Glaucoma causes variations of the optic nerve head (ONH). We propose an approach to describe the inter subject variability of the ONH by dense deformation fields produced by non-rigid registration. Non-glaucoma but acquisition related variations are eliminated in a preprocessing step. Standard non-rigid registration was extended by a radial smoothing regularizer to preserve the ONH natural circ...

2 Introduction Image registration aims to spatially align one image to another and establish point-to-point correspondences between the images. It is an active and important topic of research in medical image analysis because of its numerous applications including change detection in longitudinal studies, multi-modal image fusion, motion correction in time series, image-guided intervention, ana...

Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for correspondences between two sets of features. In addition, there could be many features in either set that have no counterparts in the other. Th...

The amount of breast compression applied during a mammographic exam affects the appearance of mammograms by introducing variations in the shape, position, and contrast of breast anatomical structures, which can conceal existing breast abnormalities or generate false alarms. Due to the complex tissue organization and elastic properties of the breast and the projective nature of mammography, rigi...

Registration of 3D medical images consists in computing the “best” transformation between two acquisitions, or equivalently, determines the point to point correspondence between the images. Registration algorithms are usually based either on features extracted from the image (feature-based approaches) or on the optimization of a similarity measure of the images intensities (intensity-based or i...

We propose several cost functions for registration of shapes encoded with Euclidean and/or nonEuclidean information (unit vectors). Our framework is assessed for estimation of both rigid and non-rigid transformations between the target and model shapes corresponding to 2D contours and 3D surfaces. The experimental results obtained confirm that using the combination of a point’s position and uni...

This paper presents the application of a signal intensity independent registration criterion for non-rigid body registration of medical images. The criterion is defined as the weighted ratio image of two images. The ratio is computed on a voxel per voxel basis and weighting is performed by setting the ratios between signal and background voxels to a standard high value. The mean squared value o...

The purpose was to assess the variability in automated translational head/neck setup corrections computed from several different imaging modalities and rigid registration methods using patient anatomy. Shifts were calculated using three commercial and one in-house automated rigid registration methods for nine head/neck patients who were imaged with three different image-guidance systems. The me...

This paper describes a new method of non-rigid registration using the combined power of elastic and statistical shape models. The transformations are constrained to be consistent with a physical model of elasticity to maintain smoothness and continuity. A Bayesian formulation, based on this model, on an intensity similarity measure, and on statistical shape information embedded in corresponding...

The exact localization of intrahepatic vessels in relation to a tumour is an important issue in oncological liver surgery. For computer-assisted preoperative planning of surgical procedures high quality vessel models are required. In this work we show how to generate such models on the basis of registered CT or MRI data at different phases of contrast agent propagation. We combine well-establis...