Classification and recognition method of formed components in stool microscopy images based on machine vision

Abstract

The invention relates to the technical field of image processing, and discloses a machine vision-based method for classifying and identifying formed elements in feces microscopy images. Firstly, the level set method is used to segment the feces microscopic image, and then for the shape contour of the formed components obtained by segmentation, the primary classification based on morphological features and the verification and recognition based on HOG+VSM training classifier are sequentially performed, and finally quickly Obtain high-precision classification and identification results of formed components, so that patients can receive medical treatment in time. In addition, this method also has the advantages of high image segmentation accuracy, fast processing speed and good user experience, which is convenient for practical application and promotion.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a machine vision-based method for classifying and identifying formed components in stool microscopy images. Background technique [0002] As one of the three routine tests in hospitals, stool microscopy plays an important role in clinical testing. It uses a microscope to analyze and judge the pictures of stool samples in order to obtain the pathological changes and causes of the body. According to rough statistics, a large city-level tertiary hospital has an average of about a hundred routine stool tests per day. However, for a long time, the traditional microscope image detection has been counted through manual classification. The inspectors smear the samples on the glass slides and use a microscope to observe the types and quantities of the formed components in the field of view, and perform manual classification. There are many deficiencies in this classification an...

AI Technical Summary

Problems solved by technology

There are many deficiencies in this classification and identification method of formed elements: (1) the inspection workload is large, the efficiency is low, and continuous work is easy to cause wrong identification due to objective factors; It brings great interference to identification and processing; (3) The identification and analysis of samples is easily restricted by visual fatigue, mixed with strong subjective factors and lacks objective standards

[0005] But so far, the research on fully automatic intelligent detection is still in a completely immature stage

The accuracy and speed of classification and recognition of formed components need to be improved

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