A Wavelet Approach for the Identification of Axonal Synaptic Varicosities from Microscope Images

Direct visualization of synapses is a prerequisite to the analysis of the spatial distribution patterns of synaptic systems. Such an analysis is essential to the understanding of synaptic circuitry. In order to facilitate the visualization of individual synapses at the subcellular level from microscope images, we have introduced a wavelet-based approach for the semiautomated recognition of axonal synaptic varicosities. The proposed approach to image analysis employs a family of redundant wavelet representations. They are specifically designed for the recognition of signal peaks, which correspond to the presence of axonal synaptic varicosities. In this paper, the two-dimensional image of an axon together with its synaptic varicosities is first transformed into a one-dimensional (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. The ability to separate the true signals (due to synaptic varicosities) from noise makes possible a reliable and accurate recognition of axonal synaptic varicosities. The proposed algorithms are also designed with a variable threshold that effectively allows variable sensitivities in varicosity detection. The algorithm has been systematically validated using images containing varicosities $({\le}30)$ that have been consistently identified by seven human observers. The proposed algorithm can give high sensitivity and specificity with appropriate threshold. The results have indicated that the semiautomatic approach is satisfactory for processing a variety of microscopic images of axons under different conditions.