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Development of methods for deconvolution algorithms performance analysis using FIJI and Icy plugins

机译:使用FIJI和Icy插件开发反卷积算法性能分析方法的开发

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The analysis of deconvolution algorithms performance is crucial when is wanted to use deconvolution as an effective image restoration approach. The performance of deconvolution algorithms available in Open Source software was compared by using three-dimensional (3D) microscopy images. DeconvolutionLab and MitivBlindDeconvolution were the plugins chosen from FIJI and Icy respectively. In the first place, analyses included 3D bead stack measurements both pre- and post-deconvolution by using theoretical and empirical Point Spread Functions (PSFs) as well as parameter variation. The set of parameters that resulted in the improvement of both the 3D morphology and intensity of the beads was applied to 3D autofluorescence colon tissue images from BALB/c mice to evaluate if original morphology and intensity features were restored.
机译:当想将反卷积作为一种有效的图像恢复方法时,对反卷积算法的性能进行分析至关重要。使用三维(3D)显微镜图像比较了开放源代码软件中可用的反卷积算法的性能。 DeconvolutionLab和MitivBlindDeconvolution是分别从FIJI和Icy中选择的插件。首先,分析包括通过使用理论和经验的点扩展函数(PSF)以及参数变化对反卷积前后的3D珠叠测量。将导致磁珠的3D形态和强度均改善的参数集应用于来自BALB / c小鼠的3D自发荧光结肠组织图像,以评估是否恢复了原始形态和强度特征。

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