Fuzzy Modeling of Electrical Impedance Tomography Image of the Lungs

dc.creatorTanaka, Harki
dc.creatorOrtega, Neli Regina Siqueira
dc.creatorGalizia, Mauricio Stanzione
dc.creatorSobrinho, Joao Batista Borges
dc.creatorAmato, Marcelo Britto Passos
dc.date2007-10-16
dc.date.accessioned2026-07-07T08:36:48Z
dc.date.available2026-07-07T08:36:48Z
dc.descriptionElectrical 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.
dc.description10 pages, 6 figures
dc.identifierhttps://arxiv.org/abs/0710.3185
dc.identifierhttp://arxiv.org/abs/0710.3185
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/140186
dc.subjectArtificial Intelligence
dc.subjectComputer Vision and Pattern Recognition
dc.titleFuzzy Modeling of Electrical Impedance Tomography Image of the Lungs
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