Deconvolution improves colocalization analysis of multiple fluorochromes in 3D confocal data sets more than filtering techniques
Deconvolution improves colocalization analysis of multiple fluorochromes in 3D confocal data sets more than filtering techniques
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DOI：
10.1046/j.1365-2818.2002.01068.x
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发表时间：
2002-11-01
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影响因子：
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通讯作者：
Landmann, L
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作者：
Landmann, L
背景和噪声通过影响分辨率和在低强度范围内模糊图像细节而损害图像质量。由于未处理的共聚焦图像中的背景电平通常在最大强度的30%左右，因此共定位分析（一种典型的分割过程）仅限于高强度信号，并且容易产生噪声诱导的假阳性事件。这使得背景的抑制或去除对于这种图像分析至关重要。本文研究了中值滤波和反卷积这两种增强信噪比的图像处理技术对生物标本共聚焦数据集共定位分析结果的影响。数据表明，中值滤波可以使信噪比提高2倍。该技术成功地消除了噪声引起的共定位事件。然而，由于滤波从局部邻域假负（信号强度低于阈值的“耗散”）和假正（低强度信号的噪声“融合”导致高于阈值的强度）中恢复体素值，因此可以生成结果。此外，滤波涉及到图像与核的卷积，这一过程本质上损害了分辨率。反卷积图像恢复避免了这两个缺点。这样的例程计算对象的模型，考虑各种参数，损害图像的形成，并能够将背景抑制到非常低的水平（< 10%的最大强度，导致信噪比提高了3倍，与原始图像相比）。这使得在低强度但高频率范围内的其他对象可用于分析。此外，去除由光学系统引起的噪声和畸变可以提高分辨率，这在涉及近分辨率大小的物体的情况下至关重要。然而，这种技术对背景水平的高估很敏感。总之，反卷积比滤波更能改善共定位分析。这尤其适用于以小物体尺寸和/或低强度为特征的标本。
Background and noise impair image quality by affecting resolution and obscuring image detail in the low intensity range. Because background levels in unprocessed confocal images are frequently at about 30% maximum intensity, colocalization analysis, a typical segmentation process, is limited to high intensity signal and prone to noise-induced, false-positive events. This makes suppression or removal of background crucial for this kind of image analysis. This paper examines the effects of median filtering and deconvolution, two image-processing techniques enhancing the signal-to-noise ratio (SNR), on the results of colocalization analysis in confocal data sets of biological specimens.The data show that median filtering can improve the SNR by a factor of 2. The technique eliminates noise-induced colocalization events successfully. However, because filtering recovers voxel values from the local neighbourhood false-negative ('dissipation' of signal intensity below threshold value) as well as false-positive ('fusion' of noise with low intensity signal resulting in above threshold intensities), results can be generated. In addition, filtering involves the convolution of an image with a kernel, a procedure that inherently impairs resolution.Image restoration by deconvolution avoids both of these disadvantages. Such routines calculate a model of the object considering various parameters that impair image formation and are able to suppress background down to very low levels (< 10% maximum intensity, resulting in a SNR improved by a factor 3 as compared to raw images). This makes additional objects in the low intensity but high frequency range available to analysis. In addition, removal of noise and distortions induced by the optical system results in improved resolution, which is of critical importance in cases involving objects of near resolution size. The technique is, however, sensitive to overestimation of the background level. In conclusion, colocalization analysis will be improved by deconvolution more than by filtering. This applies especially to specimens characterized by small object size and/or low intensities.