Simulation of Intelligent Image Processing Model Based on Machine Learning Algorithm

Medical image segmentation is an important link in medical image processing and analysis, and it is also a very arduous task. In the process of image processing and image analysis, image segmentation is the most basic step, and the accuracy of segmentation directly affects the accuracy of subsequent work, which is the premise of medical image analysis, understanding, description and three-dimensional reconstruction. The topic selected in this paper is the simulation of intelligent image processing model based on machine learning algorithm. A novel MRI (magnetic resonance imaging) image segmentation algorithm is obtained by combining machine learning with fuzzy theory. The spatial filter provides strong noise filtering performance. By guiding the clustering process of FCM (Fuzzy C-means) through the filtered results, we can expect to get stronger anti-noise ability than the standard FCM. The simulation results show that the improved FCM algorithm proposed in this paper has high diagnostic accuracy, high segmentation accuracy, stable algorithm and strong robustness. The improved FCM algorithm improves the anti-noise ability by fusing spatial filter, and this method has achieved good segmentation and anti-noise effect.