Classified statistic method and device of particles

Abstract

The invention discloses a classified statistic method of particles, which is suitable for carrying out classified statistic on particles by a particle detector. The method comprises the following steps: A1. collecting at least two features generated in the detector of each particle; B1. selecting the two features to be represented by a two-dimensional vector and mapping the two-dimensional vectoron a two-dimensional scatter diagram; C1. dividing distribution regions of various particles that need to carry out classified statistic on the scatter diagram; D1. calculating a particle distribution function in each region; and E1. carrying out classified statistic on the particles of all regions according to the particle distribution function of each region. The invention is used for automatically adjusting the size, the shape, the direction and the position of a classified boundary, has higher accuracy and stability and improves the adaptability of the scatter diagraph to an abnormal sample.

Description

【Technical field】 [0001] The invention relates to a particle detector, in particular to a method and device for classifying and counting particles of the particle detector, which are used for classifying and counting various particles. 【Background technique】 [0002] Particle detectors are used to classify and count various particles, such as blood cell analyzers, whose main function is to provide the number of various types of cells in the human body, among which the counting of various types of white blood cells is the basic function. Let the reagent-treated, liquid-coated white blood cells pass through the detection area (flow chamber) one by one, irradiate the laser light on a single cell, and collect refracted or scattered light at different angles (usually forward scattered light FSC, side Scattered light SSC), and then through photoelectric conversion and AD conversion, a set of two-dimensional data corresponding to the cell is obtained, and the two-dimensional data i...

AI Technical Summary

Problems solved by technology

This method also has certain limitations: (1) If the data points on the scatter plot are discontinuous, very discrete, and there are many single points, according to this algorithm, the boundaries around these points will be found and finally classified into a separate category , but in fact these points are not a single category but a large category of cells scattered relatively apart; (2) It is difficult to solve the above problem even if the data is smoothed using sampling grids (bins), and the smooth The more powerful (the more points the bins take), the greater the deviation between the calculated trough and the trough on the original data; (3) The algorithm performs an operation on each data point on the two-dimensional scatter diagram, but the scatter There are not many effective points on the point map, and there are many areas without data

A two-dimensional scatterplot is actually a sparse matrix (see Figure 5 ), if each point is searched, the efficiency of the algorithm will decrease

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