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Automatic Extraction of Proximal Femur Contours from Calibrated X-Ray Images Using 3D Statistical Models

机译:使用3D统计模型从校准的X射线图像中自动提取股骨轮廓

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Automatic identification and extraction of bone contours from x-ray images is the first essential task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The initialization is solved by an Estimation of Bayesian Network Algorithm to fit a multiple component geometrical model to the x-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the x-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Our experimental results demonstrate its performance and efficacy even when part of the images are occluded.
机译:从X射线图像自动识别和提取骨骼轮廓是进一步医学图像分析的首要基本任务。在本文中,我们提出了一种基于3D统计模型的框架,用于从校准的X射线图像提取股骨近端轮廓。通过贝叶斯网络算法的估计来解决初始化问题,以使多分量几何模型适合X射线数据。通过在3D统计模型和X射线图像之间进行非刚性2D / 3D配准来完成轮廓提取,其中,通过基于贝叶斯推理的图形模型来提取骨骼轮廓。我们的实验结果证明了它的性能和功效,即使部分图像被遮挡也是如此。

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