基于改进迭代阈值的白细胞图像分割方法  

Leukocyte Image Segmentation Based on Improved Iterative Threshold Method

作　　者：李智超 曹益平[1] LI Zhi-chao;CAO Yi-ping(College of Electronic Information,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电子信息学院,四川成都610065

出　　处：《光学与光电技术》2022年第2期84-94,共11页Optics & Optoelectronic Technology

基　　金：国家科技重大专项(2009ZX02204-008)资助项目。

摘　　要：人体外周血五类白细胞数量和所占比例反映了人体的健康状态,人工检查白细胞消耗医务人员的大量精力,如何用智能方法快速、准确进行白细胞智能分类是一个亟待解决的问题,其中白细胞分割的准确性是正确分类的关键。提出了一种改进的迭代阈值图像分割算法,对恢复有丝分裂连线的最小距离方法进行了基于数学和数模分析的预处理改良,提升了白细胞图像分割的精度和效率,解决了血小板粘连、白细胞边界不分明等问题。将白细胞从复杂的血液环境中分离出来,对有丝分裂细胞的多叶核进行最小距离判定并连线,然后定位到每个白细胞,制作数据集,最后用CNN分类。经测试,分割正确率达到了96%以上。实验结果验证了所提分割方法的准确性、高效性和实用性。The number and proportion of five kinds of leukocyte in human peripheral blood reflect the health state of human body. Manual examination of leukocyte consumes a lot of manpower for medical workers. How to use intelligent method to classify leukocytes quickly and accurately is an urgent problem to be solved. The accuracy of leukocyte segmentation is the key to correct classification. In this paper,an improved iterative threshold image segmentation algorithm is proposed,and the minimum distance method for restoring mitotic lines is improved based on mathematical and digital simulation analysis. The accuracy and efficiency of leukocyte segmentation are improved,and the problems of platelet adhesion and unclear leukocyte boundary are solved by these methods. The leukocytes are separated from the complex blood environment,the minimum distance of the lobulated nucleus leukocytes is determined and connected,and then located to each leukocyte to make a data set,finally,it is classified by CNN. After testing,the accuracy of leukocyte segmentation is more than 96%. The experimental results show that the proposed segmentation method is accurate,efficient and practical.

关 键 词：白细胞 迭代阈值 彩色空间 图像分割 卷积神经网络 

分 类 号：TN911.73[电子电信—通信与信息系统]