Cell segmentation in microscopic images of mice brain based on Markov Random field theory

The neurons in sectioning microscope images of mice brain are important to biologists. Image segmentation algorithms are widely applied to automatically extract the neurons to facilitate further analysis. A method for cell segmentation in microscopic image of mice brain based on Markov Random Field (MRF) theory is proposed. Firstly, manually labeled images and original images are jointly analyzed to estimate the initial parameters in Gaussian Mixture Model, which significantly reduces the number of iterations and increases the precision of segmentation. Secondly, pixel intensity and distance between pixels are integrated into the conventional Potts model to improve the description of the quantitative relationship between pixels. The experimental results demonstrate that the proposed method improves the accuracy and the efficiency of cell segmentation compared to traditional methods.