The traditional Gaussian Mixture Model is sensitive to noise in laser medical image processing, and its segmentation accuracy is not high enough. In order to remedy these defects, the Neighborhood Concerning Gaussian Mixture Model is adopted. The gray value of the center pixel in every neighborhood block is updated by the information of the neighborhood pixels according to the close correlation between them. After remolded, the image is decomposed by double Gaussian Mixture Model, which further refines the decomposition of the mixture model and improves the accuracy and anti-noise performance of image segmentation. Finally, experiments are carried out to verify the feasibility of the method. The results of the experiments show that the method of image segmentation based on the Neighborhood Concerning Gaussian Mixture Model can significantly improve the noise suppression ability, and achieve a good segmentation effect with high efficiency, while retaining the image contour and details better.