The accurate localization of molecules within the cardiomyocyte is a hotly disputed area, and colocalization analysis one of its most often used tools. However, interpretation is often uncertain because colocalization between two or more images is rarely analyzed to determine whether the observed values could have occurred by chance. To address this, we have developed a robust methodology, based on the Monte-Carlo and bootstrap methods, to measure the statistical significance of a colocalization. The method works with voxel-based, intensity-based, object-based and nearest-neighbor metrics. We extend all of these metrics to measure colocalization in images with three colors and introduce a new metric, the cluster diameter, to measure the clustering of fluorophores in three or more images. In addition, we are able to determine not only whether the labeled molecules colocalize with a probability greater than chance, but also whether they are sequestrated into different compartments. The software, written in MatLab and C++, is freely available. We have applied this technique to examine the structure of the cardiomyocyte and the position of molecules essential for E-C coupling.