A Brain MR Image Segmentation Method Based on Superpixel Fuzzy Clustering

Abstract

The invention relates to a brain MR image segmentation method based on superpixel fuzzy clustering. The method comprises the following steps: 1) obtaining an MR image; 2) performing superpixel segmentation on the MR image and thus a plurality of atom regions are obtained; 3) performing secondary refinement segmentation on the atom regions whose grey value variance is big; 4) performing fuzzy clustering on the atom regions, so that category membership degree of each atom region is obtained; 5) defining the atom regions, whose membership degrees are not clear enough, as fuzzy blocks and realizing affiliation category judgment for the fuzzy blocks through a function iterative method; and 6) performing superpixel merging operation on the atom regions and thus image segmentation results are obtained. The method is a combination of a superpixel method and a fuzzy c-mean clustering algorithm; advantages of the superpixel method and the fuzzy c-mean clustering algorithm are effectively utilized to targetedly overcome the defect that the fuzzy c-mean clustering algorithm is sensitive to noise and a bias field during pixel level clustering; and compared with the traditional fuzzy c-mean clustering algorithm, the method is higher in segmentation accuracy and robustness.

Description

technical field [0001] The invention relates to the field of medical image segmentation, in particular to a brain MR image segmentation method based on superpixel fuzzy clustering. Background technique [0002] Medical image processing and analysis, with the help of powerful means of graphic image technology, greatly improves the level of medical clinical diagnosis by using existing medical imaging equipment, provides a solid foundation for medical research and development, and has important application value. Nuclear magnetic resonance technology is a non-invasive medical imaging technology. By analyzing MR image sequences, we can obtain high-resolution 3D images with anatomical and functional information, which is conducive to improving the level of diagnosis and treatment of diseases . With the application of statistical theory, fuzzy set theory, and machine learning theory in the field of image segmentation, many new methods and ideas have been applied to the field of m...

AI Technical Summary

Problems solved by technology

This method can make full use of the advantages of fuzzy clustering in medical image processing, and use the superpixel method to strengthen the spatial constraint information and effectively deal with the uneven gray level, making up for the fuzzy c-means clustering algorithm alone. Insufficient in dealing with noise and biased fields, improving the accuracy and robustness of brain MR image segmentation

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