Grayscale inhomogeneities in Magnetic Resonance (MR) images can cause some difficulties in automated quantitative image processing and analysis. In order to remove such inhomogeneities in MR images, some researchers have employed various methods. In this study, we propose a novel bias field estimation method based on simplified Pulse Coupled Neural Network (PCNN). MR images pre-processing makes full use of PCNN?s visual characteristics and provides an efficient iterative method for MR images bias field estimation. Finally, we use estimated bias field to reconstruct simulation images. The proposed method has been successfully applied to 3-Tesla MR images with desirable results. Compared to normal images, the proposed method is effective and self-adaptive to some different slice MR images which converges to the optimal solution at a fast rate.
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