Local intensity and PCA based detection of virus particle candidates in transmission electron microscopy images

We present a general method using local intensity information and PCA to detect objects characterized only by that they differ from their surroundings. We apply our method to the problem of automatically detecting virus particle candidates in transmission electron microscopy images. Viruses have very different shapes and sizes, many species are spherical whereas others are highly pleomorphic. To detect any kind of virus particles in electron microscopy images it is therefore necessary to use a method not restricted to detection of a specific shape. The method proposed here uses only one input parameter, the approximate virus thickness, which is a conserved feature within a virus species. It is capable to detect virus particles of very varying shapes. Results on images with highly textured background of several different virus species are presented.