Morphological reconstruction is a useful but little-known method for extracting meaningful information about shapes in an image. The shapes could be just about anything: letters in a scanned text document, fluorescently stained cell nuclei, or galaxies in a far-infrared telescope image. You can use morphological reconstruction to extract marked objects, find bright regions surrounded by dark pixels, detect or remove objects touching the image border, detect or fill in object holes, filter out spurious high or low points, and perform many other operations.
Essentially a generalization of flood-filling, morphological reconstruction processes one image, called the marker, based on the characteristics of another image, called the mask. The high points, or peaks, in the marker image specify where processing begins. The peaks spread out, or dilate, while being forced to fit within the mask image. The spreading processing continues until the image values stop changing.
This excerpt from the second edition of Digital Image Processing Using MATLAB teaches what morphological reconstruction means, illustrates some useful manipulations of binary images, and shows how you can use functions in Image Processing Toolbox™ to quickly perform these manipulations.