Rapid Coarse-to-Fine Matching Using Scale-Specific Priors

The Gibbs priors with potential equal to the membrane deflection and thin plate bending energies are explored in the Bayesian approach to image matching. Their smoothness properties are qualitatively demonstrated in a matching task. The priors are further evaluated by comparing their effect on the atlas-based localization of several subcortical structures in MRI data. Results of the localization study indicate that the implementation based on the membrane prior assumed over a fine mesh outperforms, both in speed and accuracy of the anatomic labeling, a plate-based approach that uses a comparable number of unknowns. Keywords: Image matching, Bayesian analysis, smoothness constraints, anatomic atlases, cerebral anatomy 1. INTRODUCTION Given two related images in the sense that they represent instances of the same scene, the image matching operation determines the transformation that maps each point in one image into its corresponding point in the other. Such inferences are of interest..

Retrospective registration of tomographic brain images

In modern clinical practice, the clinician can make use of a vast array of specialized imaging techniques supporting diagnosis and treatment. For various reasons, the same anatomy of one patient is sometimes imaged more than once, either using the same imaging apparatus (monomodal acquisition ), or different ones (multimodal acquisition). To make simultaneous use of the acquired images, it is often necessary to bring these images in registration, i.e., to align their anatomical coordinate systems. The problem of medical image registration as concerns human brain images is addressed in this thesis. The specific chapters include a survey of recent literature, CT/MR registration using mathematical image features (edges and ridges), monomodal SPECT registration, and CT/MR/SPECT/PET registration using image features extracted by the use of mathematical morphology