摘要
白细胞分割是医学图像处理领域的一项富有挑战性的任务,针对目前白细胞分割存在的准确度不高、粘连情况不易分割等问题,将图像的分割转化为区域节点的分类问题,提出基于图卷积神经网络的白细胞分割算法。首先将训练图像经超像素分割得到若干超像素区域,把每个超像素区域作为图的一个节点,并充分利用超像素区域的彩色特征以及空间邻域关系构造稀疏加权图来训练图卷积网络,然后利用训练好的网络对测试图像进行白细胞核、质、背景的三域一次性分类。实验数据表明,本文算法对不同类白细胞均具有较好的分割效果。
White blood cell(WBC)segmentation is a challenging task in the field of medical image processing.Aiming at problems such as the low accuracy of current white cell segmentation and the difficulty of segmentation of adhesion,the image segmentation is transformed into a classification problem of graph nodes and a white cell segmentation algorithm based on graph convolutional neural network(GCN)is presented.First,the training samples are oversegmented by superpixel algorithm to obtain a number of superpixel regions,each superpixel region is used as a node of the graph,the RGB features of the superpixel region and the spatial neighborhood relationship are used to construct a sparse weighted graph to train the graph convolution network,then the trained network is used to classify the experimental data into the three domains of white blood cell nucleus,qualitative and background.Experimental data show that the algorithm in this paper has a good segmentation effect on different types of white blood cells.
作者
刘汉强
张元
LIU Hanqiang;ZHANG Yuan(School of Computer Science,Shaanxi Normal University,Xi'an,Shaanxi 710119,China)
出处
《光电子．激光》
CAS
CSCD
北大核心
2021年第10期1074-1082,共9页
Journal of Optoelectronics·Laser
基金
中央高校基本科研业务费专项资金(GK202103085)
陕西省自然科学基础研究计划项目(2020JM-299,2021JM-461)资助项目。
关键词
白细胞分割
半监督学习
图卷积网络
超像素方法
white blood cell segmentation
semi-supervised learning
graph convolution network
superpixel algorithm
