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作 者:闫明月 陈安琪 毕磊 胡舜迪 闻路红 YAN Mingyue;CHEN Anqi;BI Lei;HU Shundi;WEN Luhong(Research Institute of Advanced Technology,Ningbo University,Ningbo 315211,China;China Innovation Instrument Co Ltd,Ningbo 315100,China)
机构地区:[1]宁波大学高等技术研究院,浙江宁波315211 [2]宁波华仪宁创智能科技有限公司,浙江宁波315100
出 处:《传感器与微系统》2021年第3期116-119,共4页Transducer and Microsystem Technologies
基 金:国家重点研发计划资助项目(2018YFC1603504);国家自然科学基金资助项目(81902604);浙江省重点研发计划资助项目(2020C03026);云南省重点研发计划资助项目(2018BC011);广州市番禺区创新创业领军团队资助项目(2017—R01—5)。
摘 要:显微操作在生物医学领域具有重要的应用价值。其中,三维空间中微米尺度的毛细针精密定位是显微操作的重点与难点。传统算法对于平面内毛细针定位性能良好,但面向空间中呈倾斜放置的毛细针时,通常表现出精度低、普适性差、易受光源条件影响等缺点。为提升毛细针空间定位精度,本文提出了点扫描与自动感兴趣区域(ROI)联用算法。基于针尖双端点扫描提升平面定位精度;基于自动ROI算法并结合拉普拉斯方差聚焦评价函数提升景深方向定位精度。实验结果表明:本文方法应用于空间中呈倾斜放置的毛细针时,三维定位误差小于1μm,克服了传统算法精度低、普适性差、易受光源条件影响等问题,为精密显微操作奠定了良好的算法基础。Micromanipulation is of critical importance in biomedical applications.Particularly,the precise 3D positioning of pipette tip in micron-scale is a major topic of micromanipulation.The traditional algorithms function well for the horizontally-placed pipette.However,when facing the pipette that are placed obliquely,the traditional algorithms usually show the disadvantages of low precision,poor universality and susceptibility to influence of light source conditions.In order to improve positioning precision of pipette tip,a combined algorithm including the point scanning algorithm and automatic region of interest(ROI)algorithm is proposed.The plane positioning precision is improved by the dual-end scanning of the pipette tip.The positioning precision is improved based on the automatic ROI algorithm combined with the Laplace variance focusing evaluation function.The experimental results show that the 3D positioning error of the proposed method can be less than 1μm when applied to the obliquely-placed pipette.Moreover,the proposed method overcomes the problems of low precision,poor universality and susceptibility to influence of light source conditions of traditional algorithms,lay a good foundation for precise micromanipulation.
关 键 词:毛细针 三维定位 点扫描 自动感兴趣区域(ROI)聚焦 聚焦评价函数值
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