噪声的图像，像素值的直方图表现出与软组织区域对应的明确定义的峰值。校准区段值的固定倍数的阈值容易将骨从软组织中分离在各种患者研究中。我们在Holog QDR-1000骨密度计中使用的技术是迅速，鲁棒，并且比使用边缘检测的传统人工智能方法更简单，以定义对象和专家系统以识别它们。

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