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Due to the advantages of non-radiant, non-invasiveness, low cost, and rapid response, electrical tomography (ET) has been investigated extensively in the past decades, and has great potential in the field of process industries and biomedicine. In ET, an array of electrodes is attached around the object, alternating currents/voltages are injected via these electrodes, and the resulting voltages/capacitances are measured. Using measurements on the boundary, an approximation for the spatial distribution of conductivity or permittivity within the object can be reconstructed. Electrical Resistance Tomography (ERT) and Electrical Capacitance Tomography (ECT), applied in the process industrial area, detect the conductivity and permittivity within the object respectively, while Electrical Impedance Tomography (EIT), applied in biomedical area, detects both resistance and capacitance.How to exact the useful information from the data of ET is an important element of the research project. In this thesis, simulation images of ET are studied with Statistical Parameter Mapping. Main works are as follows:Firstly, the model of cylinder is built using COMSOL, the conductivity of a small cylinder within the model varies with time, sectional images are obtained by three-dimensional EIT simulation.Secondly, the principle of Statistical Parameter Map (SPM) is explored, which is a method of data analysis for brain functional imaging. The SPM5 software package is adopted to analyze three sets of three- dimensional simulation images, such as the noise-free, white noise of 70 dB added to the signal, and white noise of 80 dB added to the signal, preliminary statistical results are obtained.Thirdly, based on the idea of image fusion in SPM, a kind of k-means clustering algorithm, with self-determined optimal number of clustering, is adopted to split the ERT and ECT and ERT/ECT dual-modal images.At the end of thesis, the author presents some suggestions for SPM.
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