Our problem is to automatically detect and measure from images the length and number of microscopic hair-like structures (filopodia) emanating from the tip of growing nerve processes. The objects of interest are relatively long and thin, so a good edge-detection algorithm helps to separate the filopodia from the background. Since a common claim about the wavelet transform is that it splits images into an approximation and details, which contain edges, we use it in our experiments. This paper studies the edge detecting characteristics of the 2D discrete wavelet transform, and compares it to other common edge-detection methods for filopodia detection.