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Automatic localization of three-dimensional cephalometric landmarks onCBCT images by extracting symmetry features of the skull

机译:三维头颅地标的自动定位通过提取头骨的对称特征的CBCT图像

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摘要

To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm. The cephalometric land marks on CBCT data were detected automatically with in the mean error less than 2 mm.
机译:为了提出一种在CBCT数据上自动定位3D头颅地标的算法,这些算法对于头颅法和上呼吸道容积分析都是有用的。自动检测的目标是20个地标,其中12个地标存在于矢状面中。从体积自动检测中矢状平面是一项艰巨的任务。通过提取颅骨对称特征的统计参数来检测矢状中平面。根据从人体解剖学中提取的边界定义,矢状中平面被分为四个象限。将模板匹配算法应用到矢状面中部,以识别感兴趣的区域ROI,进一步提取边缘特征,以在各个区域中形成轮廓。通过使用提取的地标解剖定义知识,自动定位地标。用于检测20个界标的总平均误差为1.88 mm,标准偏差为1.10 mm。自动检测CBCT数据的头颅地标,平均误差小于2毫米。

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