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Dentomaxillofacial Radiology (2005) 34, 205-211
© 2005 British Institute of Radiology
doi: 10.1259/dmfr/56357032

RESEARCH

Software for automated application of a reference-based method for a posteriori determination of the effective radiographic imaging geometry

RKW Schulze*,1, O Weinheimer2, DD Brüllmann3, F Röder1, B d'Hoedt1 and E Schoemer2

1 Department of Oral Surgery (and Oral Radiology), Johannes Gutenberg-University, Mainz, Germany; 2 Institute for Computer Science, Johannes Gutenberg-University, Mainz, Germany; 3 Department of Operative Dentistry, Johannes Gutenberg-University, Mainz, Germany

*Correspondence to: Dr Ralf Schulze, Poliklinik für Zahnärztliche Chirurgie, Augustusplatz 2, 55131 Mainz, Germany; E-mail: rschulze{at}mail.uni-mainz.de

Received 10 May 2004; revised 24 January 2005; accepted 24 March 2005

Objectives: Presentation and validation of software developed for automated and accurate application of a reference-based algorithm (reference sphere method: RSM) inferring the effective imaging geometry from quantitative radiographic image analysis.

Methods: The software uses modern pattern recognition and computer vision algorithms adapted for the particular application of automated detection of the reference sphere shadows (ellipses) with subpixel accuracy. It applies the RSM algorithm to the shadows detected, thereby providing three-dimensional Cartesian coordinates of the spheres. If the three sphere centres do not lie on one line, they uniquely determine the imaging geometry. Accuracy of the computed coordinates is investigated in a set of 28 charge-coupled device (CCD)-based radiographs of two human mandible segments produced on an optical bench. Each specimen contained three reference spheres (two different radii r1=1.5 mm, r2=2.5 mm). True sphere coordinates were assessed with a manually operated calliper. Software accuracy was investigated for a weighted and unweighted algebraic ellipse-fitting algorithm.

Results: The critical depth- (z-) coordinates revealed mean absolute errors ranging between 1.1±0.7 mm (unweighted version; r=2.5 mm) and 1.4±1.4 mm (weighted version, r=2.5 mm), corresponding to mean relative errors between 5% and 6%. Outliers resulted from complete circular dense structure superimposition and one obviously deformed reference sphere.

Conclusions: The software provides information fundamentally important for the image formation and geometric image registration, which is a crucial step for three-dimensional reconstruction from ≥2 two-dimensional views.

Keywords: radiography, dental, digital; technology, radiologic; image processing, computer-assisted






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