Abstract
Computed tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behavior) with the help of limited set of its projections at different angles. To achieve this goal, one of the most commonly iterative reconstruction algorithm called as maximum likelihood expectation maximization (MLEM) is used. The conventional maximum likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from the optimal smoothing as the number of iteration increases. This paper presents a novel statistical image reconstruction algorithm for CT, which utilizes a fuzzy nonlinear complex diffusion as a regularization term for noise reduction and edge preservation. A proposed model has been evaluated on two simulates test case phantoms. Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography when compared with the state-of-the-art techniques.
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