3D registration of surfaces for change detection in medical images

Spatial registration of data sets is essential for quantifying changes that take place over time in cases where the position of a patient with respect to the sensor has been altered. Changes within the region of interest (e.g., biomaterial wear, tumor growth, or tissue swelling) can be problematic for automatic methods of registration. This research addresses the problem of automatic three-dimensional (3D) registration of surfaces derived from serial, single-modality images for the purpose of quantifying changes over time. The registration algorithm utilizes motion-invariant, curvature-based geometric properties to derive an approximation to an initial rigid transformation to align two image sets. Following the initial registration changed portions of the surface are detected and excluded before refining the transformation parameters. The performance of the algorithm was tested using simulation experiments. To quantitatively assess the registration, random noise at various levels, known rigid motion transformations, and analytically-defined volume changes were applied to the initial surface data acquired from models of teeth. These simulation experiments demonstrated that the calculated transformation parameters were accurate to within 1.2% of the total applied rotation and 2.9% of the total applied translation, even at the highest applied noise levels and simulated wear values.