This article analyzes the performance of the Electrical Impedance Tomography (EIT) technique in the diagnosis of breast cancer. The simulations used EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software) and OCTAVE, a computational tool for numerical calculation. We compared three algorithms: prior Laplace, NOSER and Tikhonov. The NOSER algorithm obtained the best classification according to performance metrics of proximity between neoplasms, where this factor changes the resolution of the reconstruction images generated depending on the chosen algorithm.

Maximum entropy (MENT) is a well-known image reconstruction algorithm. If only a small amount of acquisition data is available, this algorithm converges to a noisy and blurry image. We propose an improvement to this algorithm that consists on applying alternately the MENT reconstruction and the robust anisotropic diffusion (RAD). We have tested this idea for the reconstruction from full-angle parallel acquisition data, but the idea can be applied to any data acquisition scenario. The new technique has yielded surprisingly clear images with sharp edges even using extremely small amount of projection data.

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AVO (Amplitude Vs Offset) seismic inversion isa technique of tomographic seismic imaging for creating a model in stack-velocity space that can correctly reconstruct the measured AVO seismic dataset. This is usually implemented by minimizing a least squares inversion algorithm. This algorithm has limitations because it reconstructs seismic images with artifacts yield by impulsive noise contained in the input raw seismic dataset. Recently, superior seismic images were reconstructed using a MAP (Maximum A Posterior) approach, based on the Norm Lp. In this paper, we demonstrate similar results and even superior ones via minimizing a MAP approach built through L2 norm of dataset misfit and a non-L2 Lorentzian error norm of the model energy.

"Um método de estimação de sequências espaço-temporais de dados codificados usando a máquina de suporte vetorial: Aplicações na área de segurança da informação " "A method of estimating space-temporary sequences of coded data using the vector support machine: Applications in the information security area"