Image reconstruction for pet using fuzzy potential

Maximum likelihood (ML) and maximum a-posteriori (MAP) are the most widely used algorithms for emission tomography (ET). MAP-approach heavily depends on Gibbs hyper-parameter for noise free reconstruction. Choosing a correct hyper-parameter is difficult and a time consuming task. Recently, the proposed median root prior (MRP) algorithm is a good alternative, but are prone to step like streaking effect. In this research work, a fuzzy logic based approach is proposed to overcome these shortcomings. Unlike traditional potential function, a fuzzy potential is used for modeling inter pixel interaction. Two basic operations viz. edge detection and fuzzy smoothing are performed sequentially during each iteration. The first operation is employed for the detection of edges (if present) in all the eight directions of a 3 times 3 neighborhood window. The second operation uses this edge information to perform fuzzy smoothing. Due to the recursive nature of the reconstruction algorithm, both these operations are employed iteratively to reduce heavy noise produced due to dimensional instability [E. Veclerov and J. Llacer, 1987]. Simulated experimental results are obtained to show the feasibility of the proposed approach. These algorithms are also compared with other approaches such as, MAP and MRP by numerical measures and visual inspection. Algorithm evaluation shows promising results