Five classification method of blood cells based on laser sheath flow technology

Abstract

The invention discloses a method for five-classification of blood cells based on laser sheath flow technology, comprising the following steps: A. collecting at least 4 optical signals of each cell or particle, and characterizing it as each optical signal of each cell or particle Intensity-related four-dimensional initial vector; B. Initial clustering of four types of cell collections by K-means clustering method with weighted Euclidean distance; C. Sorting each cluster to obtain four clusters of W1, W2, W3 and W4, Delete the W1 cluster; D, transform the centroids of the two clusters W2 and W4 through spatial coordinate transformation to obtain the rotation matrix and rotation vector used to generate the feature vectors used for classification calculations; E, use the rotation vector or rotation matrix to convert the rotation matrix of each cell The initial vector is converted into a feature vector, and the statistical characteristics of the cell feature vector in the corresponding space coordinates are calculated and extracted, and the cells are classified into five categories through a decision tree. This method does not need to use special reagents, and has high calculation efficiency and more accurate classification results.

Description

technical field [0001] The invention relates to a blood cell classification method, in particular to a blood cell five-classification method based on laser sheath flow technology. Background technique [0002] Blood analyzers based on flow cytometry identify cells contained in a suspension wrapped in sheath fluid to classify them into different categories by collecting and analyzing two-dimensional or more than two-dimensional characteristics of cells. In the hematology analyzer based on flow cytometry, the sheath flow wraps the cell suspension and passes through an illumination area at a constant speed. In the illumination area, the cells are irradiated by laser one by one and processed by the polarized light generation channel to generate different optical signals. , such as forward transmitted light, small angle scattered light, large angle scattered light and large angle depolarized polarized light, etc. Condenser lenses from various channels converge these optical sign...

AI Technical Summary

Problems solved by technology

The second method is an improvement to the first method. Compared with the first method, it takes into account the differences and trend characteristics of the distribution of cell characteristics in different samples, and has better classification accuracy. The mutual interference of eigenvalues ​​with insignificant differences in various types of cells makes it difficult to find the best classification boundary threshold, so the accuracy and practicability for clinical testing need to be further improved

This method needs to use special reagents in the detection process, and the problem of high cost for users

Images