A new landmark-initialized segmentation and intensity-based (LI-SI) inverse-consistent linear elastic image registration algorithm is presented. This method uses manually identified landmarks, segmented volumetric (anatomical) structures, and normalized image signal intensity information to coregister datasets. The features used for image registration and evaluation include 35 cortical, cerebellar, and commissure landmarks manually identified by experts, subcortical and cerebellar regions defined semi-automatically by an artificial neural network and manually trimmed for validity by experts, and tissue classified images that were generated using a discriminant analysis of three magnetic resonance image sets representing T1, T2, and PD modalities. Four groups of results were computed for coregistering 16 datasets with the following registration techniques: rigid registration, extended Talairach registration, intensity-only inverse-consistent linear elastic registration, and the new LI-SI registration. Results are presented showing that relative overlap measurements increased as the dimensionality of the registration algorithm and amount of anatomical information increased. The average relative overlap improved from 0.53 for the rigid registration to 0.55 for the Talairach registration to 0.74 for the intensity-only and to 0.85 for the LI-SI algorithm. We showed a statistically significant improvement for all but one structure using the intensity-only algorithm compared to the Talairach registration. Furthermore, statistically significant improvements for all structures were achieved using the LI-SI algorithm compared to the intensity-only algorithm.