Raster Based Region Growing
Donald G. Bailey · 1991
In some image segmentation applications, region growing is more appropriate than simple thresholding or edge detection. One example of this is the location of intensity peaks in an image, where each peak is defined by the valley between it and any adjacent peaks. Conventional region growing techniques require a seed point for each region, and the region is grown by adding adjacent pixels which match the segmentation criterion. Some segmentation criteria are amenable to raster based region growing. Each pixel in the image is examined in turn. If it matches the inclusion criterion of a region associated with one of the pixels above it, it is allocated to that region and labelled accordingly. If not, a new region is started. Segmentation parameters for each region are held in a table indexed by the region label. In this way multiple regions are grown simultaneously. One disadvantage of this approach is that a single region may have multiple start pixels depending on the region shape. These individual regions eventually merge into a single region when they meet. Raster based region growing is fast since it only requires a single pass through the image to perform the initial segmentation, with an additional pass to relabel adjacent regions which have merged.