Method for leukocyte automatic identification based on relevant vector machine

Abstract

The invention provides a method for leukocyte automatic identification based on a relevant vector machine. According to the method, hue information of blood microscopic image characteristics is utilized and coarse segmentation of a hue image is accomplished according to a gray level image segmentation method based on the relevant vector machine; all leukocytes are detected with the assistance of an FCNN; a threshold value is determined through the clustering methodology and fine segmentation is conducted on a partial image containing one single leukocyte with the combination of the threshold value segmentation method and a binary morphology method; on the basis of the partial images obtained in the last step, the representative leukocyte characteristics are extracted, wherein the leukocyte characteristics comprise 47 characteristics in three types of forms, colors and textures; the leukocytes are identified and classified through the supported vector machine. The method has the advantages that the identification effect is ideal, stability is high and robustness is good. Valuable information is provided for diagnosis conducted by a doctor and quantitative analytical investigation is rapidly and accurately conducted on the cells.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and specifically refers to a white blood cell automatic recognition method based on a correlation vector machine. Background technique [0002] By examining the changes in the number and shape of various types of white blood cells in the blood, it can often provide valuable information for doctors to diagnose and help diagnose some diseases. The emergence of new medical branches such as quantitative cytology, molecular biology, and cellular immunology has made the requirement for rapid and accurate quantitative analysis of cells more urgent. However, it is time-consuming and labor-intensive for experts to inspect with the naked eye through a microscope, and the workload is very heavy, and the recognition error is greatly affected by subjective factors such as the experience and fatigue of the experts. With the rapid development of computer image processing technology, pattern re...

AI Technical Summary

Problems solved by technology

Sometimes leukocytes in micrographs may be contaminated with stains due to poor handling

Coupled with the inconsistency of lighting, staining and other factors, it becomes more difficult to distinguish each other.

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