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. This outlier rejection problem further complicates an already di#cult correspondence problem. We formulate feature-based non-rigid registration as a non-rigid point matching problem. After a careful review of the problem and an in-depth examination of two types of methods previously designed for rigid robust point matching (RPM), we propose a new general framework for non-rigid point matching. We consider it a general framework because it does not depend on any particular form of spatial mapping. We have also developed an algorithm---the TPS-RPM algorithm---with the thin-plate spline (TPS) as the parameterization of the non-rigid spatial mapping and the softassign for the correspondence. The performance of the TPS-RPM algorithm is demonstrated and validated in a series of carefully designed synthetic experiments. In each of these experiments, an empirical comparison with the popular iterated closest point (ICP) algorithm is also provided. Finally, we apply the algorithm to the problem of non-rigid registration of cortical anatomical structures which is required in brain mapping. While these results are somewhat preliminary, they clearly demonstrate the applicability of our approach to real world tasks involving feature-based non-rigid registration.

We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local natural and artifactual differences between the autoradiograph slices. We have executed the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated it using synthetically generated spatial mappings and provided a visual comparison against the well known iterated closest point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.

This manuscript tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or di#erent modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that can be modeled as a number of rigid or a#ne local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Finally, the pairs of sub-images are, independently, a#nely registered and a hybrid a#ne/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using real biomedical images, including MRI, histology and cryosection data.

In order to allow accurate pre-operative localisation of functional taxgets in functional neurosurgery, we aim at constructing a three dimensional registrable caxtography of the basal ganglia, based on histology. For doing this, a post mortera MR study was conducted on a cadaver's head, and the brain was then extracted and processed for histology. The post mortera MR image will allow to report the caxtography on the patient's anatomy, by its registration with the patient's MR image. In this paper, we focus on the problem of co-registering the histological and post mortera MR data of the same subject. First, realignment of the histological sections into a reliable three dimensional volume is performed. Then the reconstructed volume is registered with the post mortera MR image. To insure three dimensional integrity of the histological reconstructed volume, a reference volume is first constructed from photographs of the unstained surface of the frozen brain. This reference is then used as an intermediate volume for, on the one hand, independant alignment of each histological section with its corresponding optical section and on the other hand, three dimensional registration with the post mortera MR image.

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

In this report, we present a new method of aligning histological sections. First a displacement field between the two images is computed by block matching. Then we estimate a rigid transformation based on the field. The process is integrated within a multi-scale scheme. We carefully study the problem of robustness and we propose several ideas to deal with intersection intensity differences and background artifacts. We demonstrate experimentally that we can reach a sub-voxel accuracy and we show some results on histological sections of a rat's brain and an endometrical adenocarcinoma.

In functional neurosurgery, there is a need for accurate locatisation of the functional targets. One example is given by Parkinson's disease. The surgical intervention is based on the introduction of electrodes in the subthalamic nucleus. This nucleus is targeted on pre-operative stereotactic MR acquisitions. But MR imaging of the basal ganglia is intrinsically limited, first by image resolution, and second by the relationship between the measured MR signal and the real anatomy, not clearly understood. On the other hand, detailed and accurate cartography of the basal ganglia can be performed on post mortem histological serial sections. Indeed, histology overcomes the limitations of MR imaging. Moreover, staining of histological sections allows to recover functional information. But histology is by nature two-dimensional. An histological data set consists in a series of disorganized serial sections, as three dimensional shape information was lost during sectioning. Therefore, the first step toward the integration of histological and MR information is to perform a reliable three dimensional reconstruction of the histological volume. Acquisition of photographs during sectioning, showing the histological sections before sectioning, as well as fiducial landmarks, allows to reconstruct a volume with three dimensional integrity, and is further used to register each histological section with its corresponding optical section.

affine registration of biological images for

Abstract. The recent development of 3-D medical imaging devices has given access to the 3-D imaging of in vivo tissues, from an anatomical (MR, CT) or even functional point of view (fMRI, PET, SPECT). However, the resolution of these images is still not sufficient to image anatomical or functional details, that can only be revealed by in vitro imaging (e.g. histology, autoradiography).The deep motivation of this work is the comparison of activations detected by fMRI series analysis to the ones that can be observed in autoradiographic images. The aim of the presented work is to fuse the autoradiographic data with the pre-mortem anatomical MR image, to facilitate the above-mentioned comparison. We show that this fusion can be achieved by using only simple global transformations (rigid and affine), yielding a very satisfactory result. 1

An accurate comparison of multimodal and/or interindividual 3D image datasets of brains requires geometric transformation techniques (warping) to reduce geometric variations. Here, a subset of warping techniques, namely point-based warping, is investigated. For this kind of warping landmarks between datasets have to be defined. In large 3D datasets manually setting of landmarks is timeconsuming and therefore impracticable. Consequently we approach this problem by investigating fast automatic procedures for determining landmarks, based on Monte-Carlo-techniques. The combined methods were tested on 3D autoradiographs of brains of gerbils. The results are evaluated by three different similarity functions. We found that the combined approach is highly applicable in processing brain images. 1. Introduction Warping is a class of algorithms for digital image processing which transform the shape of a source object towards that of a target object [1]. The shapetransformation is done by redefi...