Hans J Johnson | PubMed Health

PubMed2006

Synthesizing average 3D anatomical shapes.

Average shape estimates are often used to characterize normal morphological variation and discriminate dysmorphology in populations. The purpose of this paper is to estimate "average" or the most representative shapes in populations by using high-resolution medical images as input. The "average" shape is computed from high-dimensional spatial transformations used to co-register each subject in the population rather than the image intensities. Inverse consistent image registration is used to help minimize correspondence errors and produce better population average estimates. Testing the method was done using a population of adult MR brain scans from 22 individuals with no know structural abnormalities. Population averages were computed using the spatial transformation method and local changes in morphology were mapped. Results suggest that this method is a feasible means for robust estimation of population average shape. It is also shown that using inverse consistent transformations produces average shape estimates with less error compared to those produced with transformations with nontrivial inverse consistent errors.

Adult↗

PubMed2003

Subcortical, cerebellar, and magnetic resonance based consistent brain image registration.

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.

Adult↗