Providing a general framework for mitosis detection is challenging. The variability of the visual traits and temporal features which classify the event of cell division is huge due to the numerous cell types, perturbations, imaging techniques and protocols used in microscopy imaging analysis studies. The commonly used machine learning techniques are based on the extraction of comprehensive sets of discriminative features from labeled examples and therefore do not apply to general cases as they are restricted to trained datasets. We present a robust mitotic event detection algorithm that accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetrical cell divisions, we consider the anaphase stage, immediately after the DNA material divides, at which the two daughter cells are approximately identical. Having detected pairs of candidate daughter cells, based on their association to potential mother cells, we look for the respective symmetry axes. Mitotic event is detected based on the calculated measure of symmetry of each candidate pair of cells. Promising mitosis detection results for four different time-lapse microscopy datasets were obtained.