@article {184, title = {Real time cardiac image registration during respiration: a time series prediction approach}, journal = {Journal of Real-time Image Processing}, year = {2011}, abstract = {

Cardiac image registration is drawing attention for a range of merits in integrating and enhancing real-time (RT) images using a priori and complementary images of the myocardium, which might additionally be captured from other modalities. Myocardial stem cell delivery and radio-frequency ablation are some of the cases that could benefit from RT registration of high quality images. Unfortunately, most of these applications are of long duration, and must account in some manner for respiratory motion. Moreover, registration is not so keen as to compensate for these motions. Time series prediction techniques could compensate this shortcoming by proposing future approximate displacements caused by respiratory motion. In this study, we propose a three-stage framework for RT 2D into 3D cardiac image registration during respiration, composed of prior registration to extract the trend of respiratory motion and to calibrate a set of time series predictors for future motion prediction, as well RT registration to update estimated transform parameters. The proposed approach was validated in the course of four simulations and shows acceptable results for clinical circumstances.

}, keywords = {Cardiac, image registration, MRI, Real-time, Respiratory motion compensation, Time series prediction}, author = {M Esteghamatian and Z Azimifar and P Radau and G Wright} } @article {218, title = {An adaptive Monte Carlo approach to phase-based multimodal image registration}, journal = {IEEE Transactions on Information Technology in Biomedicine}, volume = {14}, year = {2010}, pages = {173 - 179}, abstract = {

In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.

}, keywords = {adaptive Monte Carlo scheme, adaptive sampling approach, Algorithms, Computer-Assisted, Diagnostic Imaging, geometric transformation, Humans, image interpretation, image phase moments, image processing, image registration, iterative methods, Least-Squares Analysis, medical image processing, Monte Carlo Method, Monte Carlo methods, multidimensional sampling distribution, multidimensional solution space, multiresolution algorithm, optimal image alignment, Pearson type VII error, phase based multimodal image registration, random solution candidate generation, sampling methods}, issn = {1089-7771}, doi = {http://dx.doi.org/10.1109/TITB.2009.2035693}, author = {A Wong} } @article {270, title = {Fast phase-based registration of multimodal image data}, journal = {Signal Processing}, volume = {89}, year = {2010}, pages = {724 - 737}, abstract = {

An interesting problem in pattern recognition is that of image registration, which plays an important role in many vision-based recognition and motion analysis applications. Of particular interest among registration problems are multimodal registration problems, where the images exist in different feature spaces. State-of-the-art phased-based approaches to multimodal image registration methods have provided good accuracy but have high computational cost. This paper presents a fast phase-based approach to registering multimodal images for the purpose of initial coarse-grained registration. This is accomplished by simultaneously performing both globally exhaustive dynamic phase sub-cloud matching and polynomial feature space transformation estimation in the frequency domain using the fast Fourier transform (FFT). A multiscale phase-based feature extraction method is proposed that determines both the location and size of the dynamic sub-clouds being extracted. A simple outlier pruning based on resampling is used to remove false keypoint matches. The proposed phase-based approach to registration can be performed very efficiently without the need for initial estimates or equivalent keypoints from both images. Experimental results show that the proposed method can provide accuracies comparable to the state-of-the-art phase-based image registration methods for the purpose of initial coarse-grained registration while being much faster to compute.

}, keywords = {Dynamic sub-clouds, Fast Fourier Transform, image registration, Keypoints, Multimodal, phase}, issn = {0165-1684}, doi = {http://dx.doi.org/10.1016/j.sigpro.2008.10.028}, author = {A Wong and P Fieguth} } @inproceedings {289, title = {A novel algorithm for extraction of the layers of the cornea}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

Accurate corneal layer boundary extraction from optical coherence tomograms can provide precise layer thickness measurements required in the analysis of corneal disease. This paper establishes a novel approach to precisely obtain the five primary corneal layer boundaries. The proposed method determines correspondence relationships between the layer boundaries to facilitate robust boundary extraction in the presence of noise and artifacts. The first phase of the method applies morphological operators to enhance the prominent structural features of the cornea. The second phase uses a semi-automated segmentation algorithm to extract the upper and lower boundaries of the cornea; these boundaries are used to register the corneal image. The final phase extracts all five boundaries using a global optimization method exploiting the medial correspondence relationship between each layers. The proposed method is tested and verified using a representative set of optical coherence tomography images and compared against several state of the art methods. The proposed method is demonstrated to be more robust to noise, to provide more accurate segmentation results, and to require fewer user interactions than the other published methods.

}, keywords = {corneal disease, corneal image register, corneal layer boundary extraction, diseases, eye, Feature Extraction, global optimization method, image registration, image segmentation, layer thickness measurements, medical image processing, optical coherence tomography, optical tomography, optimisation, semiautomated segmentation algorithm, thickness measurement}, doi = {http://dx.doi.org/10.1109/CRV.2009.22}, author = {J A. Eichel and A Mishra and D A. Clausi and P Fieguth} } @inproceedings {327, title = {Automatic registration of inter-band and inter-sensor images using robust complex wavelet feature representations}, booktitle = {5th Workshop on Pattern Recognition for Remote Sensing}, year = {2008}, month = {12/2008}, address = {Tampa, Florida, USA}, abstract = {

A robust method for registering inter-band and inter-sensor remote sensing images has been designed and implemented. The proposed method introduces noise-resilient and contrast invariant control point detection and control point matching schemes based on robust complex wavelet feature representations. Furthermore, an iterative refinement scheme is introduced for achieving improved control point pair localization and mapping function estimation between the images being registered. The registration accuracy of the proposed method was demonstrated on the registration of multi-spectral optical and synthetic aperture radar (SAR) images. The proposed method achieves better registration accuracy when compared with the state-of-the-art MSSD and ARRSI registration algorithms.

