Abstract
Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on manually locating specific landmarks, it is a tedious, time-consuming and error prone task. In this paper, we propose an automated system based on the use of Active Appearance Models (AAMs). Special attention has been paid to clinical validation of our method since previous work in this field used few images, was tested in the training set and/or did not take into account the variability of the images. In this research, a top-hat transformation was used to correct the intensity inhomogeneity of the radiographs generating a consistent training set that overcomes the above described drawbacks. The AAM was trained using 96 hand-annotated images and tested with a leave-one-out scheme obtaining an average accuracy of 2.48mm. Results show that AAM combined with mathematical morphology is the suitable method for clinical cephalometric applications.
Radiographs provided by Y. Perrin, A. Sanpietro and the Eastman Dental Institute.
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Keywords
- Mathematical Morphology
- Active Appearance Model
- Intensity Inhomogeneity
- Cephalometric Analysis
- Error Prone Task
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Rueda, S., Alcañiz, M. (2006). An Approach for the Automatic Cephalometric Landmark Detection Using Mathematical Morphology and Active Appearance Models. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866565_20
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DOI: https://doi.org/10.1007/11866565_20
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