{"title":"Performance Analysis of Quantifying Fluorescence of Target-Captured Microparticles from Microscopy Images","authors":"P. Sarder, A. Nehorai","doi":"10.1109/SAM.2006.1706139","DOIUrl":null,"url":null,"abstract":"Fluorescence microscopy imaging is widely used in biomedical research, astronomical speckle imaging, remote sensing, positron-emission tomography, and many other applications. In companion papers P. Sarder and A. Nchorai, we developed a maximum likelihood (ML)-based image deconvolution technique to quantify fluorescence signals from a three-dimensional (3D) image of a target captured microparticle ensemble. We assumed both the additive Gaussian and Poisson statistics for the noise. Imaging is performed by using a confocal fluorescence microscope system. Potential application of microarray technology includes security, environmental monitoring, analyzing assays for DNA or protein targets, functional genomics, and drug development. We proposed a new parametric model of the fluorescence microscope 3D point-spread function (PSF) in terms of basis functions. In this paper, we present a performance analysis of the ML-based deconvolution techniques (P. Sarder and A. Nchorai) for both the noise models","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM.2006.1706139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Fluorescence microscopy imaging is widely used in biomedical research, astronomical speckle imaging, remote sensing, positron-emission tomography, and many other applications. In companion papers P. Sarder and A. Nchorai, we developed a maximum likelihood (ML)-based image deconvolution technique to quantify fluorescence signals from a three-dimensional (3D) image of a target captured microparticle ensemble. We assumed both the additive Gaussian and Poisson statistics for the noise. Imaging is performed by using a confocal fluorescence microscope system. Potential application of microarray technology includes security, environmental monitoring, analyzing assays for DNA or protein targets, functional genomics, and drug development. We proposed a new parametric model of the fluorescence microscope 3D point-spread function (PSF) in terms of basis functions. In this paper, we present a performance analysis of the ML-based deconvolution techniques (P. Sarder and A. Nchorai) for both the noise models