(As you may recall from previous threads, my current project involves aligning two arrays of pixel data that were taken from slightly different perspectives) In an attempt to quantify the accuracy of the alignment I've obtained, I want to do some centroid analysis. The images I'm working with are of "beads" (very small fluorescent blobs), thus each 512x512 image is of a number of more-or-less circular islands each on the order of 50 pixels or so. I figure that I can threshold each image, identify distinct islands, get their centroids, map those to centroids in the other wavelength, and thus get the distance between centroids, which should be a good absolute measure of alignment quality. I can hack something together to do this easily enough, where I find a pixel in one of the islands, flood-fill out to get all connected pixels, calculate the centroid, flip the pixels to 0, and repeat until all islands are gone. This isn't exactly very speedy though. What's the efficient way to do this? Is there one? Is there a better approach I should be taking? The image processing class I dimly remember taking years ago didn't cover this kind of thing, so I'm lacking even the basic vocabulary needed to search for algorithms. -Chris