Automatic localization of craniofacial landmarks using multi-layer perceptron as a function approximator

There are 20–30 visible landmarks in the lateral X-ray skull that are used by orthodontists in what is known as cephalometric evaluation. The evaluation assists in the diagnosis of anomalies and in the monitoring of treatments. Currently, this process is carried-out manually by outlining the soft and bonny tissues of the skull then locating the landmarks on line crossings. This can take an experienced orthodontist up to 20 min. The process is tedious, time consuming and subject to human error. In this paper, we propose a system for automatic localization of cephalometric landmarks using Multi-Layer Perceptron (MLP). Image processing techniques are utilized to extract features representing rotation, scale and distances from the outer edges of the skull image. Features from manually labeled images are used as inputs to train the MLP. After training, the MLP is used to estimate the location of the landmarks on targeted images based on knowledge obtained on the training stage. Results obtained by testing the algorithm on images which are not seen by the MLP during training, show an improvement over previously reported techniques.