For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.
Algorithms ; Artificial Intelligence ; Humans ; Image Enhancement ; methods ; Image Interpretation, Computer-Assisted ; methods ; Imaging, Three-Dimensional ; methods ; Magnetic Resonance Imaging ; methods ; Meningeal Neoplasms ; diagnosis ; pathology ; Meningioma ; diagnosis ; pathology ; Pattern Recognition, Automated ; methods

This article summarizes the strategy and effects of preventing and controlling the epidemic in the evacuation support of the aero medical evacuation team of the 7th peacekeeping medical contingent of China to Mali， to actively respond to the coronavirus disease-19 （COVID-19 ）epidemic based on existing medical conditions and further provide scientific evidence for guaranteeing military medical service in public health emergencies.