面向计算机显微视觉系统的白细胞识别  

Leukocyte recognition oriented to computer micro-vision system

作　　者：张从鹏 马岩 毛潭 熊国顺 ZHANG Congpeng;MA Yan;MAO Tan;XIONG Guoshun(College of Mechanical and Material Engineering,North China University of Technology,Beijing 100144,China)

机构地区：[1]北方工业大学机械与材料工程学院

出　　处：《激光技术》2020年第1期125-129,共5页Laser Technology

基　　金：北京市教委基本科研资助项目(110052971803)

摘　　要：为了解决人工镜检白细胞识别效率低下的问题,采用计算机显微视觉平台进行了白细胞自动识别研究。白细胞图像分割方面,筛选图像颜色模型之后采用区域生长算法实现白细胞与图像背景的精确剥离;并利用大津法(即灰度直方图波谷阈值分割方法)实现了白细胞细胞核和细胞浆的提取;根据细胞的形态、颜色及纹理特征用人工神经网络分类器对大样本量的白细胞进行了识别分类。结果表明,采用白细胞图像分割和智能辨识算法具有较高的精度和效率,最终准确度能够达到95.6%。该系统满足临床医学显微视觉白细胞自动检测的需求。To solve the problem of low efficiency of leukocyte recognition in artificial microscopy,automatic recognition of white blood cells was studied on computer micro vision platform.After filtering the image color model,precise stripping of white blood cells and image background was realized by region growing algorithm.The extraction of nucleus and cytoplasm of leucocytes was realized by Otsu method,which is valley threshold segmentation method of gray histogram.According to the morphological,color and texture characteristics of cells,a large number of white blood cells were identified and classified by artificial neural network classifier.The results show that,white blood cell image segmentation and intelligent identification algorithm have high accuracy and efficiency.The final accuracy can reach 95.6%.It meets the need of automatic detection of leukocytes in clinical microscopic vision.

关 键 词：图像处理 白细胞识别 灰度直方图波谷阈值分割方法 人工神经网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]