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Dentomaxillofacial Radiology, Vol 27, Issue 3 140-150, Copyright © 1998 by British Institute of Radiology

ARTICLES

Observer-independent registration of perspective projection prior to subtraction of in vivo radiographs

T. M. Lehmann, K. Grondahl, H. G. Grondahl, W. Schmitt and K. Spitzer
Institute of Medical Informatics, Aachen University of Technology (RWTH), Germany.

OBJECTIVES: To prove that the model of perspective projection allows precise registration of intra-oral radiographs regardless of whether they have been acquired with or without individual adjustment aids and independent of the human observer or computer algorithm marking corresponding landmarks in the images and, based on in vivo radiographs, to introduce and evaluate a model-based registration method. METHODS: Five observers (three experts and two non-experts) were asked to define corresponding points in 24 pairs of in vivo dental radiographs from the same region of the same patient. The landmarks were used to fit the model of perspective projection applying the least squares method. Misplaced landmarks were detected and suppressed by analysing the quality of all subsets of landmarks with respect to the minimal residual (leaving one out method). In addition, local correlation was used to optimize the quality of registration as well as observer independence. RESULTS: Using six or more corresponding landmarks in both radiographs the correlation of the images registered was > 0.95 (S.D. < 0.063) irrespective of the observers' expertise. CONCLUSIONS: Perspective projection is a reliable model for sequentially acquired intra-oral radiographs. The co-ordinates of anatomical landmarks are useful for determining the parameters of perspective projection. Local correlation and leaving one out techniques improve the geometrical adjustment as well as observer independence. Registration is nearly independent of the actual position of the landmarks and hence independent of the observer. Our algorithm will also be useful for registration techniques based on automatically detected landmarks.


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