Research On Mri Brain Contour Finding By Deformable Model Algorithm

EEG (electroencephalography) and MEG (magnetoencephalongraphy) are noninvasive methods for exploring the neural electrical activities of brain. Brain segmentation is a very important and difficult part in EEG/MEG research, and many researchers have been working on this field.To constructing real head models , the problem of brain contour finding from head MRI images must be solved. Due to the complex composition of the head ,it is difficult to segment brain from the head. The deformable model algorithm is adopted in this research.Deformable models ,also known as 'snakes', have been one of the most active and successful research areas in image segmentation. Deformable models are now extensively used in medical image analysis to locate and depict tissues because of its good boundary integration and features extraction. The idea of deformable models are introduced . The research , development and applications of the deformable models are reviewed . The algorithm is realized to extract brain contour from MRI head images . Some improvement on deformable models is presented . The proposed algorithms have good performances.Some improvement on the GVF snake algorithm is presented to solve the problem of GVF snake's inability to converge to the rough contour of the brain. A variable vector field is designed and the constraint parameters of the GVF field is made adaptable to the target object features. The proposed algorithm is called AGVF snake algorithm.When deformable models are used to extract brain contour from the head MR images, problems associated with initialization and time consuming iteration limit their utility. To overcome these problems, a region based variable expansion force is proposed . During the growing period of the deformable models , external forces vary according to the region information . The new algorithm better the performance of the deformable models .When deformable models are used to extract brain contour from head MRI T2 images ,local minimums largely affect the algorithm's converging to the global minimum. The stochastic disturbance is brought to the deformable models and the simulated annealing isintroduced to the iteration process. A new algorithm named SA snake (simulated annealing snake) is proposed . The algorithm has the ability to search globally.