Iterative image reconstruction for emission tomography using fuzzy potential

The maximum a-posteriori (MAP) and maximum likelihood (ML) algorithm produces good reconstruction for emission tomography. However they still suffer from noise and optimal smoothing. Penalized iterative algorithms based on MAP-estimation often result in over smooth reconstructions. These algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class. Reconstruction with better edge information is often difficult due to the lack of prior knowledge. In this paper, a fuzzy logic based approach is proposed to model the nature of pixel-pixel interaction. The proposed algorithm consists of two elementary steps: (1) Edge detection - fuzzy rule based derivatives are used for the detection of edges in the nearest neighborhood window. (2) Fuzzy smoothing - penalization is performed only for those pixels for which no edge is detected in the nearest neighborhood. Both of these operations are carried out iteratively until convergence. Quantitative analysis shows that the proposed fuzzy rule based reconstruction algorithm is capable of producing better reconstructed images when compared with MAP and MRP reconstructed images.