摘要翻译：
电阻抗断层成像(EIT)是一种功能成像方法，正在开发用于重症医学的床边应用。为了提高EIT图像的胸部解剖学分辨率，基于EIT的高时间分辨率，结合肺灌注和通气信号所包含的功能信息，建立了一个模糊模型。在正常通气和呼吸暂停时收集实验动物模型的EIT数据，并以注射高渗盐水作为参考。该模糊模型分为三个部分：心脏模型、通气图像肺图和灌注图像肺图。采用阈值法进行图像分割，生成通气/灌注图。将模糊模型处理的EIT图像与高渗盐水注射法和CT扫描图像进行比较，在定性（模型获得的图像与CT扫描图像非常相似）和定量（ROC曲线提供的面积等于0.93）方面均有较好的效果。毫无疑问，这些结果代表了EIT图像领域的重要一步，因为它们打开了开发基于EIT的床旁临床方法的可能性，而这些方法目前还没有。这些研究成果为开发重症医学中常见的危及生命疾病的EIT诊断系统奠定了基础。
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英文标题：
《Fuzzy Modeling of Electrical Impedance Tomography Image of the Lungs》
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作者：
Harki Tanaka, Neli Regina Siqueira Ortega, Mauricio Stanzione Galizia,
  Joao Batista Borges Sobrinho, and Marcelo Britto Passos Amato
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Electrical Impedance Tomography (EIT) is a functional imaging method that is being developed for bedside use in critical care medicine. Aiming at improving the chest anatomical resolution of EIT images we developed a fuzzy model based on EIT high temporal resolution and the functional information contained in the pulmonary perfusion and ventilation signals. EIT data from an experimental animal model were collected during normal ventilation and apnea while an injection of hypertonic saline was used as a reference . The fuzzy model was elaborated in three parts: a modeling of the heart, a pulmonary map from ventilation images and, a pulmonary map from perfusion images. Image segmentation was performed using a threshold method and a ventilation/perfusion map was generated. EIT images treated by the fuzzy model were compared with the hypertonic saline injection method and CT-scan images, presenting good results in both qualitative (the image obtained by the model was very similar to that of the CT-scan) and quantitative (the ROC curve provided an area equal to 0.93) point of view. Undoubtedly, these results represent an important step in the EIT images area, since they open the possibility of developing EIT-based bedside clinical methods, which are not available nowadays. These achievements could serve as the base to develop EIT diagnosis system for some life-threatening diseases commonly found in critical care medicine.
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PDF链接：
https://arxiv.org/pdf/0710.3185