Ultrasound image segmentation method and system

Abstract

The invention is suitable for the technical field of image processing, and particularly relates to an ultrasound image segmentation method and system. The method includes the following steps that coarse registration is conducted on a statistical shape model and collected three-dimensional ultrasound data of a specified organ to obtain initialized coordinate transformation parameters; according to the initialized coordinate transformation parameters, an image segmentation algorithm based on particle filter is used for conducting iteration segmentation on the three-dimensional ultrasound data, wherein the statistical shape model is a combination of an average value obtained by training manual segmentation results of a plurality of high-definition three-dimensional data and a group of feature vectors of a representation change mode. Therefore, the problem that more artificial participation is needed in manual segmentation and semi-automatic segmentation is solved. Compared with an existing full-automatic segmentation method, the ultrasound image segmentation method and system solve the problems of low image resolution and segmentation accuracy under the image fuzzy condition.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an ultrasonic image segmentation method and system. Background technique [0002] Image segmentation technology is of great significance in image navigation, disease diagnosis and many other aspects. In image navigation, only more accurate image segmentation result data can provide a good three-dimensional visualization environment for the navigation process, and the subsequent path planning functions are also based on good segmentation results. [0003] In disease diagnosis, accurate segmentation results can allow doctors to better observe the shape of organs, and can provide some characteristic indicators of organs to assist doctors in diagnosis. The current segmentation techniques can be divided into three categories: manual, semi-automatic, and fully automatic. Manual segmentation relies on the doctor's personal experience. It is necessary to manually outline the e...

AI Technical Summary

Problems solved by technology

Although there is no need for a lot of work such as outlining the edges, it relies on initialization and still requires human-computer interaction, which may interfere with the normal surgical process.

The automatic segmentation technology relies entirely on the algorithm to process the image to obtain the segmentation results of the specified organs, but the automatic technology often has problems in accuracy and stability, and it is difficult to achieve a satisfactory segmentation effect in the case of poor image quality.

Images