Urinary erythrocyte multifocal video acquisition method and system

Abstract

The invention belongs to the technical field of artificial intelligence assisted medical treatment, and in particular relates to a method and a system for collecting urine red blood cell multi-focal length video. The method includes the following steps: S1, continuously adjusting the focal length of the microscope, and collecting a microscope zoom video of the sample; S2, performing sharpness calculation on each frame image in the zoom video, and the sharpness calculation is calculated by using an improved image sharpness evaluation function of DCT. ; S3, according to the calculated sharpness value, draw the sharpness change curve of the zoom video; in the sharpness value change curve, the image focal length corresponding to the maximum slope of the curve is taken as the starting point focal length of the collected video, and the corresponding image focal length when the curve slope is the smallest. The focal length of the image is used as the focal length of the end point of the acquisition video, and the zoom video of the microscope is acquired and saved. The invention uses the evaluation function of the image to perform multi-focal length video acquisition, which improves the acquisition accuracy and efficiency.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence assisted medical treatment, and in particular relates to a method and a system for collecting urine erythrocyte multifocal length video. Background technique [0002] The clinical manifestations of patients with nephropathy usually include changes in urine. Generally, in the urine samples of patients with nephropathy, in addition to the normal urine red blood cells, there are a large number of glomeruli and other tissues squeezed. Abnormal urine red blood cells. Doctors usually diagnose whether a patient has kidney disease by collecting and observing a sample of the patient's urine. Among them, some abnormal urinary erythrocytes will show different morphological features under different focal lengths of the microscope, and may even appear morphological features similar to other types of abnormal urinary erythrocytes under a certain focal length. For the identification of such abn...

AI Technical Summary

Problems solved by technology

For the identification of such abnormal urine red blood cells, if the microscope focal length is not suitable for observation, it is very easy to misidentify such abnormal urine red blood cells

In addition, the morphological characteristics of some abnormal urinary red blood cells are also very similar to each other, which is very easy to be confused when identifying

[0003] At present, there are published invention patents that propose a classification and statistics method and system for abnormal urine red blood cells, and propose the concept of multi-focus video data of urine red blood cells under a microscope, but the video data used are manually collected

Manual data collection usually has many uncontrollable factors, such as changes in the external contrast environment when collecting data, camera exposure adjustment, etc.

In the process of collecting multi-focal length video data, the more important problem is that it is difficult to judge the focal length corresponding to the correct abnormal urine red blood cell characteristics when collecting data manually, and the judgment standard is subjective, and it is impossible to obtain a unified standard. High-resolution multi-focal video data for accurate characterization of abnormal urine red blood cells

[0004] In the prior art, there is no video data acquisition equipment and system that can automatically analyze and collect multi-focus videos of urine red blood cells under a microscope

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