Algorithms for accurate 3D registration of neuronal images acquired by confocal scanning laser microscopy.
This paper presents automated and accurate algorithms based on high-order transformation models for registering three-dimensional (3D) confocal images of dye-injected neurons. The algorithms improve upon prior methods in several ways, and meet the more stringent image registration needs of applications such as two-view attenuation correction recently developed by us. First, they achieve high accuracy ( approximately 1.2 voxels, equivalent to 0.4 micro m) by using landmarks, rather than intensity correlations, and by using a high-dimensional affine and quadratic transformation model that accounts for 3D translation, rotation, non-isotropic scaling, modest curvature of field, distortions and mechanical inconsistencies introduced by the imaging system. Second, they use a hierarchy of models and iterative algorithms to eliminate potential instabilities. Third, they incorporate robust statistical methods to achieve accurate registration in the face of inaccurate and missing landmarks. Fourth, they are fully automated, even estimating the initial registration from the extracted landmarks. Finally, they are computationally efficient, taking less than a minute on a 900-MHz Pentium III computer for registering two images roughly 70 MB in size. The registration errors represent a combination of modelling, estimation, discretization and neuron tracing errors. Accurate 3D montaging is described; the algorithms have broader applicability to images of vasculature, and other structures with distinctive point, line and surface landmarks.