Novas propostas em filtragem de projeções tomográficas sob ruído Poisson

Abstract

In this dissertation we present techniques for filtering of tomographic projections with Poisson noise. For the filtering of the tomogram projections we use variations of three filtering techniques: Bayesian estimation, Wiener filtering and thresholding in Wavelet domain. We used ten MAP estimators, each estimator with a diferent probability density as prior information. An adaptive windowing was used to calculate the local estimates. A hypothesis test was used to select the best probability density to each projection. We used the Pointwise Wiener filter and FIR Wiener Filter, in both cases we used a adaptive scheme for the filtering. For thresholding in wavelet domain, we tested the performance of four families basis of wavelet functions and four techniques for obtaining thresholds. The experiments were done with the phantom of Shepp and Logan and five set of projections of phantoms captured by a CT scanner developed by CNPDIA-EMBRAPA. The image reconstruction was made with the parallel POCS algorithm. The evaluation of the filtering was made after reconstruction with the following criteria for measurement of error: ISNR, PSNR, SSIM and IDIV.