Investigation of filter sets for supervised pixel classification of cephalometric landmarks by spatial spectroscopy

Automatic landmarking of cephalograms using active appearance models

There have been many attempts to further improve and automate cephalometric analysis in order to increase accuracy, reduce errors due to subjectivity, and to provide more efficient use of clinicians' time. The aim of this research was to evaluate an automated system for landmarking of cephalograms based on the use of an active appearance model (AAM) that contains a statistical model of shape and grey-level appearance of an object of interest and represents both shape and texture variations of the region covered by the model. Multi-resolution implementation was used, in which the AAM iterate to convergence at each level before projecting the current solution to the next level of the model. The AAM system was trained using 60 randomly selected, hand-annotated digital cephalograms of subjects between 7.2 and 25.6 years of age, and tested with a leave-five-out method that enabled testing not only of the accuracy of the AAM system but also the accuracy of each AAM. Differences between methods were examined using the non-parametric Wilcoxon signed rank test. An average accuracy of 1.68 mm was obtained, with 61 per cent of landmarks detected within 2 mm and 95 per cent of landmarks detected within 5 mm precision. A noticeable increase in overall precision and detection of low-contrast cephalometric landmarks was achieved compared with other automated systems. These results suggest that the AAM approach can adequately represent the average shape and texture variations of craniofacial structures on digital radiographs. As such it can successfully be implemented for automatic localization of cephalometric landmarks.

Alignment of CT images of skull dysmorphology using anatomy-based perpendicular axes

Rigid body registration of 3D CT scans, based on manual identification of homologous landmarks, is useful for the visual analysis of skull dysmorphology. In this paper, a robust and simple alignment method was proposed to allow for the comparison of skull morphologies, within and between individuals with craniofacial anomalies, based on 3D CT scans, and the minimum number of anatomical landmarks, under rigidity and uniqueness constraints. Three perpendicular axes, extracted from anatomical landmarks, define the absolute coordinate system, through a rigid body transformation, to align multiple CT images for different patients and acquisition times. The accuracy of the alignment method depends on the accuracy of the localized landmarks and target points. The numerical simulation generalizes the accuracy requirements of the alignment method. Experiments using a human dried skull specimen, and ten sets of skull CT images (the pre- and post-operative CT scans of four plagiocephaly, and one fibrous dysplasia patients), demonstrated the feasibility of the technique in clinical practice.

Automatic computerized radiographic identification of cephalometric landmarks

Computerized cephalometric analysis currently requires manual identification of landmark locations. This process is time-consuming and limited in accuracy. The purpose of this study was to develop and test a novel method for automatic computer identification of cephalometric landmarks. Spatial spectroscopy (SS) is a computerized method that identifies image structure on the basis of a convolution of the image with a set of filters followed by a decision method using statistical pattern recognition techniques. By this method, characteristic features are used to recognize anatomic structures. This study compared manual identification on a computer monitor and the SS automatic method for landmark identification on minimum resolution images (0.16 cm2 per pixel). Minimum resolution (defined as the lowest resolution at which a cephalometric structure could be identified) was used to reduce computational time and memory requirements during this development stage of the SS method. Fifteen landmarks were selected on a set of 14 test images. The results showed no statistical difference (p > 0.05) in mean landmark identification errors between manual identification on the computer display and automatic identification using SS. We conclude that SS shows potential for the automatic detection of landmarks, which is an important step in the development of a completely automatic cephalometric analysis.

Investigation of filter sets for supervised pixel classification of cephalometric landmarks by spatial spectroscopy

The diagnostic process of orthodontics requires the analysis of a cephalometric radiograph. Image landmarks on this two-dimensional lateral projection image of the patient's head are manually identified and spatial relationships are evaluated. This method is very time consuming. A reliable method for automatic computer landmark identification does not exist. Spatial Spectroscopy is a proposed method of automatic landmark identification on cephalometric radiographs, that decomposes an image by convolving it with a set of filters followed by a statistical decision process. The purpose of this paper is to discuss and test appropriate filter sets for the application of Spatial Spectroscopy for automatic identification of cephalometric radiographic landmarks. This study evaluated two different filter sets with 15 landmarks on fourteen images. Spatial Spectroscopy was able to consistently locate landmarks on all 14 cephalometric radiographs tested. The mean landmark identification error of 0.841 +/- 1.253 pixels for a Multiscale Derivative filter set and 0.912 +/- 1.364 pixels for an Offset Gaussian filter set was not significantly different. Furthermore, there were no significant differences between identification of individual landmarks for the Multiscale Derivative and the Offset Gaussian filter set (P > 0.05). These results suggest that Spatial Spectroscopy may be useful in landmark identification tasks.