}, keywords = {ARRSI, automatic registration, contrast invariant control point detection, control point matching schemes, control point pair localization, estimation theory, Feature Extraction, image matching, image registration, image representation, images registration, inter-band images, inter-sensor images, iterative methods, iterative refinement scheme, mapping function estimation, MSSD, multispectral optical images, registration algorithms, Remote Sensing, remote sensing images, robust complex wavelet feature representations, synthetic aperture radar, synthetic aperture radar images, wavelet transforms}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783164}, author = {A Wong and D A. Clausi and P Fieguth} } @article {317, title = {Efficient FFT-accelerated approach to invariant optical-LIDAR registration}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {46}, year = {2008}, pages = {3917 - 3925}, abstract = {

This paper presents a fast Fourier transform (FFT)-accelerated approach designed to handle many of the difficulties associated with the registration of optical and light detection and ranging (LIDAR) images. The proposed algorithm utilizes an exhaustive region correspondence search technique to determine the correspondence between regions of interest from the optical image with the LIDAR image over all translations for various rotations. The computational cost associated with exhaustive search is greatly reduced by exploiting the FFT. The substantial differences in intensity mappings between optical and LIDAR images are addressed through local feature mapping transformation optimization. Geometric distortions in the underlying images are dealt with through a geometric transformation estimation process that handles various transformations such as translation, rotation, scaling, shear, and perspective transformations. To account for mismatches caused by factors such as severe contrast differences, the proposed algorithm attempts to prune such outliers using the random sample consensus technique to improve registration accuracy. The proposed algorithm has been tested using various optical and LIDAR images and evaluated based on its registration accuracy. The results indicate that the proposed algorithm is suitable for the multimodal invariant registration of optical and LIDAR images.

}, keywords = {Fast Fourier Transform, fast Fourier transforms, feature mapping transformation optimization, FFT-accelerated approach, geometric distortions, geometric transformation estimation process, geophysical techniques, image registration, LIDAR images, light detection and ranging image registration, multimodal invariant registration, optical image registration, optical radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2008.2001685}, author = {A Wong and J Orchard} } @inproceedings {339, title = {Simultaneous multi-modal registration of multiple images based on multi-dimensional joint phase moment distributions}, booktitle = {19th International Conference on Pattern Recognition}, year = {2008}, abstract = {

In this paper, a novel method for simultaneously registering multiple images acquired from different imaging modalities is presented. The optimal alignment is computed as the set of transformations that minimize the dispersion of the multi-dimensional joint phase moment distribution. Dispersion is measured as the cumulative quadratic orthogonal distance between samples from the joint phase moment distribution and the corresponding multi-dimensional fitted hyperline. The proposed method is designed to be computationally efficient, robust to signal non-homogeneities and noise, and maintains internal consistency amongst all images. Experimental results using real-world medical and remote sensing images show that the proposed method achieves a high level of registration accuracy when simultaneously registering multiple multimodal images.

}, keywords = {cumulative quadratic orthogonal distance, image dispersion minimisation, image registration, imaging modality, minimisation, multidimensional fitted hyperline, multidimensional joint phase moment distribution, simultaneous multimodal multiple image registration}, doi = {http://dx.doi.org/10.1109/ICPR.2008.4761912}, author = {A Wong} } @article {355, title = {ARRSI: Automatic registration of remote-sensing images}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {45}, year = {2007}, pages = {1483 - 1493}, abstract = {

This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested

}, keywords = {ARRSI, Automatic Registration of Remote-Sensing Images, contrast issue, control point detection, image registration, intensity-difference issue, matching process, maximum distance sample consensus, Multimodal registration, performance evaluation, random sample and consensus algorithm, Remote Sensing, squared differences registration techniques}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2007.892601}, author = {A Wong and D A. Clausi} } @inproceedings {365, title = {Indirect knowledge-based approach to non-rigid multi-modal registration of medical images}, booktitle = {20th Canadian Conference on Electrical and Computer Engineering}, year = {2007}, abstract = {

Information acquired using different medical imaging techniques (e.g., MRI, PET, CT, etc.) can be combined to get a clear understanding of the overall condition of a patient for the purpose of diagnosis. Registering images from different modalities without a priori knowledge is difficult since the images may have very different intensity mappings and structural characteristics. This paper presents a novel approach to the multi-modal registration of medical images through the use of a priori knowledge to align medical images using an indirect mapping. The proposed algorithm uses stored information from successful alignment results to infer a relationship between the input images from different modalities. This relationship is then used to estimate the transformations needed to align the medical images together. Experimental results show that a high level of accuracy can be achieved using the proposed algorithm to align medical images from different modalities.

}, keywords = {biomedical MRI, CT, image registration, indirect knowledge-based approach, knowledge engineering, medical image processing, medical images, medical imaging techniques, MRI, nonrigid multimodal image registration, patient diagnosis, PET, positron emission tomography}, doi = {http://dx.doi.org/10.1109/CCECE.2007.298}, author = {A Wong and W Bishop} } @inproceedings {593, title = {Automatic registration of SAR and visible band remote sensing images}, booktitle = {Joint IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Remote Sensing Symposium}, year = {2002}, abstract = {

Image registration is one of the basic image processing operations in remote sensing. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for synthetic aperture radar (SAR) and optical remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching. The application of the developed automatic image registration model to SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.

}, keywords = {algorithm, edge extraction, edge linking pattern extraction, fully automatic registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, image registration, land surface, multi-source image, noise removal, optical imaging, pattern matching, radar imaging, radar remote sensing, Remote Sensing, remote sensing by radar, remote sensing image, SAR, sensor fusion, synthetic aperture radar, terrain mapping, visible band}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026106}, author = {M Ali and D A. Clausi} }