Partition method for interactive three-dimensional body partition sequence image

Abstract

The invention relates to a segmentation method of an interactive three-dimensional segmentation sequence image and an application thereof. The segmentation method is the relative fuzzy connectivity segmentation method which is based on three-dimensional voxel and confidence interval, and a similarity region is constituted by gathering pixels with certain specific similar characteristic. The selection of seed points is carried out on the three-dimensional image, thereby being capable of accurately judging whether the seed points belong to the points of a target segmentation object or not. The segmentation method does not need the excessive manual participation, the implementation speed is fast, the result can be obtained within a relatively short period of time, and the parameters do not need to be determined according to the experience. The application of the segmentation method of the interactive three-dimensional segmentation sequence image is used for the segmentation of a liver sequence image. The invention combines the spatial voxel and the similarity among the pixels of the CT sequence image based on the analysis of the characteristics of the abdominal liver CT image and uses the relative fuzzy connectivity method which is based on the three-dimensional voxel and the confidence interval to precisely extract the liver, thereby providing accurate data for the follow-up liver three-dimensional reconstruction.

Description

technical field [0001] The invention relates to a segmentation method of an interactive three-dimensional volume segmentation sequence image and its application, and belongs to the transformation technology of the segmentation method of an interactive three-dimensional volume segmentation sequence image and its application. Background technique [0002] Image segmentation is one of the key steps in 3D reconstruction of CT tomographic data. Separate different areas in the image that have special meanings that do not cross each other, so that each area meets the consistency of a specific area. Image segmentation is of special significance in medical applications. Medical image segmentation is the basis for lesion region extraction, specific tissue measurement, and 3D reconstruction. With the successful application of imaging medicine in clinical medicine, image segmentation is playing an increasingly important role in imaging medicine. Due to the complex anatomical structure...

AI Technical Summary

Problems solved by technology

[0020] The disadvantages of the above method are: 1. The selection of the seed point is carried out on the two-dimensional image, and it cannot be accurately judged whether it belongs to the point of the target segmentation object; 2. Too much manual participation; It takes a long time to get results; 4. Parameter determination needs to be determined based on experience

[0022] The disadvantage of the above method is: the selection of the seed point of the algorithm is also carried out on the two-dimensional image, so the incorrect selection of the seed point will easily lead to insufficient accuracy of the segmentation result when it is applied to the sequence image segmentation of multiple targets in a complex background. Mis-segmentation occurs, and the running speed is relatively slow, requiring users to wait for a long time

At the same time, there are also many parameters that need to be set based on experience, and the amount of user participation is too large

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