Local Gauss Multiplicative Components (LG-MC) Method for MR Image Segmentation

In magnetic resonance (MR) images quantitative analysis, there are often considerable difficulties due to factors such as intensity inhomogeneities and low contrast. Based on these problems, this paper proposes a model that can simultaneously perform bias field estimation and image segmentation. Our idea is to make use of the property that observed image can be decomposed into multiplicative components. First, the bias field representation is given by a series of smooth basic functions, the required true image is represented as the function of observed image and bias field. Then, the segmentation model of Gaussian probability distribution with different means and variances is constructed by local information. Different from the existing distribution model, our model is constructed based on the local information of the true image, therefore the influence of above mentioned factors is better avoided. A series of image segmentation experiments demonstrate the superiority and effectiveness of our model.