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   数字胸片图像 的翻译结果: 查询用时:0.012秒
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数字胸片图像
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  digital chest radiographs
     Several Methods for Ribs Segmentation in Digital Chest Radiographs
     CR数字胸片图像的几种肋骨分割方法
短句来源
     Image Segmentation of Digital Chest Radiographs Basedon Gaussian Curve Plane Threshold Method
     基于高斯曲面阈值的CR数字胸片图像分割
短句来源
  “数字胸片图像”译为未确定词的双语例句
     An Automatic Method Segmenting Lung Regions From Digital Chest Radiograph
     一种数字胸片图像肺部区域的自动分割方法
短句来源
     Digital Chest Image Segmentation Based on Level Set Method
     基于水平集方法的数字胸片图像分割
短句来源
     In this paper,we present a novel trigonometric transform contrast enhancement method for digital chest radiograph, which is based on the anatomical feature of the image processed.
     介绍一种基于数字胸片图像解剖结构的三角形变换对比度增强方法。
短句来源
  相似匹配句对
     Singular Value Decomposition of Digital Image
     数字图像的奇异值分解
短句来源
     Photography in Digital Time
     数字时代的图像
短句来源
     Digital Chest Image Segmentation Based on Level Set Method
     基于水平集方法的数字胸片图像分割
短句来源
     Several Methods for Ribs Segmentation in Digital Chest Radiographs
     CR数字胸片图像的几种肋骨分割方法
短句来源
     Image Segmentation of Digital Chest Radiographs Basedon Gaussian Curve Plane Threshold Method
     基于高斯曲面阈值的CR数字胸片图像分割
短句来源
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  digital chest radiographs
One hundred digital chest radiographs of low-birthweight infants were obtained in the NICU using a storage phosphor system.
      
In 75 patients referred for thoracic CT, digital chest radiographs were randomly obtained with either SPT at a standard dose (speed class S400, n=25), standard-dose FDT (S400, n=25) or FDT at a low dose (S800, n=25).
      
The purpose of our study was to extract the two-dimensional (2D) cardiothoracic ratio from digital chest radiographs using image analysis software on a Magic View 300 system.
      
Integration of Temporal Subtraction and Nodule Detection System for Digital Chest Radiographs into Picture Archiving and Commu
      
Since May 2002, temporal subtraction and nodule detection systems for digital chest radiographs have been integrated into our hospital's picture archiving and communication systems (PACS).
      
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In this paper,we present a novel trigonometric transform contrast enhancement method for digital chest radiograph, which is based on the anatomical feature of the image processed. With automatic segmenting chest image into a lung region and a non - chest region, a positive linear transform and a negative one are used in lung region and the other region respectively. Therefore,it can enhance the contrast both in lung regions and in mediastinum region up to entire dynamic range of display device.

介绍一种基于数字胸片图像解剖结构的三角形变换对比度增强方法。该方法根据图像的直方图将胸片分成肺部区域和肺部以外的区域,并在这两个区域分别采用正、负线性变换。从而能够同时使肺区和肺部以外区域的对比度都得到最大的增强。

In medical domain, the Computed radiography image system comes into a brand-new development. Image segmentation takes an important place in medical image processing. Different segmentation methods used in medical images cause different effects due to the particularities of medical images. With digital chest radiographs as research objects in this paper, several segmentation methods are presented to detect the ribs in digital chest radiographs, which include iterative probabilistic relaxation, K-means clustering...

In medical domain, the Computed radiography image system comes into a brand-new development. Image segmentation takes an important place in medical image processing. Different segmentation methods used in medical images cause different effects due to the particularities of medical images. With digital chest radiographs as research objects in this paper, several segmentation methods are presented to detect the ribs in digital chest radiographs, which include iterative probabilistic relaxation, K-means clustering and Gaussian curve plane threshold methods, etc. The evaluations of their results are given in the end of the paper. The experimental results indicate that Gaussian curve plane threshold method is more effective than the others to detect the ribs in digital chest radiographs. By using it, subsequent processing can be simplified and full information of the ribs can be obtained, so more reliable experiment data can be supplied to subsequent computer aided diagnosis.

在医学领域里,计算机X线摄影(computedradiography,CR)影像系统已经进入全新的发展阶段。图像分割在医学图像处理中占有很重要的位置,由于医学图像的一些特殊性,不同的分割方法会产生不同的效果。以CR数字胸片图像为研究对象,给出了概率松弛迭代法、K-均值聚类法和高斯曲面阈值法在胸片肋骨分割中的应用,并且对其结果给予了一定的评价。实验结果表明,几种分割方法中高斯曲面阈值法更为有效,它方便后继处理,可以得到比较完整的肋骨信息,为后期的计算机辅助诊断提供更为可靠的实验数据。

With digital chest radiographs being research objects in this paper, Gaussian curve plane threshold method is presented to detect the ribs in digital chest radiographs. The experimental results indicate that it is an effective method to detect the ribs in digital chest radiographs, and by using it subsequent processing can be simplified and full information of the ribs can be obtained, so more reliable experiment data can be supplied to subsequent computer aided diagnosis.

以CR数字胸片图像为研究对象,提出基于高斯曲面阈值法的分割算法,它方便后继处理,可以得到比较完整的肋骨信息,为后期的计算机辅助诊断提供更为可靠的实验数据,是一种更为有效的算法。

 
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