We introduce the incremental optimization transfer framework to estimate emission means in positron emission tomography. The proposed algorithms accelerate convergence speeds and ensure global convergence (to a stationary point) under mild regularity conditions without inconvenient relaxation parameters. In experiments using synthetic data and real phantom data, it was found that, for a fixed level of background noise, the proposed could provide high quality reconstructed images.