Locally Orderless Registration

Image registration is an important tool for medical image analysis and is used to bring images into the same reference frame by warping the coordinate field of one image, such that some similarity measure is minimized. We study similarity in image registration in the context of Locally Orderless Images (LOI), which is the natural way to study density estimates and reveals the 3 fundamental scales: the measurement scale, the intensity scale, and the integration scale. This paper has three main contributions: Firstly, we rephrase a large set of popular similarity measures into a common framework, which we refer to as Locally Orderless Registration, and which makes full use of the features of local histograms. Secondly, we extend the theoretical understanding of the local histograms. Thirdly, we use our framework to compare two state-of-the-art intensity density estimators for image registration: The Parzen Window (PW) and the Generalized Partial Volume (GPV), and we demonstrate their differences on a popular similarity measure, Normalized Mutual Information (NMI). We conclude, that complicated similarity measures such as NMI may be evaluated almost as fast as simple measures such as Sum of Squared Distances (SSD) regardless of the choice of PW and GPV. Also, GPV is an asymmetric measure, and PW is our preferred choice.Comment: submitte

Application of Generalized Partial Volume Estimation for Mutual Information based Registration of High Resolution SAR and Optical Imagery

Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance has been thoroughly explored for registration of high resolution SAR (TerraSAR-X) and optical (IKONOS-2) satellite images. Our experiments highlight possibility of an inconsistent MI behavior with different joint histogram bin size which gets reduced with an increase in order of B-spline kernel employed in GPVE. In general, bin size reduction and/or increasing B-spline order have a smoothing affect on MI surfaces and even the lowest order B-spline with a suitable histogram bin size can achieve same pixel level accuracy as achieved by the higher order kernels more consistently

Combined Mutual Information of Intensity and Gradient for Multi-modal Medical Image Registration

In this thesis, registration methods for multi-modal medical images are reviewed with mutual information-based methods discussed in detail. Since it was proposed, mutual information has gained intensive research and is getting very popular, however its robustness is questionable and may fail in some cases. The possible reason might be it does not consider the spatial information in the image pair. In order to improve this measure, the thesis proposes to use combined mutual information of intensity and gradient for multi-modal medical image registration. The proposed measure utilizes both the intensity and gradient information of an image pair. Maximization of this measure is assumed to correctly register an image pair. Optimization of the registration measure in a multi-dimensional space is another major issue in multi-modal medical image registration. The thesis first briefly reviews the commonly used optimization techniques and then discusses in detail the Powell\u27s conjugate direction set method, which is implemented to find the maximum of the combined mutual information of an image pair. In the experiment, we first register slice images scanned in a single patient in the same or different scanning sessions by the proposed method. Then 20 pairs of co-registered CT and PET slice images at three different resolutions are used to study the performance of the proposed measure and four other measures discussed in this thesis. Experimental results indicate that the proposed combined measure produces reliable registrations and it outperforms the intensity- and gradient-based measures at all three resolutions

3D nonrigid medical image registration using a new information theoretic measure.

International audienceThis work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy

Robust Algorithms for Registration of 3D Images of Human Brain

This thesis is concerned with the process of automatically aligning 3D medical images of human brain. It concentrates on rigid-body matching of Positron Emission Tomography images (PET) and Magnetic Resonance images (MR) within one patient and on non-linear matching of PET images of different patients. In recent years, mutual information has proved to be an excellent criterion for automatic registration of intra-individual images from different modalities. We propose and evaluate a method that combines a multi-resolution optimization of mutual information with an efficient segmentation of background voxels and a modified principal axes algorithm. We show that an acceleration factor of 6-7 can be achieved without loss of accuracy and that the method significantly reduces the rate of unsuccessful registrations. Emphasis was also laid on creation of an automatic registration system that could be used routinely in clinical environment. Non-linear registration tries to reduce the inter-individual variability of shape and structure between two brain images by deforming one image so that homologous regions in both images get aligned. It is an important step of many procedures in medical image processing and analysis. We present a novel algorithm for an automatic non-linear registration of PET images based on hierarchical volume subdivisions and local affine optimizations. It produces a C2-continuous deformation function and guarantees that the deformation is one-to-one. Performance of the algorithm was evaluated on more than 600 clinical PET images

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

Volume cardiac SPECT image registration

The registration of cardiac images is relevant for the diagnosis of cardiac pathologies. In the case of SPECT (Single Photon Emission Computer Tomography) datasets, this is difficult because of the low resolution of the images. In this paper we present a new registration method for 3D images with poor resolution. Our method is based on the maximization of the mutual information (MI) using a new interpolation method which significantly improves the result of an MI-based strategy on low resolution images. We also use a different optimization strategy than other MI-based algorithms, typically achieving errors of the order of one pixel. We also discuss experimental results of our method.Postprint (published version

Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets