Thesis (Ph. D.)--University of Iowa, 2007. / Thesis supervisor: Gary E. Christensen. Includes bibliographical references (leaves 159-173).

Thesis supervisor: Gary E. Christensen. Includes bibliographic references (p. 110-113).

Image registration is an important pre-processing operation to be performed before many image exploitation and processing functions such as data fusion, and super-resolution frame. Given two image frames, obtained from the same sensor or from different sensors, the registration problem involves determining the transformation that most nearly maps (or aligns) one image frame into the other. Typically, image registration requires intensive computational effort and the developed techniques are scene dependent. Furthermore, the problems of multimodal image registration (i.e. problem of registering images acquired from dissimilar sensors) and sub-pixel image registration (i.e. registering two images at sub-pixel accuracy) are highly challenging and no satisfactory solutions exist.This dissertation introduces novel techniques to solve the image registration problem both at the pixel-level and at the sub-pixel level. For pixel-level registration, a procedure is offered that enjoys the advantages that it is not scene dependent and provides the same level of accuracy for registering images acquired from different types of sensors. The new technique is based on obtaining the local frequency content of an image and using this local frequency representation to extract control points for establishing correspondence. To extract the local frequency representation of an image, a computationally efficient scheme based on minimizing the latency of a Gabor filter bank by exploiting certain biological considerations is presented. The dissertation also introduces an extension of using local frequency to solve the sub-pixel image registration problem. The new algorithm is based on using the scaled local frequency representation of the images to be registered, with computationally inexpensive scaling of the local frequency of the images prior to correlation matching. Finally, this dissertation provides a novel approach to solve the problem of multi-modal image registration. The principal idea behind this approach is to employ Computer Aided Design (CAD) models of man-made objects in the scene to permit extraction of regions-of-interest (ROI) whose local frequency representations are computed for extraction of stable matching points. Detailed performance evaluation results from an extensive set of experiments using diverse types of images are presented to highlight the strong points of the proposed registration algorithms.

No description available.

In certain neuro-degenerative diseases likemultiple sclerosis (MS), the rate of brain atrophy can be measured by monitoring the brain parenchymal fraction (BPF) in such patients. The BPF is defined as the ratio of brain parenchymal volume (BPV, defined as the total volume of gray matter tissue, white matter tissue and other unidentified tissue) and intracranial volume (ICV, the total volume of the skull). It can be represented by the formula in equation 1: <img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Csmall%20BPF%20=%20%5Cfrac%20%7BPBV%7D%7BICV%7D%20%5C;%20%5C;%20%5C;%20%5C;%20(1)" /> A complication with this measure is that the BPF is affected by the presence of edema in the brain, which leads to swelling and hence may obscure the true rate of brain atrophy. This leads to uncertainty when establishing “normal values” of BPF when analyzing different magnetic resonance imaging (MRI) scans of the same patient. Another problem is that different MRI scans of the same patient cannot be compared directly, due to the fact that the head of the patient will be in a different position for every scan. The SyMRI software used in this master thesis has the functionality to perform brain tissue characterization and measurement of brain volume, given a number of MR images of a patient. Using tissue properties such as longitudinal relaxation time (T1), transverse relaxation time (T2) and proton density (PD), each voxel in a volume can be classified to belong to a certain tissue type. From these measurements, the intracranial volume, brain volume, white matter, gray matter and cerebrospinal fluid volumes can easily be estimated. In this master thesis, the BPF of several patients were analyzed based on quantitative MRI (qMRI) images, in order to identify the change of BPF due to the presence of edema over time. Volumes obtained from the same patients at different time points were aligned (registered), such that the BPF can be easily compared between years. A correlation analysis between the BPF and R1, R2 and PD was performed (R1 is the longitudinal relaxation rate defined as 1/T1 relaxation time and R2 Is transverse relaxation rate defined as 1/T2 relaxation time) to investigate if any of these variables can explain the change in BPF. The results show that due to image registration, and removing some of the slices from the top and bottom of the head, the BPF of the patients was corrected to a certain extent. The change in the mean BPF of each patient over four years was less than 1% post registration and slice removal. However, the decrease in standard deviation was between 6.9% to 52% after registration and removing of slices. The BPF of the follow-up years also came closer to the initial BPF value measured in the first year. The statistical analysis of the BPF and R1, R2 and PD, showed a very low correlation (0.1) between BPF and PD, and intermediate correlations between BPF and R1, R2 (0.385 and -0.51, respectively). Future work will focus on understanding how these results relate to edema.

