Background:　In　the　nervous　system,　the　neurons　communicate　through　synapses.　The　size,　morphology,　and　connectivity　of　these　synapses　are　significant　in　determining　the　functional　properties　of　the　neural　network.　Therefore,　they　have　always　been　a　major　focus　of　neuroscience　research.　Two-photon　laser　scanning　microscopy　allows　the　visualization　of　synaptic　structures　in　vivo,　leading　to　many　important　findings.　However,　the　identification　and　quantification　of　structural　imaging　data　currently　rely　heavily　on　manual　annotation,　a　method　that　is　both　time-consuming　and　prone　to　bias.　Results:　We　present　an　automated　approach　for　the　identification　of　synaptic　structures　in　two-photon　images.　Axon　boutons　and　dendritic　spines　are　structurally　distinct.　They　can　be　detected　automatically　using　this　image　processing　method.　Then,　synapses　can　be　identified　by　integrating　information　from　adjacent　axon　boutons　and　dendritic　spines.　In　this　study,　we　first　detected　the　axonal　boutons　and　dendritic　spines　respectively,　and　then　identified　synapses　based　on　these　results.　Experimental　results　were　validated　manually,　and　the　effectiveness　of　our　proposed　method　was　demonstrated.　Conclusions:　This　approach　will　helpful　for　neuroscientists　to　automatically　analyze　and　quantify　the　formation,　elimination　and　destabilization　of　the　axonal　boutons,　dendritic　spines　and　synapses.