Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for automatically classifying particles

A technology of automatic classification and clustering method, which is applied in the field of automatic classification of flow cytometer, can solve the problems of particle classification deviation and slow speed, and achieve the effect of reducing calculation amount, improving efficiency and saving time

Active Publication Date: 2012-06-20
DIRUI MEDICAL TECH CO LTD
View PDF6 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The invention provides an automatic particle classification method, which solves the problems of deviation and slow speed in particle classification in the above overlapping areas, automatically adjusts the boundary, size and direction of particles, and improves the adaptability and accuracy of abnormal samples

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for automatically classifying particles
  • Method for automatically classifying particles
  • Method for automatically classifying particles

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0047] One embodiment of the present invention is: the clustering method adopted in the step C1 is a k-means clustering method, and the k-means clustering method includes the following steps:

[0048] C11. The initial center of k-means clustering adopts a centrosymmetric Gaussian kernel. This Gaussian kernel is suitable for data of any dimension. The Gaussian kernel and the effective data of the image are convolved to obtain a smoothing effect map of this data, and then find Each of its peaks is used as the initial center;

[0049] C12. Calculate the distance between each effective cell or particle and the initial center, and find the cell or particle with the smallest distance;

[0050] C13. Combining the cells or particles into the type of its nearest center;

[0051] C14, calculate the center, repeat the above steps C12, C13;

[0052] C15. Until its central position and the previous central position are less than a small value, the clustering ends.

[0053] An embodiment...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a method for automatically classifying particles and belongs to a particle classification method. The method comprises the following steps of: obtaining at least two types of characteristic information through collecting scattered light signals of a particle detector and respectively mapping the at least two types of the characteristic information of the particles into a two-dimensional scatter diagram; carrying out Gaussian filtering on the two-dimensional scatter diagram and selecting a peak value of the Gaussian filtering as an initial value of means clustering; clustering data of the two-dimensional scatter diagram by using the means clustering; and calculating the particle number of each area according to clustered results, so as to count percentage of the particle number of each area. According to the invention, the boundary position, shape, direction and size of each area can be adjusted; and the method provided by the invention is strong in accuracy and stability, and can be used for improving the adaptability of the scatter diagram to various samples.

Description

technical field [0001] The invention relates to a particle classification method, and is especially suitable for an automatic classification method of a flow cytometer. Background technique [0002] Flow cytometry and blood analyzers, urine analyzers, and particle analyzers based on flow cytometry technology identify different particles in liquids by collecting or analyzing two-dimensional or multi-dimensional data of particles to separate them. into different categories. Such as figure 1 As shown, in the flow cytometer, the cell or particle suspension is wrapped in the sheath fluid and passes through the illumination area one by one. Backscatter signal, and multiplex fluorescence signal. Large-angle signals reflect cell complexity information, and small-angle signals reflect cell volume information. The analysis system generates a two-dimensional or three-dimensional data scatter diagram of these signals collected by the detector, divides multiple regions on the scatter...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G01N15/10G06F17/30
Inventor 宋洁朱海波孙媛媛丁立明
Owner DIRUI MEDICAL TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products