首页> 外文期刊>電子情報通信学会技術研究報告. 画像工学. Image Engineering >PLUG-IN MODULES ON PLUTO FOR IDENTIFYING INFLAMMATORY NODULES FROM LUNG NODULES IN CHEST X-RAY CT IMAGES
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PLUG-IN MODULES ON PLUTO FOR IDENTIFYING INFLAMMATORY NODULES FROM LUNG NODULES IN CHEST X-RAY CT IMAGES

机译:在X射线CT图像中从肺结节中识别炎症结节的冥王星插入模块

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摘要

We introduce an implementation of plug-ins on PLUTO. These plug-ins discriminate inflammatory nodules from other types of nodules in chest X-ray CT images. The PLUTO is a common platform for computer-aided diagnosis systems on Microsoft Windows series and it is easy to add new functions as plug-ins. We coded two plug-ins. One of the them calculates features based on medical knowledge. The other plug-in calculates parameters to classify the type of nodules, and it also classifies nodules into inflammatory nodules and others using SVM. These plug-ins are coded using MIST library which is produced at Nagoya University, Japan. In our previous study, the MIST library was parallelized, so that we can utilize a number of CPUs to calculate features and SVM learning/classifying depending on the amount of computation. Using these plug-ins, it became easy to extract features to discriminate inflammatory nodules from other types of nodules and to change parameters for feature extraction and SVM learning/classifying with GUI interface. The accuracy of the classifying result is 100% with 78 solid nodules which contains 43 inflammatory nodules and 35 other type of nodules.
机译:我们介绍了PLUTO上插件的实现。这些插件在胸部X射线CT图像中将炎症性结节与其他类型的结节区分开。 PLUTO是Microsoft Windows系列上计算机辅助诊断系统的通用平台,很容易添加新功能作为插件。我们编写了两个插件。其中之一基于医学知识来计算特征。另一个插件计算参数以对结节类型进行分类,还使用SVM将结节分类为炎性结节和其他结节。这些插件使用日本名古屋大学生产的MIST库进行编码。在我们之前的研究中,MIST库是并行的,因此我们可以利用许多CPU来计算功能,并根据计算量来支持SVM学习/分类。使用这些插件,提取特征以区分炎症性结节与其他类型的结节并更改参数以进行特征提取和使用GUI界面进行SVM学习/分类变得容易。分类结果的准确度为100%,包含78个实体结节,其中包含43个炎性结节和35个其他类型的结节。

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