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Standardization of thresholding for binary conversion of vocal tract modeling in computed tomography.

机译:在计算机断层扫描中声道建模的二进制转换的阈值标准化。

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Postoperative head and neck cancer patients suffer from speech disorders, which are the result of changes in their vocal tracts. Making a solid vocal tract model and measuring its transmission characteristics will provide one of the most useful tools to resolve the problem. In binary conversion of X-ray computed tomographic (CT) images for vocal tract reconstruction, nonobjective methods have been used by many researchers. We hypothesized that a standardized vocal tract model could be reconstructed by adopting the Hounsfield number of fat tissue as a criterion for thresholding of binary conversion, because its Hounsfield number is the nearest to air in the human body. The purpose of this study was to establish a new standardized method for binary conversion in reconstructing three-dimensional (3-D) vocal tract models. CT images for postoperative diagnosis were secondarily obtained from a CT scanner. Each patient's minimum settings of Hounsfield number for the buccal fat-pad regions were measured. Thresholds were set every 50 Hounsfield units (HU) from the bottom line of the buccal fat-pad region to -1024 HU, the images were converted into binary values, and were evaluated according to the three-grade system based on anatomically defined criteria. The optimal threshold between tissue and air was determined by nonlinear multiple regression analyses. Each patient's minimum settings of the buccal fat-pad regions were obtained. The optimal threshold was determined to be -165 HU from each patient's minimum settings of the Hounsfield number for the buccal fat-pad regions. To conclude, a method of 3-D standardized vocal tract modeling was established.
机译:术后头颈癌患者患有语音障碍,这是其声道变化的结果。制作可靠的声道模型并测量其传输特性将提供解决该问题的最有用工具之一。在X射线计算机断层扫描(CT)图像的二进制转换以用于声道重建中,许多研究人员已经使用了非客观方法。我们假设可以通过采用脂肪组织的Hounsfield数作为二元转换阈值的标准来重建标准化声道模型,因为它的Hounsfield数与人体中的空气最近。这项研究的目的是为重建三维(3-D)声道模型建立一种新的二进制转换标准方法。其次,从CT扫描仪获得用于术后诊断的CT图像。测量每个患者颊脂垫区域的Hounsfield数的最小设置。从颊脂肪垫区域的底线起每隔50 Hounsfield单位(HU)设置阈值,将图像转换为二进制值,并根据解剖学定义的标准根据三级系统进行评估。通过非线性多元回归分析确定组织与空气之间的最佳阈值。获得每个患者的颊脂垫区域的最低设置。根据每个患者颊颊脂肪垫区域的Hounsfield数的最小设置,确定最佳阈值为-165 HU。总而言之,建立了3-D标准化声道建模的方法。

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