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The extraction of the best SGLD texture features in the ultrasound B-scan images of cancered stomach coats

机译:胃癌薄层的B超扫描图像中最佳SGLD纹理特征的提取

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SGLD (spatial gray level dependence) matrices are used to analyze the B-scan images of 23 samples of normal stomach coats and 14 samples of cancerous stomach coats. According to these matrices, the values of eight texture features of each sample image are computed. Two groups of conditional frequency distributions are obtained. On the basis of these distributions, the authors evaluated the quality, which reflects the error probability in discriminating between pattern classes of all the features. By comparing the measurements of the quality, the authors select from these features the most effective ones in discriminating between a normal stomach and a cancerous stomach. The evaluation methods include normal distribution hypothesis testing, and T testing. The result of the experiments indicates that the selected texture features can be applied to an automatic diagnosis system in the near future.
机译:SGLD(空间灰度依赖性)矩阵用于分析23份正常胃衣样本和14份癌胃衣样本的B扫描图像。根据这些矩阵,计算每个样本图像的八个纹理特征的值。获得两组条件频率分布。基于这些分布,作者评估了质量,这反映了在区分所有特征的图案类别时的错误概率。通过比较质量度量,作者从这些功能中选择了区分正常胃和癌胃的最有效方法。评估方法包括正态分布假设检验和T检验。实验结果表明,所选择的纹理特征可以在不久的将来应用于自动诊断系统。

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