Image Registration Using Robust Correlation

We have investigated an intensity-based image registration technique using a robust correlation coefficient as a similarity measure. The proposed method has an advantage over the ordinary correlation coefficient since it reduces the effect of "outlier" image intensity values. For the application of image registration to radiotherapy or image-guided surgery, there may be outlier samples due to the presence of the objects such as surgical instruments. We have verified the usefulness of the proposed method by simulation and phantom experiment.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86025/1/Fessler176.pd

Intensity-Based Image Registration Using Robust Correlation Coefficients

The ordinary sample correlation coefficient is a popular similarity measure for aligning images from the same or similar modalities. However, this measure can be sensitive to the presence of “outlier” objects that appear in one image but not the other, such as surgical instruments, the patient table, etc., which can lead to biased registrations. This paper describes an intensity-based image registration technique that uses a robust correlation coefficient as a similarity measure. Relative to the ordinary sample correlation coefficient, the proposed similarity measure reduces the influence of outliers. We also compared the performance of the proposed method with the mutual information- based method. The robust correlation-based method should be useful for image registration in radiotherapy (KeV to MeV X-ray images) and image-guided surgery applications. We have investigated the properties of the proposed method by theoretical analysis, computer simulations, a phantom experiment, and with functional magnetic resonance imaging data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85801/1/Fessler55.pd

A Solution to The Similarity Registration Problem of Volumetric Shapes

This paper provides a novel solution to the volumetric similarity registration problem usually encountered in statistical study of shapes and shape-based image segmentation. Shapes are implicitly representedby characteristic functions (CFs). By mapping shapes to a spherical coordinate system, shapes to be registered are projected to unit spheres and thus, rotation and scale parameters can be conveniently calculated.Translation parameter is computed using standard phase correlation technique. The method goes through intensive tests and is shown to be fast, robust to noise and initial poses, and suitable for a variety of similarity registration problems including shapes with complex structures and various topologies

Robust Phase-Correlation based Registration of Airborne Videos using Motion Estimation

This paper presents a robust algorithm for the registration of airborne video sequences with reference images from a different source (airborne or satellite), based on phase-correlation. Phase-correlations using Fourier-Melin Invariant (FMI) descriptors allow to retrieve the rigid transformation parameters in a fast and non-iterative way. The robustness to multi-sources images is improved by an enhanced image representation based on the gradient norm and the extrapolation of registration parameters between frames by motion estimation. A phase-correlation score, indicator of the registration quality, is introduced to regulate between motion estimation only and frame-toreference image registration. Our Robust Phase-Correlation registration algorithm using Motion Estimation (RPCME) is compared with state-of-the-art Mutual Information (MI) algorithm on two different airborne videos. RPCME algorithm registered most of the frames accurately, retrieving much better orientation than MI. Our algorithm shows robustness and good accuracy to multisource images with the advantage of being a direct (non-iterative) method

Affine registration of multispectral images of historical documents for optimized feature recovery

Multispectral (MSI) imaging of historical documents can recover lost features, such as text or drawings. This technique involves capturing multiple images of a document illuminated using different wavelengths of light. The images created must be registered in order to ensure optimal results are produced from any subsequent image processing techniques. However, the images may be misaligned due to the presence of optical elements such as filters, or because they were acquired at different times or because the images were captured from different copies of the documents . There is little prior work or information available about which image registration techniques are most appropriate. Image registration of multispectral images is challenging as the illumination changes for each image and the features visible in images captured at different wavelengths may not appear consistently throughout the image sequence. Here, we compare three image registration techniques: two based on similarity measures and a method based on phase correlation. These methods are characterized by applying them to realistic surrogate images and then assessed on three different sets of real multispectral images. Mutual information is recommended as a measure for affine image registration when working with multispectral images of documentary material as it was proven to be more robust than the other techniques tested

Registration of 3D Fetal Brain US and MRI

We propose a novel method for registration of 3D fetal brain ultrasound and a reconstructed magnetic resonance fetal brain volumes. The reconstructed MR volume is first segmented using a probabilistic atlas and an ultrasound-like image volume is simulated from the segmentation of the MR image. This ultrasound-like image volume is then affinely aligned with real ultrasound volumes of 27 fetal brains using a robust block-matching approach which can deal with intensity artefacts and missing features in ultrasound images. We show that this approach results in good overlap of four small structures. The average of the co-aligned US images shows good correlation with anatomy of the fetal brain as seen in the MR reconstruction

A protocol for registration and correction of multicolour STED superresolution images.

Multicolour fluorescence imaging by STimulated Emission Depletion (STED) superresolution microscopy with doughnut-shaped STED laser beams based on different wavelengths for each colour channel requires precise image registration. This is especially important when STED imaging is used for colocalisation studies of two or more native proteins in biological specimens to analyse nanometric subcellular spatial arrangements. We developed a robust postprocessing image registration protocol, with the aim to verify and ultimately optimise multicolour STED image quality. Importantly, this protocol will support any subsequent quantitative localisation analysis at nanometric scales. Henceforth, using an approach that registers each colour channel present during STED imaging individually, this protocol reliably corrects for optical aberrations and inadvertent sample drift. To achieve the latter goal, the protocol combines the experimental sample information, from corresponding STED and confocal images using the same optical beam path and setup, with that of an independent calibration sample. As a result, image registration is based on a strategy that maximises the cross-correlation between sequentially acquired images of the experimental sample, which are strategically combined by the protocol. We demonstrate the general applicability of the image registration protocol by co-staining of the ryanodine receptor calcium release channel in primary mouse cardiomyocytes. To validate this new approach, we identify user-friendly criteria, which - if fulfilled - support optimal image registration. In summary, we introduce a new method for image registration and rationally based postprocessing steps through a highly standardised protocol for multicolour STED imaging, which directly supports the reproducibility of protein co-localisation analyses. Although the reference protocol is discussed exemplarily for two-colour STED imaging, it can be readily expanded to three or more colours and STED channels

Registration of 3D fetal neurosonography and MRI.

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image

Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05)