Method for classifying and identifying visible components of microscopic excrement examination images based on machine vision

Abstract

The invention relates to the technical field of image processing and discloses a method for classifying and identifying visible components of microscopic excrement examination images based on machine vision. Firstly, a level set method is applied to conduct image segmentation on microscopic excrement examination images, then primary classification based on morphological characteristics and verifying identification based on an HOG + VSM are sequentially performed according to shapes and outlines of the visible components obtained through segmentation, and finally high-precision visible component classification and identification results are rapidly obtained for timely consulting of patients. In addition, the method further has the advantages of being high image segmentation precision, high in processing speed, good in user experience and the like and is convenient to apply actually and popularize.

Description

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AI Technical Summary

Benefits of technology

The technology described in this patented describes an improved way to recognize and sort small parts from large ones called stools during examinations or procedures like xray imagery analysis (XI). By performing these techniques at different stages of the procedure, patient care may become quicker and more accurate than previously possible. Additionally, there have also been developed methods to improve the efficiency of identifying specific areas within each part without relying solely upon visual inspection alone. Overall, this innovative approach improves overall performance and convenience for clinicians while reducing their workload.

Problems solved by technology

In summary, current methods used for identifying stools during medical imaging can lead to errors or delays caused by subjectivity issues such as subjectiveness and lack of standardization across multiple facilities. Additionally, conventional automated systems often require significant effort and resources even if they were able to accurately detect all possible variations within specific areas (regions) without requiring experts manually reviewed data.

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