Automated Cephalometric Landmark Localization Using Sparse Shape and Appearance Models

In this paper an automated method is presented for the localization of cephalometric landmarks in craniofacial cone-beam computed tomography images. This methodmakes use of a statistical sparse appearance and shape model obtained fromtraining data. The sparse appearance model captures local image intensity patterns around each landmark. The sparse shape model, on the other hand, is constructed by embedding the landmarks in a graph. The edges of this graph represent pairwise spatial dependencies between landmarks, hence leading to a sparse shape model. The edges connecting different landmarks are defined in an automated way based on the intrinsic topology present in the training data. A maximum a posteriori approach is employed to obtain an energy function. To minimize this energy function, the problem is discretized by considering a finite set of candidate locations for each landmark, leading to a labeling problem. Using a leave-one-out approach on the training data the overall accuracy of the method is assessed. The mean and median error values for all landmarks are equal to 2.41 mm and 1.49 mm, respectively, demonstrating a clear improvement over previously published methods.