Performance Analysis of Quantifying Fluorescence of Target-Captured Microparticles from Microscopy Images

P. Sarder, A. Nehorai
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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
从显微镜图像中定量捕获目标微粒的荧光性能分析
荧光显微镜成像广泛应用于生物医学研究、天文散斑成像、遥感、正电子发射断层成像等领域。在合著论文P. Sarder和a . Nchorai中,我们开发了一种基于最大似然(ML)的图像反褶积技术,用于量化目标捕获微粒集合的三维(3D)图像中的荧光信号。我们假设噪声具有加性高斯统计量和泊松统计量。成像是通过使用共聚焦荧光显微镜系统进行的。微阵列技术的潜在应用包括安全、环境监测、DNA或蛋白质目标分析测定、功能基因组学和药物开发。提出了一种基于基函数的荧光显微镜三维点扩散函数(PSF)参数化模型。在本文中,我们提出了基于ml的反卷积技术(P. Sarder和a . Nchorai)对这两种噪声模型的性能分析
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