The Study Of Brain Tissue Segmentation Methods From MR Images With ITK

ObjectiveTo segment brain tissue from MR Images semi-automaticly and automaticly respectively with ITK. Then to observe and study the segmented brain tissue(whitematter, graymatter, ventricle)images so as to analyze the advantages and disadvantages of those segmented brain tissue images that will be made preparation for the 3D visualization and surgery navigation of MR brain tissue.Material and MethodsTo segment 41 sheets of MR brain image with DICOM format in the ITK and receive corresponding brain tissue(whitematter, graymatter, ventricle) images.Semi-automatic segmentation adopts connected threshold region growing algorithm, while automatic segmentation adopts K-Means clustering algorithm.ResultsBecause of the diversity of connectivity among pixels of different brain tissue in the different slices, connected threshold region growing algorithm segments specific MR Images with 3 sheets of images including leftwhitematter, rightwhitematter and graymatter or 3 sheets of images including whitematter, graymatter and ventricle or 4 sheets of images including leftventricle, rightventricle, whitematter and graymatter. Meanwhile, under the influence of initial means, clustering criterion functions and semblance metric methods, K-Means clustering algorithm segments whitematter, graymatter and ventricle on one sheet of image. Both algorithms obtain high quality of corresponding brain tissue image. On the aspect of overall segmentation speed, region growing algorithm is slower than K-Means clustering algorithm: region growing algorithm receives one sheet of one brain tissue image after one segmentation, while K-Means clustering algorithm receives one sheet of all brain tissue image after one segmentation. Meanwhile, on the aspect of segmentation precision and details, region growing algorithm is better than K-Means 4 clustering while K-Means 5 clustering is better than region growing algorithm. The fast effective segmentation mode of K-Means clustering algorithm which receives one sheet of all brain tissue image after one segmentation is the orientation of image segmentation in the future.ConclusionBecause of the different strong points, advantages and disadvantages of different segmentation algorithms, otherness, complexity and multimodality of medical images and diversity, materiality and particularity of the aims and requests of medical image segmentation, concrete issue should be analyzed concretely. Region growing algorithm and K-Means clustering algorithm segment MR brain images and receive different high quality brain tissue images that can be applied to the 3D visualization and surgery navigation of MR brain tissue.