Block iterative methods for Bayesian segmentation of positron emission tomography images

Direct Bayesian segmentation of PET emission images gives more satisfactory results compared to the segmentation methods relying only on reconstructions by CBP or deterministic iterative methods, when the data is sparse and noisy. The objective function for Bayesian segmentation is nonconvex and nondifferentiable. Therefore, gradient based techniques cannot be used. A Gauss-Seidel type method has been proposed before. In this paper, we propose a block-iterative approach which finds a higher maximum of the likelihood function than other techniques with local search strategy