Local Gauss multiplicative components method for brain magnetic resonance image segmentation

Background and Objectives: In magnetic resonance (MR) images' quantitative analysis, there are often considerable difficulties due to factors, such as intensity inhomogeneities and low contrast. Here, we construct a new image segmentation method to solve the MR image segmentation problem caused by internal and external factors. Materials and Methods: We downloaded a series of MR images as research objects through the BrainWeb (http://www.bic.mni.mcgill.ca/brainweb/). There is low contrast information between different components in these images. In addition, we randomly added a certain degree of bias field information to the images. We proposed 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. Results: Qualitative experiments (intensity inhomogeneity images) show that our model achieves satisfactory segmentation results with very few (<10) iterations for severe intensity inhomogeneities image segmentation, while quantitative experiments (20 brain MR images) show that the proposed model can achieve higher accuracy in segmentation. Conclusions: Different from the existing model, our model is constructed based on the local information of the true image, and the influence of above-mentioned factors is better avoided and obtain satisfactory results.