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 > or =2 two-dimensional views.