[1]陈远,赵志敏,刘磊.基于ST图的微循环血细胞自动跟踪与测量技术[J].东南大学学报(自然科学版),2011,41(1):72-76.[doi:10.3969/j.issn.1001-0505.2011.01.015]
 Chen Yuan,Zhao Zhimin,Liu Lei.Automatic tracking and measurement of blood cells motion in microcirculation based on ST image[J].Journal of Southeast University (Natural Science Edition),2011,41(1):72-76.[doi:10.3969/j.issn.1001-0505.2011.01.015]
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基于ST图的微循环血细胞自动跟踪与测量技术()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第1期
页码:
72-76
栏目:
计算机科学与工程
出版日期:
2011-01-20

文章信息/Info

Title:
Automatic tracking and measurement of blood cells motion in microcirculation based on ST image
作者:
陈远1赵志敏2刘磊1
(1南京航空航天大学自动化学院, 南京 211106)
(2南京航空航天大学理学院, 南京 211106)
Author(s):
Chen Yuan1Zhao Zhimin2Liu Lei1
(1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
(2College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
关键词:
微循环细胞跟踪流速测量时空图
Keywords:
microcirculation cells tracking velocity measurement spatiotemporal image
分类号:
TP391.41
DOI:
10.3969/j.issn.1001-0505.2011.01.015
摘要:
为了有效分析微循环中血细胞的运动,利用血管中心线生成ST图,通过提取ST图中的轨迹,实现对血细胞的自动跟踪与测量.首先,设计出多尺度的方向滤波器,对ST图进行增强预处理; 然后,在分析增强图像的概率密度分布函数和方向角度的基础上,设计了噪声滤波函数和方向滤波函数以提取细胞轨迹; 最后,对提取的轨迹细化并计算其方向,实现对细胞的跟踪和流速测量.分别对人体微循环中的红细胞和白细胞进行跟踪与测量,跟踪的正确率达到96.5%以上,误差率小于1%.将流速测量结果与人工测量结果相比较,平均相关系数为0.98,高于现有的测量方法,表明该方法能更有效地分析和测量微循环中血细胞的运动.
Abstract:
In order to analyze the blood cells motion in microcirculation effectively, a spatiotemporal (ST) image is generated according to the centerline of the blood vessel, and the blood cells tracking and measurement is realized by extracting the traces on the ST image. Firstly, a multi-scale directional filter is designed for ST image enhancement. Then, with the analysis of the probability density and the orientations of the enhanced image, the noise suppression function and the orientation filtering function are designed for extracting the traces. Finally, by thinning the extracted traces and calculating the orientations, the blood cells tracking and velocity measurement are realized. The red blood cells (RBCs) and leukocytes in microcirculation are taken as the experimental targets. The correct-tracking rate is more than 96. 5% while false-tracking rate is less than 1%. Comparing the velocity measurement results with the manual evaluation, the average correlation coefficient is 0. 98, which is higher than the existing method. The results show that the novel method can analyze and measure the blood cells motion in microcirculation more efficiently.

参考文献/References:

[1] Angelis D,Grassi R W,Cutolo M.A growing need for capillaroscopy in rheumatology [J].Arthritis and Rheumatism,2009,61(3):405-410.
[2] 葛云,章东.活体细胞图像斑点的自动提取和跟踪方法[J].东南大学学报:自然科学版,2009,39(3):464-467.
  Ge Yun,Zhang Dong.Automated methods for particle tracking of the intracellular transport of the clathrin coated pits and and vesicles [J].Journal of Southeast University:Natural Science Edition,2009,39(3):464-467.(in Chinese)
[3] Tsukada K,Minamitani H,Sekizuka E,et al.Image correlation method for measuring blood flow velocity in microcirculation:correlation window simulation and in vivo image analysis[J].Physiological Measurement,2000,21(4):459-464.
[4] Mukherjee D,Ray N,Acton S.Level set analysis for leukocyte detection and tracking [J].IEEE Transactions on Image Processing,2004,13(4):562-572.
[5] Sourice A,Plantier G,Saumet J.Red blood cell velocity estimation in microvessels using the spatiotemporal autocorrelation [J].Measurement Science and Technology,2005,16(11):2229-2239.
[6] Dobbe J G G,Streekstra G J,Atasever B,et al.Measurement of functional microcirculatory geometry and velocity distributions using automated image analysis [J].Medical Biologic Engineer Computer,2008,46(7):659-670.
[7] Drew P J,Pablo B,Gert C,et al.Rapid determination of particle velocity from space-time images using the Radon transform [J].Journal of Computational Neuroscience,2010,29(1):1-7.
[8] Shahidi M,Wanek J,Gaynes B,et al.Quantitative assessment of conjunctival microvascular circulation of the human eye [J].Microvascular Research,2010,79(2):109-113.
[9] Sato Y,Jian C,Reza A Z,et al.Automatic extraction and measurement of leukocyte motion in microvessels using spatiotemporal image analysis [J].IEEE Transactions on Biomedical Engineering,1997,44(4):225-236.
[10] Ray N,Acton S.Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images [J].IEEE Transactions on Biomedical Engineering,2005,52(10):1702-1712.
[11] Frangi A F,Niessen W J,Vincken K L,et al.Multiscale vessel enhancement filtering [C]//Proceedings of Medical Image Computing and Computer-assisted Intervention.Cambridge,MA,USA,1998,1496:130-137.
[12] Jacob M,Unser M.Design of steerable filters for feature detection using canny-like criteria [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):1007-1019.
[13] Wu C C,Zhang G,Huang T C,et al.Red blood cell velocity measurements of complete capillary in finger nail-fold using optical flow estimation [J].Microvascular Research,2009,78(3):319-324.

备注/Memo

备注/Memo:
作者简介:陈远(1981—),男,博士生;赵志敏(联系人),女,教授,博士生导师,zhaozhimin@nuaa.edu.cn.
基金项目:国家自然科学基金资助项目(10172043)、教育部博士点基金资助项目(20040287012).
引文格式: 陈远,赵志敏,刘磊.基于ST图的微循环血细胞自动跟踪与测量技术[J].东南大学学报:自然科学版,2011,41(1):72-76.[doi:10.3969/j.issn.1001-0505.2011.01.015]
更新日期/Last Update: 2011-01-20