Method for acquiring vocal organ profile in medical image

Abstract

The invention provides a method for acquiring a vocal organ profile in a medical image. The method comprises the following steps of: for the medical image, performing binaryzation on the lip and a background area in the medical image by using an automatic segmentation threshold of the lip and the background so as to acquire a lip profile; extracting an upper tooth profile, a lower tooth profile, an upper jaw profile and a lower jaw profile in a face range included in the lip profile; for an image area between the upper jaw profile and the lower jaw profile, acquiring a reliable edge point of a tongue profile; and fitting a tongue edge profile by the reliable edge point of the tongue profile. By the method, the vocal head part and the organ area can be automatically segmented from the image background, and the whole process is automatically completed without manual interaction.

Description

technical field [0001] The invention relates to the technical field of image processing in the information technology industry, in particular to a method for acquiring the outline of a vocal organ in a medical image. Background technique [0002] At present, the physiological mechanism of human pronunciation is still unclear. One of the important reasons is the lack of effective observation and analysis tools for the movement relationship between speech and organs. At present, the commonly used auxiliary methods for the study of dynamic images of oral pronunciation include X-ray images, ultrasound images, and MRI images. These medical images are characterized by low resolution, blurred imaging, and severe noise. Although researchers at home and abroad have proposed many methods for automatically extracting and tracking the motion contours of human organs from medical images, due to the variety of human vocal organs, flexible movement, and serious mutual occlusion in video i...

AI Technical Summary

Problems solved by technology

These medical images are characterized by low resolution, blurred imaging, and severe noise

Although researchers at home and abroad have proposed many methods for automatically extracting and tracking the motion contours of human organs from medical images, due to the variety of human vocal organs, flexible movement, and serious mutual occlusion in video images, there will be False contours with wide amplitude, etc.

[0003] The applicant found that traditional image analysis techniques are not suitable for the automatic acquisition of the contours of vocal organs in low-quality medical images. Automatically obtaining the contours of vocal organs from low-quality medical images is still a challenge in speech and visual computing, especially for Some Tongue Movement Details in Pronunciation

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