Automatic parameter selection of image reconstruction algorithms for planar array capacitive imaging

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Improvements in reconstruction algorithms for electrical impedance tomography of brain function

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Automatic parameter selection of image reconstruction algorithms for planar array capacitive imaging

Landmines are often be made out of plastic with almost no metallic components which makes detection difficult. A plausible solution is to detect superficial buried plastic objects using planar array electrical capacitance tomography (ECT). Distance detection is a big limiting factor of planar array ECT. Given the ill-posedness and loss of sensitivity with depth, regularization, and optimal selection of reconstruction parameters are required for detection. In this paper, we propose an 'automatic parameter selection' (APS) method for image reconstruction algorithms that selects optimal parameters based on the input data based on a three step process. The aim of the first two steps is to provide an approximate estimate of the parameters so that future reconstructions can be performed quickly in step 3. To optimize the reconstruction parameters the APS method uses the following metrics. Front surface distance detection (FSDD) is a method of determining an accurate distance measurement from sensor head to object surface in low resolution image reconstructions using interpolation between voxels and Otsu thresholding. Cross-section reconstruction score (CSRS) is a simple binary image comparison method which calculates a ratio of expected image to reconstructed image. An initial set of capacitance data was taken for an object at various distances and used to train the APS method by finding the best reconstruction parameters for each distance. Then, another set of capacitance data was taken for a new object at different distances than before and reconstructed using the parameters selected by the APS method. The results of this showed that the APS method was able to select unique parameters for each reconstruction which produced accurate FSDDs and consistent CSRSs. This has taken away the need for an expert to manually select parameters for each reconstruction and sped up the process of reconstructions after training. The introduction of FSDD and CSRS is useful as they accurately describe how reconstructions were score and will allow future work to compare results effectively.</p

Total variation regularization with Split Bregman based method in magnetic induction tomography using experimental data

International audienceMagnetic induction tomography (MIT) is an imaging modality with a wide range of potential applications due to its non-contact nature. MIT is a member of the electrical tomography family that faces the most difficult imaging challenges, due to its demanding measurement accuracy requirements and its difficult forward and inverse problems. This paper presents for the first time split Bregman total variation (TV) regularization to solve the MIT inverse problem. Comparative evaluations are presented between proposed TV algorithm and more commonly used Tikhonov regularization method. Tikhonov regularization which is based on the l_2-norm is solved linearly while TV is solved using the Split Bregman formulation, which has been shown to be optimal for l_1-norm regularization. Experimental results are quantified by a number of image quality measurements, which show the superiority of the proposed TV method both on low conductivity and high conductivity MIT data. Significant improvement in MIT imaging results will make the proposed TV method a great candidate for both types of MIT imaging