The quantification of colocalizing signals in multichannel fluorescence microscopy images depends on the reliable segmentation of corresponding regions of interest, on the selection of appropriate colocalization coefficients, and on a robust statistical criterion to discriminate true from random colocalization. Here, we introduce a confined displacement algorithm based on image correlation spectroscopy in combination with Manders colocalization coefficients M1(ROI) and M2(ROI) to quantify true and random colocalization of a given florescence pattern. We show that existing algorithms based on block scrambling exaggerate the randomization of fluorescent patterns with resulting inappropriately narrow probability density functions and false significance of true colocalization in terms of p values. We further confine our approach to subcellular compartments and show that true and random colocalization can be analysed for model dendrites and for GABA(B) receptor subunits GABA(B)R1/2 in cultured hippocampal neurons. Together, we demonstrate that the confined displacement algorithm detects true colocalization of specific fluorescence patterns down to subcellular levels.