Automated cephalometric landmark localization using sparse shape and appearance models
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
| Hi-index | 0.00 |
In this paper a method is presented for the automated identification of cephalometric anatomical landmarks in craniofacial cone-beam CT images. This method makes use of statistical models, incorporating both local appearance and shape knowledge obtained from training data. Firstly, the local appearance model captures the local intensity pattern around each anatomical landmark in the image. Secondly, the shape model contains a local and a global component. The former improves the flexibility, whereas the latter improves the robustness of the algorithm. Using a leave-one-out approach to the training data, we assess the overall accuracy of the method. The mean and median error values for all landmarks are equal to 2.55mm and 1.72mm, respectively.