Direct visualization of synapses is a prerequisite to the analysis of the spatial distribution patterns of synaptic systems and is a crucial step leading to the understanding of synaptic circuitry. In order to facilitate the identification of individual synapses from microscope images, we have introduced a wavelet-based approach for the automated recognition of axonal synaptic varicosities. The proposed differential wavelets are specifically designed for the recognition of peaks, which correspond to the axonal synaptic varicosities of parallel fibers. The 2-D image of an axon together with its synaptic varicosities is first transformed into a 1-D profile in which the axonal varicosities are represented by peaks in the signal. Next, by decomposing the 1-D profile in the differential wavelet domain, we employ the multiscale point-wise product to distinguish between peaks and noises in the multiscale domain. The ability to separate the peaks (due to synaptic varicosities) from noise makes possible a reliable and accurate recognition of axonal synaptic varicosities. The performance of the algorithms has been systematically evaluated. The results indicate that they are satisfactory for practical use.
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