The quantitative characterization of endocytic vesicles in images acquired with microscope is critically important for deciphering of endocytosis mechanisms. Image segmentation is the most important step of quantitative image analysis. In spite of availability of many segmentation methods, the accurate segmentation is challenging when the images are heterogeneous with respect to object shapes and signal intensities what is typical for images of endocytic vesicles. We present a Morphological reconstruction and Contrast mapping segmentation method (MrComas) for the segmentation of the endocytic vesicle population that copes with the heterogeneity in their shape and intensity. The method uses morphological opening and closing by reconstruction in the vicinity of local minima and maxima respectively thus creating the strong contrast between their basins of attraction. As a consequence, the intensity is flattened within the objects and their edges are enhanced. The method accurately recovered quantitative characteristics of synthetic images that preserve characteristic features of the endocytic vesicle population. In benchmarks and quantitative comparisons with two other popular segmentation methods, namely manual thresholding and Squash plugin, MrComas shows the best segmentation results on real biological images of EGFR (Epidermal Growth Factor Receptor) endocytosis. As a proof of feasibility, the method was applied to quantify the dynamical behavior of Early Endosomal Autoantigen 1 (EEA1)-positive endosome subpopulations during EGF-stimulated endocytosis.
Keywords: Image processing algorithm; colocalization; endocytosis; image segmentation.