The Research Of Algorithm Skull Segmentation In MR Images

In order to obtain valuable information from brain MR imaging, the first step of image processing is image segmentation. Researchers, who focused on the brain extraction and tissue segmentation in the past few years, proposed many related algorithms, such as BET, BEAST, LABEL, and so on, which increases the accuracy of brain extraction. However, not many algorithms on segmenting skull and scalp have been brought up due to few researchers concerning this problem. It is a challenge to propose an efficient skull segmentation algorithm. In one hand, the image quality of skull region in the MR image is poor. In other hand, it is difficult to distinguish skull from its neighborhood tissues because of similar intensity. In fact, skull segmentation in the MR imaging has been applied in various fields.The source imaging problem of EEG and MEG is a typical example. In the problem of constructing the forward matrix, using standard sphere model instead of individual brain model reduces the accuracy of source imaging because of the unique conductivity distribution of individual brain. To make the subsequent calculations simplified and feasible, it is required that the skull should be segmented as a closed part. However, recent existing skull segmentation algorithms of MR imaging cannot handle this request. So the first proposed skull segmentation algorithm focuses on generating a closed skull model to fit the subsequent relevant calculation steps. The frame of the deformable method is inspired from the brain extraction method proposed by Smith in 2002. Through changing the positions of the vertexes, the surface of the contour profile moves into interest area. Next, we compare the accuracy of segmentation of our method with the classic skull segmentation methods. The result shows that our method have a better performance in the problem of skull segmentation.To obtain the accurate attenuation correction of the PET-MRI all-in-one machine, it is vital to segment the exact skull part from the MR imaging. However, some related skull segmentation algorithms, like the one based on morphology proposed in 2005, are not robust to the images of MR. This article creates a new segmentation algorithm originated from the label fusion and the classification methods, which is of better robustness and higher segmentation accuracy. The algorithm of label fusion and classification is based on machine learning. Since the intensity of skull is uncertain in MR images, the method by using classification merely is hard to acquire the ideal results. In this paper, a new method, which set the label fusion as the prior information, was proposed. Our method receives a better performance in comparison with other two methods.First chapter had shown the background and the meaning of the research topic. In the second chapter, the classic skull segmentation algorithm was introduced. In the third chapter and fifth chapter, we proposed the new skull segmentation methods in detail. And in the fourth chapter, the principle of the feature selection method by using the discrete particle swarm optimization is explained. The last chapter has shown the conclusion of the paper.