Image-based white blood cell counting method and system

Abstract

The invention relates to an image-based white blood cell counting method and system. The method includes the steps of collecting a blood sample image, removing image noise, conducting coarse segmentation on a cell region, obtaining the number of adherent cells, and obtaining the total number of white blood cells. According to the method, the segmentation accuracy of the white blood cells is higherby adopting a white blood cell automatic segmentation technique of an adaptive fuzzy clustering technology, the adherent cells with unapparent regions and chain code features can be quickly identified based on a three-dimensional surface adherent cell localization segmentation method, and the localization, counting and segmentation of the adherent cells are more accurate, so that the measurementof the number of the white blood cells is more accurate.

Description

technical field [0001] The invention relates to an image-based white blood cell counting method, and at the same time, the invention also relates to a white blood cell counting system. Background technique [0002] As an indication of certain diseases, the number of white blood cells has important clinical significance for disease diagnosis. Physical method, physical-chemical method and image analysis method are the main methods of white blood cell differential counter at present. Although the automatic counters under these methods can quickly and accurately count white blood cells, they still have the characteristics of bulky equipment, high price, high professionalism in operation, and difficult maintenance, which are not suitable for primary medical and health institutions and mobile medical services. However, there are great limitations in the field of application. For the image-based leukocyte adhesion segmentation and counting method, it mainly focuses on the researc...

AI Technical Summary

Problems solved by technology

In this case, the nucleus area cannot be distinguished on the image, and it is difficult to construct a cell machine model; under a 4-20 magnification microscope, the white blood cells have blurred boundaries and a large number, and the method based on morphological boundary extraction still has low precision. When the adhesion cells cannot be identified, the iterative convergence of the B-snake model is too slow and takes a long time; the boundary of the adhesion cells is blurred, and it is difficult to distinguish the concave and convex points of the boundary, and the discrimination error is large

To sum up, the application of the above methods cannot obtain accurate cell segmentation, cannot completely separate the adherent cells, and there is a problem of low counting accuracy

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