Leukocyte extraction and classification method based on improved K-means and convolutional neural network

Abstract

The invention relates to a leukocyte extraction and classification method based on an improved K-means and a convolutional neural network. The method comprises the steps of firstly, selecting an initial clustering center according to cell image gray level distribution, and clustering all pixels of an image initially according to the principle of proximity; then, improving the Euclidean distance ofthe FWSA-KM algorithm; before the extraction of leukocytes, carrying out the color space decomposition firstly, and carrying out the cell nucleus and cytoplasm extraction by adopting a color component beneficial to leukocyte segmentation and an improved K-means algorithm; separating a complex adhesion part by adopting a watershed algorithm; and finally, performing classification based on the convolutional neural network. According to the method, the leukocyte nucleus segmentation precision and the cytoplasm segmentation precision are 95.81% and 91.28% respectively, and compared with a traditional segmentation method, the precisions are greatly improved, the classification accuracy can reach 98.96% at most, the classification average time is 0.39 s, and compared with an existing leukocyteclassification algorithm, the CNN classification method not only has obvious advantages, but also has the great improvement space.

Description

technical field [0001] The invention relates to the technical field of medical image segmentation and extraction, in particular to a white blood cell extraction and classification method based on improved K-means and convolutional neural network. Background technique [0002] In medicine, white blood cells are an important part of the human immune system, responsible for identifying and engulfing abnormal cells. The traditional differential counting and morphological analysis of white blood cells in blood routine examination rely on manual counting and expert analysis of blood tests, which is inefficient and highly subjective. Currently, flow cytometry is commonly used, which cannot automatically classify white blood cells, and has limitations in clinical application. [0003] In recent years, in order to better segment images and identify white blood cells, researchers have successively proposed some algorithms with better effects to realize accurate segmentation and class...

AI Technical Summary

Problems solved by technology

The traditional differential counting and morphological analysis of white blood cells in blood routine examination rely on manual counting and expert analysis of blood tests, which is inefficient and highly subjective

Currently, flow cytometry is commonly used, which cannot automatically classify white blood cells, and has limitations in clinical applications.

[0003]In recent years, in order to better segment images and identify white blood cells, researchers have successively proposed some algorithms with better effects to achieve accurate segmentation and classification of white blood cells. However, there are still problems in white blood cell segmentation. These problems are mainly caused by the different brightness of the image color, impurities in the image, various shapes of white blood cells, and similar colors of cytoplasm and red blood cells after staining.

The segmentation accuracy achieved by the existing methods cannot meet the actual clinical needs, so there is still a lot of work to be done in the field of white blood cell segmentation

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