自适应混合发射单分子定位算法  

Self-Adaptive Mixed-Emitter Single-Molecule Localization Algorithm

作　　者：刘一哲 赵唯淞 刘宇桢 李浩宇[1] Liu Yizhe;Zhao Weisong;Liu Yuzhen;Li Haoyu(School of Instrumentation Science and Engineering,Harbin Institute of Technology,Harbin 150080,Heilongjiang,China)

机构地区：[1]哈尔滨工业大学仪器科学与工程学院,黑龙江哈尔滨150080

出　　处：《中国激光》2023年第21期88-95,共8页Chinese Journal of Lasers

摘　　要：单分子定位技术通过随机激发荧光标记获得一组稀疏的图像序列,同时对荧光点进行亚像素级别定位,最终实现超分辨显微成像。基于拟合的单分子定位算法,如单发射(SE)及多发射(ME)定位算法,通过对估计器性能进行改进提高了单分子定位的精度和速度;然而,受失配误差和串扰误差的影响,SE算法和ME算法在不同密度情况下各有优劣,均无法达到全密度范围内最优的估计效果,并且分别存在荧光分子利用效率低和计算量大的缺点。本文提出了自适应混合发射单分子定位(SM)算法,该算法通过图像荧光发射密度及强度自适应地确定的拟合区域以及所采用的拟合模型及模型初值,有效避免了上述两种误差的影响,达到了全密度范围内一致、良好的定位效果。在仿真和实验数据上将所提SM算法与SE算法、ME算法进行比较,结果显示,SM算法重构图像的分辨率和对比度在不同发射密度下均具有优势。Objective Currently,various super-resolution imaging technologies can surpass the Abbe diffraction limit,thereby improving imaging resolution to several tens of nanometers.This provides biologists with an effective tool for investigating biological structures and their functions on a novel scale.Among these,single-molecule localization techniques such as photoactivated localization microscopy(PALM)and stochastic optical reconstruction microscopy(STORM)yield the highest resolution.Traditional fitting-based methods,such as single-emitter localization(SE)and multi-emitter localization(ME)algorithms,employ fixed-size sliding windows to select the fitting areas.However,this was found to lead to an inadequate use of the prior emitter recognition information during the emitter localization stage in this study,thereby resulting in diverse advantageous density ranges and different artifact forms of SE and SM.The SE results are distorted by truncates near the emitters,which are generated by the fixed sizes of the fitting areas,whereas the ME suffers from an inappropriate fitting number.In summary,a self-adaptive mixed-emitter single-molecule localization algorithm(SM)that can adaptively determine the fitting area and fitting number is proposed in this study.Consequently,compared with the SE and ME algorithms,the images reconstructed by the SM algorithm exhibit a superior resolution and contrast over the complete density range on both simulated and experimental data.Methods The complete SM algorithm comprises several steps.First,an SNR binary map that can shrink and expand with the power of noise was generated based on the original image.Subsequently,the SNR binary map was combined with the local maxima for emitter recognition,and the sliding window and fitting number were generated using the SNR binary map.The center and size of the generated sliding window were then determined based on the center position and size of the connected domain,respectively,whereas the fitting number was obtained from previous emitter recognitio

关 键 词：生物医学 单分子定位显微技术 超分辨成像 单发射模型 多发射模型 自适应算法 

分 类 号：O436[机械工程—光学工程]