激光技术, 2020, 44 (1): 125, 网络出版: 2020-04-13
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
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 image processing white blood cell classification valley threshold segmentation method of gray histo artificial neural network
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。
Abstract
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.
张从鹏, 马岩, 毛潭, 熊国顺. 面向计算机显微视觉系统的白细胞识别[J]. 激光技术, 2020, 44(1): 125. ZHANG Congpeng, MA Yan, MAO Tan, XIONG Guoshun. Leukocyte recognition oriented to computer micro-vision system[J]. Laser Technology, 2020, 44(1): 125.