Parasagittal Meningioma Brain Tumor Classification System Based on Mri Images and Multi Phase Level Set Formulation

The most common type of brain tumor known as Meningioma arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, we propose Meningioma brain tumor classification system using MRI image is developed . Firstly, based on the characteristics of MRI image and Chan-Vese model, we use multiphase level set method to get the interesting region. Therefore, we obtain two matrixes, in which one contains the whole cell's boundary, and the other contains the boundary of some cells. Secondly, with respect to the cells' boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. At last, we consider every discrete region according to the tumor's features to judge whether a tumor appears in the image or not. Our method has a desirable performance in the presence of common tumors. For some non-convex tumors, we utilized a traditional way (SVM and LBP) as a second processing, which increased the coverage and accuracy. Experiments show that our method has a high coverage without any learning-based classifiers for most common tumors, which saves a lot time and reduces a lot workload. Therefore, the proposed method has a good practical application for assisting physicians in detecting Meningiom tumors using MRI images.

Research on the Accuracy of Mask Projection Stereo-Lithography

In this research, sub-region curing and mask DPI modification are used to improve the accuracy of MPSL. Because the curing shrinkage at the discrete region does not cause the shrinkage of the entire resin surface, the accuracy of the RP work piece under sub-regional MPSL can be improved effectively. For the optical system error, the accuracy can be improved through the DPI (Dot per Inch) modification. The research results show that accuracy of work piece less than 12mm can be improved through DPI (Dot per Inch) modification. For the work piece above 16mm, the main method which improves the accuracy can adapt sub-region curing procedure. The combination methods of sub-regional exposure and exposure mask DPI modification can significantly improve the dimensional accuracy of parts, and the overall size of the error of parts can be controlled less than 1%.