Colocalization analysis yields superior results after image restoration

Colocalization analysis is a powerful tool for the demonstration of spatial and temporal overlap in the distribution patterns of fluorescent probes. In unprocessed images, background affects image quality by impairing resolution and obscuring image detail in the low-intensity range. Because confocal images suffer from background levels up to 30% maximum intensity, colocalization analysis, which is a typical segmentation process, is limited to high-intensity signal. In addition, noise-induced, false-positive events ("dust") may skew the results. Therefore, suppression of background is crucial for this type of image analysis. Analysis of synthetic and biological objects demonstrates that median filtering is able to eliminate noise-induced colocalization events successfully. Its disadvantages include the occasional generation of false-positive and false-negative results as well as the inherent impairment of resolution. In contrast, image restoration by deconvolution suppresses background to very low levels (<10% maximum intensity), which makes additional objects in the low-intensity but high-frequency range available for analysis. The improved resolution makes this technique extremely suitable for examination of objects of near resolution size as demonstrated by correlation coefficients. Deconvolution is, however, sensitive to overestimation of the background level. Conclusions for practical application are: (1) In raw images, colocalization analysis is limited to the intensity range above the background level. This means the higher the RS/N the better. Unfortunately, images of most biological specimens have a low RS/N. (2) Filtering improves the result substantially. The reduction of background levels and the concomitant increase of the RS/N are generated at the expense of resolution. This is a quick and simple method in cases where resolution is not a major concern. (3) If colocalization in the low-intensity range and/or maximum resolution play a role, deconvolution should be used.