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首页> 外文会议>2013 International Conference on QiR >Lung tuberculosis identification based on statistical feature of thoracic X-ray
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Lung tuberculosis identification based on statistical feature of thoracic X-ray

机译：基于胸部X射线统计特征的肺结核识别

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This paper presents experiments and results on lung tuberculosis (TB) identification by using computer. This research's attempt is to reduce patient waiting time in obtaining X-ray diagnosis result on lung TB disease due to the mismatch the ratio of radiologist to the number of patients, especially in remote areas in Indonesia. To imitate radiologist which make visual examination on textural feature of thoracic X-ray images to make diagnosis, we exploit textural features calculated by computer to be used as descriptor in classifying images as TB or non-TB. We used statistical feature of image histograms by calculate five features: mean, standar deviation (std), skewness, kurtosis, and entropy. Features calculated where then reduced to two and one principal feature using Principal Componen Analysis (PCA) method. Finally, we used minimum distance classifier as classifier method based on two and one principal feature as descriptor. This experiment results shown that it is possible to classify TB and non-TB images based on statistical features on image histogram.
机译：本文介绍了计算机识别肺结核的实验和结果。这项研究的目的是减少由于放射医师与患者人数比例不匹配而导致获得肺结核疾病X射线诊断结果的患者等待时间，特别是在印度尼西亚的偏远地区。为了模仿放射科医生对胸部X射线图像的纹理特征进行目视检查以进行诊断，我们利用计算机计算出的纹理特征作为描述符将图像分类为TB或非TB。我们通过计算五个特征来使用图像直方图的统计特征：平均值，标准偏差（std），偏度，峰度和熵。计算出的特征然后使用主成分分析（PCA）方法简化为两个和一个主要特征。最后，我们以最小距离分类器为基础，以两个主特征作为一个描述符作为分类器。该实验结果表明，可以基于图像直方图的统计特征对TB和非TB图像进行分类。

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