Brain MR Image Segmentation Method Combining Weighted Neighborhood Information and Offset Field Restoration

Abstract

The invention discloses a brain MR image segmentation method combining weighted neighborhood information and biased field restoration. The method comprises the steps that first, anisotropy neighborhood information is built, and is integrated in an FCM model; second, in order to reduce influences of a biased field, biased field information is integrated in an improved model, and a biased field is restored when the model is segmented. According to the method, the biased field is coupled to the model as a multiplicative additional field, and therefore influences of the biased field on segmentation are eliminated; then, a weighted field information field is built, and made to have anisotropy; the anisotropyc weighted field information field is used for replacing gray level information in the traditional FCM, influences of noise are reduced, and meanwhile, information of long and thin topological structures can be well kept. According to the brain MR image segmentation method combining weighted neighborhood information and biased field restoration, no spatial neighborhood information regular term parameters need to be adjusted, and robustness of the model is improved.

Description

technical field [0001] The invention belongs to the technical field of brain MR image segmentation, and in particular relates to a brain MR image segmentation method combining weighted neighborhood information and offset field restoration. Background technique [0002] Brain disease is one of the main diseases that threaten human health. Using brain imaging technology to analyze brain function qualitatively and quantitatively is of great help to the effective diagnosis of brain diseases. In human brain research and clinical disease diagnosis and treatment, medical magnetic resonance imaging (magnetic resonance image, MRI) can provide images with high soft tissue contrast for the anatomical structure of the brain and is less harmful to the human body. The application is more and more extensive and in-depth, and has become the main means for people to study brain function and pathology. Fuzzy clustering technology is widely used in MR image segmentation due to the fuzziness ...

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Problems solved by technology

[0015] Among them, K is the Gaussian kernel function, b is the offset field, and b is added to the model as a multiplicative additional field, so that the CLIC model realizes the coupling of segmentation and estimated offset field, but the CLIC model also has some defects [9] : First, the Gaussian kernel function is used as the weight of the spatial neighborhood. This weight is only related to the spatial distance of the current target pixel point, without considering the image structure information, and because the Gaussian kernel function is isotropic, it results in segmentation boundaries or slender topological structures It is prone to misclassification; secondly, the model cannot effectively remove the influence of noise, because it is still based on the original FCM model, but it is only localized; finally, a large number of kernel convolution calculations lead to greater computational complexity

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