KNN (K-Nearest Neighbor) sorting algorithm based method for correcting and segmenting grayscale nonuniformity of MR (Magnetic Resonance) image

Abstract

The invention relates to a KNN (K-Nearest Neighbor) sorting algorithm based method for correcting and segmenting the grayscale nonuniformity of an MR (Magnetic Resonance) image, belonging to the field of image processing. The method comprises the following steps of: firstly constructing a grayscale nonuniform field model by utilizing surface fitting knowledge and using a group of orthonormalization basis functions, and establishing energy functions; and then solving model parameters according to an energy function minimization principle to realize grayscale nonuniformity correction and image segmentation, wherein subordinate functions are solved by adopting an iterative algorithm and the KNN algorithm in the model parameter solving process, therefore a partial volume effect is greatly reduced while a grayscale nonuniform field is eliminated, and the influence of noises on the correction and the segmentation of the grayscale nonuniformity of the MR image is reduced. The subordinate functions are solved with KNN through the following steps of: firstly acquiring an accurate smooth normalization histogram by using a kernel estimation algorithm; then respectively solving a threshold value TCG between cerebrospinal fluids and gray matters and a threshold value TGW between the gray matters and white matters by using a maximum between-cluster variance method; carrying out rough sorting on the KNN sorting algorithm by utilizing the two threshold values; and finally accurately sorting points to be fixed by adopting the traditional KNN sorting algorithm.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a gray scale non-uniformity correction algorithm of MR images. Background technique [0002] In order to improve the quality of magnetic resonance (MR) images and the accuracy of image segmentation and registration, it is necessary to correct the gray level inhomogeneity of magnetic resonance images before processing. The traditional correction methods mainly include: [0003] (1) Filter-based method: Because the gray-scale inhomogeneous field changes slowly in the entire image area, the gray-scale inhomogeneous field spectrum can be classified as a low frequency spectrum. Therefore, a low-pass filter can be used to separate the non-uniform gray field from the real image. But in the magnetic resonance image, the spectrum of the real image and the gray inhomogeneous field overlap, so the effect of this method is limited. [0004] (2) Method based on statistical informatio...

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Problems solved by technology

For example, based on EM (expectation-maximization, expectation maximization) gray-level non-uniformity correction and segmentation method, this method has high requirements on the initial value, and needs to manually select the feature points of each tissue as the initial value, and the segmentation result is affected by the initial value. Larger; based on the FCM (Fuzzy c-means clustering, fuzzy C-means clustering) gray-scale inhomogeneity correction and segmentation method, this method introduces a smoothing item to ensure the smoothness of the gray-scale inhomogeneous field, and the segmentation results are better , but the algorithm is complex and the amount of calculation is large

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