Method for segmenting white blood cell image

Abstract

The invention provides a method for segmenting a white blood cell image, which is characterized by comprising the following steps: firstly carrying out binaryzation on the green component picture of a color white blood cell image to obtain an initial interested region based on a significance attention mechanism of human vision; carrying out cancellation and mergence on a region by labeling the initial interested region to obtain an adaptive significance window of each cell; and finally realizing segmentation of nucleuses and cytoplasm in each adaptive significance window by a boundary-extending method. The segmentation method is used to realize the fast segmentation of the white blood cells, particularly the accurate segmentation for high-capacity pictures which are the non-standard dyed and contain overlapped cells.

Description

technical field [0001] The invention relates to a cell recognition technology of normal human peripheral blood white blood cell images, in particular to a method for automatic separation of non-standard stained normal human peripheral blood adhesion white blood cell images containing many cells and background impurities. Background technique [0002] Blood cell image segmentation and classification recognition is one of the hot topics in medical imaging analysis technology and application research in recent years. The purpose of this technical research is to use computer to simulate the visual analysis process of human blood test experts, automatically extract and calculate various morphological parameters of cells, and then classify and analyze cells, so as to improve the accuracy and robustness of medical imaging analysis applications , intelligent and real-time, to meet the needs of high-efficiency intelligent and automatic application of blood routine examination. Only ...

AI Technical Summary

Problems solved by technology

[0005] Disadvantages: Since the selection of seed points is through continuous erosion or through distance transformation first and then a certain domain value selection, it is very sensitive to weak edges

[0008] Disadvantages: The rules for region merging are difficult to determine, which may lead to over-segmentation

[0011] Disadvantages: Since the selection of the concave point is based on the contour, the algorithm is extremely sensitive to burrs and noise on the contour

[0014] Disadvantages: The cells in the actual situation are often not round, the red blood cells are hollow, and the monocytes often have vacuoles, which will lead to segmentation errors; in addition, the adhesion of white blood cells and impurities does not satisfy the circular segmentation

[0015] If there are more impurities in the white blood cell picture and uneven illumination, the above algorithm cannot achieve the segmentation between white blood cells and impurities under the premise of ensuring the integrity of the white blood cell shape; if the capacity of the white blood cell picture increases, and one picture contains dozens of white blood cells, The speed of the above algorithm will slow down sharply; if the size of the cohesive cells is greatly different, the above algorithm cannot fully realize the morphological segmentation of the cells, so it cannot meet the needs of real-time and accuracy of white blood cell identification.