Automation of Landmark Selection for Rodent Brainmri-histology Registration Using Thin-plate Splines

Image Registration Based on Thin-Plate Splines and Local Estimates of Anisotropic Landmark Localization Uncertainties

We present an approach to elastic registration of tomographic brain images which is based on thin plate splines and takes into account landmark errors The inclusion of error information is important in clin ical applications since landmark extraction is always prone to error In comparison to previous work our approach can cope with anisotropic er rors which is signi cantly more realistic than d...

Approximating Thin-Plate Splines for Elasti Registration: Integration of Landmark Errors and Orientation Attributes

TPS-HAMMER: Improving HAMMER registration algorithm by soft correspondence matching and thin-plate splines based deformation interpolation

We present an improved MR brain image registration algorithm, called TPS-HAMMER, which is based on the concepts of attribute vectors and hierarchical landmark selection scheme proposed in the highly successful HAMMER registration algorithm. We demonstrate that TPS-HAMMER algorithm yields better registration accuracy, robustness, and speed over HAMMER owing to (1) the employment of soft correspo...

Smart point landmark distribution for thin-plate splines

Landmark placement is crucial in manual demarcation and registration of anatomical structures, registration of different image modalities (i.e. MRI/CT), labeling training data for lip and face principal component models, training for neural networks, and signal interpolation to name some applications. Although landmark placement at curvature and coordinate extrema (e.g. corners of the mouth, lo...

Nonrigid Registration of Medical Images Based on Anatomical Point Landmarks and Approximating Thin-Plate Splines

Previous work on nonrigid registration of medical images based on thin plate splines has concentrated on using interpolation schemes Such schemes force the corresponding land marks to exactly match each other and thus assume that the landmark positions are known exactly However in real applications the localization of landmarks is always prone to error Therefore to cope with these errors we hav...