Method for automatically segmenting human lateral geniculate nucleus through prior knowledge

Abstract

The invention discloses a method for automatically segmenting the human lateral geniculate nucleus through prior knowledge. According to the method, field bias correction is performed on structural image data of the human brain, and then minor structures of the brain are segmented through the corrected image so as to obtain a template in the ventral diencephalon region. Next, the corrected structural image data and the template of the ventral diencephalon region are in registration into MNI standard space. The region limit in the MNI space of the LGN is obtained according to the prior knowledge of the anatomic structure of the lateral geniculate nucleus, then in the region, the LGN is segmented out though the region growing method, the k average value method, the Ostu method and the image segmenting method. Then, several segmenting results are fused to obtain an estimated value of the LGN region to serve as a segmenting result. Finally, the segmenting result is changed into original space of a structural image, and it is that the final segmenting result of the LGN is obtained.

Description

technical field [0001] The invention relates to the field of computer image processing, in particular to a medical image segmentation method that uses the anatomical prior knowledge of the lateral geniculate body (LGN) to automatically segment the LGN in a human brain structure image. Background technique [0002] Image segmentation refers to the use of image information to extract the "target object of interest" in the image from the complex scene. The target object of interest is generally called the foreground, and the rest of the image is called the background. Overall, image segmentation methods can be divided into two systems: human-computer interactive segmentation and computer automatic segmentation. The former relies too much on expert experience and takes too long, while the result of the latter's autonomous segmentation is often unsatisfactory. In the field of practical application, people choose suitable methods according to different needs, and so far there is...

AI Technical Summary

Problems solved by technology

[0005] In the field of medical images, manual segmentation of target objects is very time-consuming, but the accuracy of automatic segmentation methods is not high. In order to solve the problem of low accuracy, we use the

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