J4 ›› 2009, Vol. 27 ›› Issue (03): 299-.

• 论文 • 上一篇    下一篇

基于特征提取算法下点细胞的识别侦测实现

王 峰a,刘晓丹b,黄万风c
  

  1. 吉林大学 a.工商管理学院|长春130025;b.护理学院|长春130021;c.数学学院|长春130025
  • 出版日期:2009-05-20 发布日期:2009-07-13
  • 通讯作者: 刘晓丹(1959— ),女,长春人,吉林大学副教授,主要从事儿科护理学、管理学研究 E-mail:xdliu@jlu.edu.cn
  • 作者简介:王峰(1972— )|男|山东烟台人|吉林大学博士研究生|主要从事企业信息化、投资和创业研究|(Tel)86-13943055368(E-mail)WF64@hotmail.com;刘晓丹(1959— ),女,长春人,吉林大学副教授,主要从事儿科护理学、管理学研究|(Tel)86-13009100181(E-mail) xdliu@jlu.edu.cn
  • 基金资助:

    吉林省发改委基金资助项目(2008220101000383)

 Point-Cell Recognition and Detecting Realization Based on Feature Selection Algorithm

WANG Fenga,LIU Xiao-danb,HUANG Wan-fengc   

  1. a.College of Business Administration|Jilin University|Changchun 130025, China|b. College of Nursing,Jilin University, Changchun 130021,China;c. College of Mathematics, Jilin University, Changchun 130025,China
  • Online:2009-05-20 Published:2009-07-13

摘要:

  为解决类似血液环境中点细胞的自动识别与侦测,提高点细胞的人工辨别和计数效率、避免人为统计误差,达到对点细胞信息状态的快速、自动、准确统计,设计了点细胞识别与匹配流程。通过分析点细胞背景图像场,对其图像进行相应切分、二值化、去燥等处理后,利用细节特征算子得到点细胞信息特征空间场。通过特征向量的决策选优改善传统的搜索计算方法,避免了大量信息冗余查询;采用空间决策性能函数优化搜索,减少了特征提取计算量,提高了点细胞识别准确性与状态定位。该方法可逐步应用在医院等领域进行液体环境中的细胞识别、计数、运动轨迹追踪等。

关键词: 点细胞识别, 图像处理, 特征抽取

Abstract:

In order to solve automatic Identification and detection of point cell under the similar blood environment  achieving the goal of cells information state fast statistic function,and avoid artificial error to Raise manually identification and counting efficiency of point-cell, reduce artificial statistical error, reach rapid and automatic, accurate statistics state of point-cell information, a reasonable point-cell recognition and matching flow is designed. By analyzing point-cell background image field, with its image corresponding segmentation and binarization are done, eliminating its noise disturbance; using the feature selection operator to get some cellular information space based on detail feature extraction, feature extraction has some related relation with feature space transformation which included rigid transformation, affine transformation, projection transformation and nonlinear transformation.By improving the tradition search for the computing technology excellently through decision with vectorial characteristic, large amount of information from being inquired about redundantly has been reduced; to use the optimization search of spatial decision-making performance function it also could reduce feature extraction computation load which enhanced accuracy function of point-cell recognition and position localization. Its technology can be gradually applied to cells path tracing, identification, counting in liquid environment and other domains.

Key words: point-cell recognition, image processing, feature extraction

中图分类号: 

  • TP391.4