Efficient segmentation and correction model for brain MR images with level set framework based on basis functions

摘要

With the wide application of MR images to detect disease in human's brain deeply, the shortcomings of the technology are necessarily waiting to be solved. For example, MR images always show serious intensity in homogeneity called the bias field, which may prevent to deduce exact analysis of images. To eliminate the distraction, many methods are proposed. Though experimental results already have stood for the advantages of those methods, there are still lots of problems that cannot be neglected, such as bad segmentation, wrong correction and over-correction which has not attracted much attention yet. Among all those methods, the multiplicative intrinsic component optimization (MICO) model influenced us more. Based on the MICO model and split Bregman method, in this paper, we put forward a new model to segment and correct bias field moderately and simultaneously for MR images. Then, we applied our model to a large quantity of MR images, and gained lots of expected results. For a better observation, we compared our model with the MICO model in both segmentation and bias correction results, it can be seen from the experimental results that our model has performed well for the challenging intensity inhomogeneity problems. Many good characteristics like accuracy, efficiency and robustness also have been exhibited in numerical results and comparisons with the MICO model.