Xiong, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 56-61). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.6 / Chapter 2.1 --- Stereo matching --- p.6 / Chapter 2.2 --- Digital image matting --- p.8 / Chapter 2.3 --- Expectation Maximization --- p.9 / Chapter 3 --- Model Definition --- p.12 / Chapter 4 --- Initialization --- p.20 / Chapter 4.1 --- Initializing disparity --- p.20 / Chapter 4.2 --- Initializing alpha matte --- p.24 / Chapter 5 --- Optimization --- p.26 / Chapter 5.1 --- Expectation Step --- p.27 / Chapter 5.1.1 --- "Computing E((Pp(df = d1̐ưجθ(n),U))" --- p.28 / Chapter 5.1.2 --- "Computing E((Pp(db = d2̐ưجθ(n),U))" --- p.29 / Chapter 5.2 --- Maximization Step --- p.31 / Chapter 5.2.1 --- "Optimize α, given {F, B} fixed" --- p.34 / Chapter 5.2.2 --- "Optimize {F, B}, given α fixed" --- p.37 / Chapter 5.3 --- Computing Final Disparities --- p.40 / Chapter 6 --- Experiment Results --- p.42 / Chapter 7 --- Conclusion --- p.54 / Bibliography --- p.56

The advancements in medical technologies have allowed more accurate diagnosis and quantitative assessments. Magnetic Resonance Imaging is one of the most effective and critical technologies in modern diagnosis. However, preprocessing tasks are required to perform various research topics basing on MR image. Registration is one of the those preprocessing tasks. In this research, a semi-automatic utility was developed for doing MRI registration of small animals. It focuses on 2D rigid body registration. The test results show that this developed utility can perform registration well for MRI of small animals in both intra-subject and inter-subjects.

Thesis (M.S.)--University of Iowa, 2009. / Thesis supervisor: Punam K. Saha. Includes bibliographical references (leaves 45-48).

Most computational theories of stereopsis require a registration stage prior to stereo matching to reduce the matching to a one-dimensional search. Even after registration, it is critical that the stereo matching process tolerate some degree of residual misalignment. In this paper, we study with psychophysical techniques the tolerance to vertical disparity in situations in which false targets abound ?? in random dot stereograms ??d eye movements are eliminated. Our results show that small amounts of vertical disparity significantly impair depth discrimination in a forced-choice task. Our main results are: a) vertical disparity of only the central "figure" part of a random dot stereogram can be tolerated up to about 3.5', b) vertical disparity of the "figure + ground" is tolerated up to about 6.5', and c) the performance of the Grimson implementation of the Marr-Poggio stereo matching algorithm for the stereograms of experiment (a) is consistent with the psychophysical results. The algorithm's tolerance to vertical disparity is due exclusively to the spatial averaging of the underlying filters. The algorithm cannot account by itself for the results of experiment (b). Eye movements, which are the principal registration mechanism for human stereopsis, are accurate to within about 7'. Our data suggest that tolerance to this residual vertical disparity is attained by two non-motor mechanisms: 1) the spatial average performed by the receptive fields that filter the two images prior to stereo matching, and 2) a non-motor shift mechanism that may be driven at least in part by monocular cues.

Image Registration plays a very important role in the field of remote sensing. In order to have a better registration quality and make the automatization possible, choos ing and matching the control points from conjugate images become very important. In fact, the control points required for image registration should have following three key factors, that is, the amount, validity and distribution of control points. ¡@¡@In the study, we take QuickBird Satellite Images as the main ones; on the other hand, it conducts two groups of image registrations resulted from aerial images at various times. After detecting feature points using different algorithms, the study makes use of feature matching methods to get conjugate points between two overlapped images. The algorithms used above are SIFT, ASIFT and MESR. SIFT is an algorithm which invariant to scales, rotation, affine stretch and change in brightness. ASIFT undertakes simulations based on the theory of SIFT and thus carries out fully affine invariant. The feature points obtained from MSER have physical meaning in its location. By using feature matching algorithms like K-d tree and BBF, the matched feature points from two overlapped images would be turned into the conjugate points which can be control points for image registration. ¡@¡@During the process of image preprocessing, it is learned that the feature points detected by SIFT and MSER through feature matching are very few. Hence, this study attempts to employ histogram specification¡Bcontrast stretching and scale change methods to see if it is helpful to the feature detections and matching through change of image quality and image size. The experiment found that scale change will improve both the amount and accuracy of conjugate points detected by different algorithms. When considering distribution of the feature points, the study takes advantage of image cropping approach to conduct feature detections and matching individually. It is found that more conjugate points with uniform distribution can be obtained via image cropping technique.