Automatic Elastic Registration Research Articles

Elastic Registration Based on Compliance Analysis and Biomechanical Graph Matching.

An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is the fusion of pre-operative information onto the organ during surgery, to compensate for the limited details provided by the intra-operative imaging modality (e.g. cone beam CT) and to cope with changes in the shape of the organ. Due to image modalities differences and organ deformation, each graph has a different topology and shape. The adaptive compliance graph matching (ACGM) method presented does not require any manual initialization, handles intra-operative nonrigid deformations of up to 65mm and computes a complete displacement field over the organ from only the matched vasculature. ACGM is better than the previous biomechanical graph matching method (Garcia Guevara et al. IJCARS, 2018) (BGM) because it uses an efficient biomechanical vascularized liver model to compute the organ's transformation and the vessels bifurcations compliance. This allows to efficiently find the best graph matches with a novel compliance-based adaptive search. These contributions are evaluated on 10 realistic synthetic and 2 porcine automatically segmented datasets. ACGM obtains better target registration error (TRE) than BGM, with an average TRE in the real datasets of 4.2 mm compared to 6.5 mm, respectively. It also is up to one order of magnitude faster, less dependent on the parameters used and more robust to noise.

Biomechanics-based graph matching for augmented CT-CBCT.

Augmenting intraoperative cone beam computed tomography (CBCT) images with preoperative computed tomography data in the context of image-guided liver therapy is proposed. The expected benefit is an improved visualization of tumor(s), vascular system and other internal structures of interest. An automatic elastic registration based on matching of vascular trees extracted from both the preoperative and intraoperative images is presented. Although methods dedicated to nonrigid graph matching exist, they are not efficient when large intraoperative deformations of tissues occur, as is the case during the liver surgery. The contribution is an extension of the graph matching algorithm using Gaussian process regression (GPR)(Serradell et al. in IEEE Trans Pattern Anal Mach Intell 37(3):625-638, 2015): First, an improved GPR matching is introduced by imposing additional constraints during the matching when the number of hypothesis is large; like the original algorithm, this extended version does not require a manual initialization of matching. Second, a fast biomechanical model is employed to make the method capable of handling large deformations. The proposed automatic intraoperative augmentation is evaluated on both synthetic and real data. It is demonstrated that the algorithm is capable of handling large deformations, thus being more robust and reliable than previous approaches. Moreover, the time required to perform the elastic registration is compatible with the intraoperative navigation scenario. A biomechanics-based graph matching method, which can handle large deformations and augment intraoperative CBCT, is presented and evaluated.

Fuzzy Inference Mechanism Based Automatic Elastic Registration of Two-dimensional Gel Electrophoresis Images

Fuzzy Inference Mechanism Based Automatic Elastic Registration of Two-dimensional Gel Electrophoresis Images

Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations

We present an algorithm for automatic elastic registration of three-dimensional (3D) medical images. Our algorithm initially recovers the global spatial mismatch between the reference and floating images, followed by hierarchical octree-based subdivision of the reference image and independent registration of the floating image with the individual subvolumes of the reference image at each hierarchical level. Global as well as local registrations use the six-parameter full rigid-body transformation model and are based on maximization of normalized mutual information (NMI). To ensure robustness of the subvolume registration with low voxel counts, we calculate NMI using a combination of current and prior mutual histograms. To generate a smooth deformation field, we perform direct interpolation of six-parameter rigid-body subvolume transformations obtained at the last subdivision level. Our interpolation scheme involves scalar interpolation of the 3D translations and quaternion interpolation of the 3D rotational pose. We analyzed the performance of our algorithm through experiments involving registration of synthetically deformed computed tomography (CT) images. Our algorithm is general and can be applied to image pairs of any two modalities of most organs. We have demonstrated successful registration of clinical whole-body CT and positron emission tomography (PET) images using this algorithm. The registration accuracy for this application was evaluated, based on validation using expert-identified anatomical landmarks in 15 CT-PET image pairs. The algorithm's performance was comparable to the average accuracy observed for three expert-determined registrations in the same 15 image pairs.

Registration of serial transverse sections of muscle fibers

Background: Structural and functional characterization of skeletal muscles is often assessed by histochemical techniques, which enable the classification into different fiber types by combining the reactions in serial transverse muscle cross-sections. A drawback is that a knowledgeable operator is required to combine and evaluate the reactions, which is a time-consuming, tedious, and subjective task. To enhance the speed and reproducibility of muscle fiber typing, the registration of serial transverse sections of muscle fibers images has been proposed as a preprocessing step. Methods: Three different registration methods were considered: first, a semi-automatic elastic point-based registration; second, an automatic (rigid, affine, and projective) whole image content-based registration; and, third, an automatic hierarchical elastic registration obtained by integration of the first two methods. The performances of the methods were tested on a database of 50 image stacks each containing three images of histochemically differently stained serial human muscle cross-sections. Results: The amounts of successful globally and locally registered stacks were approximately 20% for automatic rigid registration, 60% for automatic affine and projective registrations, and 80% for semi-automatic and automatic elastic registration. Conclusions: By using robust elastic registration methods, the automatic registration of serial transverse muscle fiber images seems feasible and might allow automatic muscle fiber typing, and consequently, improve the characterization of skeletal muscles. Cytometry 37:93–106, 1999. © 1999 Wiley-Liss, Inc.