A COMPUTERIZED SYSTEM FOR CEPHALOMETRIC SUPERIMPOSITION Kuo-sheng Cheng, Yen-ting Chen, and Jia-kuang Liu* Institute of Biomedical Engineering, National Cheng Kung University, Tainan, TAIWAN, ROC. *Department of Dentistry, National Cheng Kung University Hospital, Tainan, TAIWAN, ROC. E-mail:
[email protected] Abstract : A new system for automatic superimposition in cephalometry is developed. The distance measures-based curve matching and the fuzzy-based weighting function are combined to perform the feature superimposition with ambiguity. The results show the feasibility of the proposed technique. Keywords : feature matching, fuzzy logic, distance measures, cephalometry, landmark.
the highest average value.
III. RESULTS Fig.1 shows one resulting example for the superimposition in cranium base. The feature curves in the first tracing is drawn in black, and those of the second tracing in gray. The sella turcica of the two tracings are exactly matched, and the part of cranium base can be appropriately overlapped. It consists of the clinical requirements. The technique proposed in this study has been I. INTRODUCTION The procedure of superimposition in cephalometry is a also applied to other features such as the maxilla and the method to observe an overview of growth changes and to mandible based superimposition. evaluate the effects of orthodontics treatments on the jaws and teeth with the comparison of headfilms taken before and after treatment [1]. In practice, tracing papers representing the anatomical features from the headfilms are usually superimposed on their corresponding landmarks and the best fit of some specific features. Some subjective errors are inevitable in the manual superimposition with vision. Hence the computerized system with the technique of feature matching is developed for objective quantification of the superimposition. II. METHODS A. Images acquisition and landmarks identification The tracing papers are first digitized with desk-top scanner. The points of the required landmarks can be located on the screen without following a pre-defined order. The landmarks can be identified based on the estimation with the angle relationship between those points with the angular relationship of actual landmarks defined previously. With the distance measures, the located points can be identified and labeled to be the veritable landmarks. B. Feature matching The regions containing feature are obtained in reference to the defined relationship with corresponding landmarks. The processing as follows are sequentially performed in the feature regions: a). Line thinning and tracing: The morphology-based thinning method and chain codes are used for feature line thinning and tracing. b). Vector characterization: The chain codes of the above feature lines are resampled and represented as a sequence of vectors. c). Find the best match of two tracings: This feature matching is modified from the assessment of the curves matching in [2]. The fuzzy theory [3,4] is incorporated to define the varied degree of importance of features. The combined matching quality for two sets of vectors with distance and angle is evaluated. The superimposition of the two images with best orientation could be computed with
Fig.1 The resulting example of the superimposition in the cranium base of two tracings.
IV. SUMMARY Up to now, no automated cephalometry superimposition with the quantified matching quality is available in clinical use. The prototype system proposed in this paper is demonstrated to be feasible and have the potential for this application. However, it still needs for further investigation. * Acknowledge: This work was supported in part by the National Science Council, ROC, under the Grant NSC86-2745-B-006-001-M08. REFERENCES [1] A. Jacobson, Radiographic cephalometry, Chicago: Quintessence Publishing Co., 1995. [2] A. J. Pinho and L. B. Almeida, “Figures of merit for quality assessment of binary edge maps,” Proc. IEEE 3rd Int. Conf. Imag. Processing, pp. 343-349, 1996. [3] G. J. Klir and B. Yuan, Fuzzy sets and fuzzy logic, NJ: Prentice Hall, 1995. [4] R. Krishnapuram, J. M. Keller, and Y. Ma, “Quantitative analysis of properties and spatial relations of fuzzy image regions,” IEEE Trans. Fuzzy Syst., vol.1, no.3, pp. 222-233, 1993.
