White blood cell image accurate segmentation method and system based on support vector machine

Abstract

The invention discloses a white blood cell image accurate segmentation method and system based on a support vector machine. The method comprises performing nucleus initial positioning and segmenting, performing rough expansion so as to obtain a substantial area labeled graph of cells, and accurately segmenting the cells by using color characteristics and the classifier of the support vector machine. According to the method provided by the invention, on one hand, according to a mankind visual saliency attention mechanism, the sensitivity of human eyes to the change of image edges are simulated, and a nucleus area can be accurately and rapidly segmented by using the clustering of edge-color pairs; and on the other hand, the adopted classifier of the support vector machine has excellent stability and anti-interference performance, and at the same time the space relationship of color information and pixel points are fully utilized so that the training sample sampling mode of the classifier of the support vector machine is improved, thus the accurate segmentation of white blood cells in a cell small image can be realized.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to a method and system for accurate segmentation of white blood cell images based on a support vector machine. Background technique [0002] Automatic blood cell segmentation and recognition technology is one of the hot research directions of image processing technology in recent years. The increase and decrease in the number of white blood cells can be used as the main criterion for judging whether the human body is infected or has inflammation. Therefore, the automatic counting and classification of white blood cells using computer vision technology can assist doctors to achieve rapid analysis and diagnosis. Generally speaking, the automatic leukocyte classification and recognition system includes three main steps: cell segmentation, feature extraction, and classification and recognition. Both feature extraction and classification recognition depend on th...

AI Technical Summary

Problems solved by technology

However, the problem caused by rapid staining is that the staining is uneven and contains red blood cells that have not been completely dissolved

So far, there is no effective automatic segmentation method that can produce better image segmentation results not only for traditional standard stained images, but also for fast stained images.

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