Method for automatically segmenting human lateral geniculate nucleus (LGN) by using prior knowledge

Abstract

The invention discloses a method for automatically segmenting human lateral geniculate nucleus (LGN) by using prior knowledge. The method comprises the steps: firstly, carrying out bias field correction on data of a structure image of a human brain, and then, carrying out brain substructure segmentation on the corrected image, so as to obtain a template of a LGN; then, registering the corrected structure image data and the template of the LGN to a MNI (Montreal Neurological Institute) standard space; acquiring the region boundary of the LGN in the MNI space according to prior knowledge of an LGN anatomical structure, and then, segmenting the LGN in the region respectively by a region-growing method, a k-means method, an Otsu algorithm and an image segmentation method; then, fusing the segmented results, so as to obtain an estimated value of the LGN region, which serves as a segmented result; finally, transforming the segmented result into an original space of the structure image, thereby obtaining a final LGN segmented result.

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...

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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 segmentation results of several different methods to calculate the probability estimation value of the real segmentation of the target object , and use this estimated value as the segmentation result of the target object

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