Purpose. A new technique for analyzing the morphology of the corpus callosum is presented, and it is applied to a group of elderly subjects. Methods. The proposed approach normalizes subject data into the Talairach space using an elastic deformation transformation. The properties of this transformation are used as a quantitative description of the callosal shape with respect to the Talairach atlas, which is treated as a standard. In particular, a deformation function measures the enlargement/shrinkage associated with this elastic deformation. Inter-subject comparisons are made by comparing deformation functions. Results. This technique was applied to eight male and eight female subjects. Based on the average deformation functions of each group, the posterior region of the female corpus callosum was found to be larger than its corresponding region in the males. The average callosal shape of each group was also found, demonstrating visually the callosal shape differences between the tw...

Objective. The spatial normalization and registration of tomographic images from different subjects is a major problem in several medical imaging areas, including functional image analysis, morphometrics, and computer aided neurosurgery. The focus of this paper is the development of a computerized methodology for the spatial normalization of 3D images. Materials and Methods. We propose a technique which is based on geometric deformable models. In particular, we first describe a deformable surface algorithm which finds a mathematical representation of the outer cortical surface. Based on this representation, a procedure for obtaining a map between corresponding regions of the outer cortex in two different images is established. This map is subsequently used to derive a three-dimensional elastic warping transformation, which brings two images in register. Results. The performance of our algorithm is demonstrated on several datasets. In particular, we first test our deformable surface a...

Abstract. 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 boundary points, is employed to find a more accurate and robust non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach. It is shown that statistical boundary shape information significantly augments and improves elastic model based non-rigid registration. 1

This paper describes a method to automatically generate the mapping between a completely labeled reference image and the 3D medical image of a patient. Toachieve this, we combined three techniques: the extraction of 3D feature lines, their matching using 3D deformable line models, the extension of the deformation to the whole image space using warping techniques. We present experimental results for the segmentation of structures in Magnetic Resonance images of the brain of different patients; the segmentation of the cortical and ventricle structures. We emphasize the advantages of using crest lines deformable models prior to surface based models. This gives a sparser representation of the data, easier to manipulate, and which makes the convergence of the model much less sensitive to initial positionning. In the future, we hope to use this method to generate anatomical atlases, by the automatic interpretation of large sets of 3D medical images.

Image registration is a very important problem in computer vision and medical image processing. Numerous algorithms for registering multi-modal image data have been reported in these areas. Robustness as well as computational efficiency are prime factors of importance in image data registration. In this paper, a robust and efficient algorithm for estimating the transformation between two image data sets is presented. Estimating the registration between two image data sets is formulated as a motion estimation problem. We use an optical flow motion model which allows for both global as well as local motion between the data sets. In this hierarchical motion model, we represent the flow field with a B-spline basis which implicitly incorporates smoothness constraints on the field. In computing the motion, we minimize the expectation of the squared differences energy function numerically via a modified Newton iteration scheme. The main idea in the modified Newton method is that we precompute...

This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the rst method and those of viscous uids in the second, to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, an intensity similarity measure, and statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statist...

This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statisti...

A three-dimensional wavelet-based algorithm for nonlinear registration of an elastic body model of the brain is developed. Surfaces of external and internal anatomic brain structures are used to guide alignment. The deformation field is represented with a multiresolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. A progressive estimation of the registration parameters and the usage of an adaptive distance map reduce algorithm complexity, thereby providing computational flexibility that allows mapping of large, high resolution datasets. The performance of the algorithm was evaluated on rat brains. The wavelet-based registration method yielded a twofold improvement over affine registration.

A new algorithm has been developed in this thesis for the non-rigid point matching problem. Designed as an integrated framework, the algorithm jointly estimates a one-to-one correspondence and a non-rigid transformation between two sets of points. The resulting algorithm is called “robust point matching (RPM) algorithm ” because of its capability to tolerate noise and to reject possible outliers existed within the data points. The algorithm is built upon the heuristic of “fuzzy correspondence”, which allows for multiple partial cor-respondences between points. With the help of the deterministic annealing technique, this new heuristic enables the algorithm to overcome many local minima that can be encountered in the matching process. Devised as a general point matching framework, the algorithm can be easily extended to accommodate differ-ent speci£c requirements in many registration applications. Firstly, the modular design of the transformation module enables convenient incorporation of different non-rigid splines. Secondly, the point matching algorithm can be easily extended into a symmetric joint clustering-matching framework. It will be shown that by introducing a super point-set, the joint cluster-matching extension can be applied to estimate an average shape point-set from multiple point shape sets. The algorithm is applied to the registration of 3D brain anatomical structures. We proposed in this work a joint feature registration framework, which is mainly based on the joint clustering-matching extension of the robust

this paper a landmark-based optical flow interpolation scheme is proposed for image reconstruction of living aorta walls in two-photon autofluorescence image sequences. Landmarks are extracted and evaluated by an active contour-based aorta model, and are aligned and reconstructed by a hierarchical algorithm. The accuracy of the calculation of optical flow is improved by applying landmark-based image warping