Improving cephalogram analysis through feature subimage extraction

An MLP with a GA was proposed to extract feature subimages containing orthodontic landmarks. Simulated images and cephalograms were used to investigate its performance in comparison with the cross-correlation method. From the results of simulated image containing shapes with different geometrical conditions, it was shown that the fault tolerance of the MLP for rotation, scaling, brightness variety, and other anomalous deformations is good enough to overcome the clinical application problems. It was also shown that the stability, accuracy, and speed of this proposed algorithm are very promising. Moreover, the performance of the MLP can be significantly improved by collecting more 'representative' false patterns. The GA is a good approach to speed up the process of feature subimage extraction based on the fitness evaluated using the MLP.