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

Abstract

The invention discloses a five-way classification method of blood cells based on laser sheath flow technology, which includes the following steps: A. Collect at least 4 optical signals of each cell or particle, and characterize them into each optical signal of each cell or particle. Strength-related four-dimensional initial vector; B. Use the K-means clustering method of weighted Euclidean distance to perform the initial clustering of four types of cell collections; C. Sort each cluster and obtain four clusters W1, W2, W3 and W4. Delete the W1 cluster; D. Transform the centroids of the W2 and W4 clusters through spatial coordinates to obtain the rotation matrix and rotation vector used to generate the feature vectors used in classification calculations; E. Use the rotation vector or rotation matrix to transform the The initial vector is converted into a feature vector, the statistical characteristics of the cell feature vector in the corresponding spatial coordinates are calculated and extracted, and the cells are classified into five categories through a decision tree. This method does not require the use of special reagents, 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

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