Local Analysis as Grouping in Measurement Space.
Another approach towards segmentation is based on a local analysis of the raw data, explicitly utilizing the spatial information content of the data set. Within this approach, objects are defined as areas being enclosed by borders, i.e., strong local signal variations. However, a local analysis can yield only local data, i.e. edge elements. Thus in a second processing step these edge elements have to be grouped into continuous and closed borderlines defining valid object regions. The edge-grouping process has to delete edge elements not consistent with other data, and to create some missing edge elements in order to close boundaries (figure 1.c). This requires, of course, some global knowledge about the borderlines present.
Figure 1: The image a) used for testing segmentation algorithms. It consist of several simple objects, with gaussian distributed noise added. In b) the results of a simple cluster algorithm are displayed, an edge-detection scheme was employed in c).