Section 11.3.2.5. Localizing diffraction spots

Recognition and refinement of the parameter values controlling the observed diffraction pattern begins with the extraction of a list of coordinates of strong spots occurring in the images. As implemented in XDS, this list is obtained by the following procedure. First, each pixel value is compared with the mean value and standard deviation of surrounding pixels in the same image and classified as a strong pixel if its value exceeds the mean by a given multiple (typically 3 to 5) of the standard deviation. Values of the strong pixels and their location addresses and image running numbers are stored in a hash table during spot search [for a discussion of the hash technique, see Wirth (1976)]. After processing a fixed number of images, or when the table is full, all strong pixels are labelled by a unique number identifying the spot to which they belong. By definition, any two such pixels which can be connected by direct strong neighbours in two or three dimensions (if there are adjacent images) belong to the same spot (equivalence class). The labelling is achieved by the highly efficient algorithm for the recording of equivalence classes developed by Rem (see Dijkstra, 1976). At the end of this procedure, the table is searched for spots that have no contributing strong pixel on the current or the previous image. These spots are complete and their centroids are evaluated and saved in a file. To make room for new strong pixels as the spot search proceeds, all entries of strong pixels that are no longer needed are removed from the hash table and the remaining ones are rehashed. On termination, a list of the centroids of strong spots is available.