Numerical oceanographic models are constantly improving and must be validated when improvements are made. One means of determining how to improve these models and performing validations is to compare model predictions to the future observed outcome, which is measured many ways, including satellite imagery. Comparisons of model forecasts to future satellite images result in error measurements. One common problem with modern oceanographic models is spatial error, i.e., the incorrect placement and shape of ocean features, rendering traditional error metrics such as mean-square and cross-correlation ineffective. Such problems are common in meteorological forecast verification as well, so the application of spatial error metrics have been a recently popular topic in that field of study. Spatial error metrics separate model error into a displacement component and an amplitude component, providing a more reliable assessment of numerical model inaccuracies and a more descriptive portrayal of model prediction skill.The application of spatial error metrics to oceanographic models has been sparse, and significantly further advances exist in the medical imaging and registration field. These advances are presented, along with modifications necessary for application to oceanographic model output and satellite imagery. Standard approaches and options for those methods in the literature are explored, and where the best arrangements of options are unclear, comparison studies are conducted. The first of these trials require the reproduction of synthetic displacements in conjunction with synthetic amplitude perturbations across 480 Navy Coastal Ocean Model (NCOM) temperature fields from various regions of the globe throughout 2009. Results revealed the success of certain approaches novel to both meteorology and oceanography, including B-spline transforms and mutual information. That, combined with other common methods, such as quasi-Newton optimization and land masking, could best recover the synthetic displacements under various synthetic intensity changes. The second set of trials compare temperature fields from NCOM and Navy Layered Ocean Model (NLOM), both 1/16-degree and 1/32-degree, to Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Lessons learned from the first trials were applied and extended. The resulting methods algorithmically reproduced portions of a previous hand-analyzed study and were successful in separating spatial from amplitude (temperature) errors.

Traditional deformable registration methods have achieved impressive performances but are computationally time-consuming since they have to optimize an objective function for each new pair of images. Very recently some learning-based approaches have been proposed to enable fast registration by learning to estimate the spatial transformation parameters directly from the input images. Here we present a method for 3D fast pairwise registration of brain MR images. We model the deformation function with B-splines and learn the optimal control points using a U-Net like CNN architecture. An inverse-consistency loss has been used to enforce diffeomorphicity of the deformation. The proposed algorithm does not require supervised information such as segmented labels but some can be used to help the registration process. We also implemented several strategies to account for the multi-resolution nature of the problem. The method has been evaluated on MICCAI 2012 brain MRI datasets, and evaluated on both similarity and invertibility of the computed transformation.

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State-of-the-art three-dimensional endo-luminal virtual colonoscopy (VC) or CT colonography (CTC) is a minimally invasive medical procedure that examines the entire colon in order to detect polyps and colorectal cancer. Most colon cancers malignantly transform from polyps, which are extra growths on the surface of the mucous membrane. Three dimensional endoscopic colon lumen interior images offered by CTC allow physicians to examine the colon interactively. Thus, CTC has several advantages over conventional optical colonoscopy including reduced risk. One of the challenging problems that prevent practical use in clinical situations is the complexity of the human colon. The colon's deformation by peristalsis and the diverse shapes of polyps make it difficult to distinguish polyps from other non-threatening entities in the colon. Hence, most CTC protocols acquire both prone and supine images to improve the visualization of the lumen wall, reduce false positives, and improve sensitivity. Comparisons between the prone and supine images can be facilitated by computerized registration between the scans. In this dissertation, two algorithms for registering colons segmented from prone and supine images are presented. First is an algorithm for three dimensional registration of the prone and supine colon when both are well distended and there is a single connected lumen. Second is another registration algorithm between colons with topological differences caused by inadequate bowel preparation or peristalsis. Such topological changes make deformable registrations of the colons difficult, and at present there are no registration algorithms which can accommodate them. The first algorithm uses feature matching of the colon centerline and a modified version of the demons deformable registration algorithm to define a deformation field between the prone and supine lumen surface. The second method utilizes embedded map representation of colon volume. The two proposed colon registration methods will contribute to improving the accuracy of the computerized registration process and increasing the versatility of the clinical use of CT colonoscopy. / Ph. D.

In medical images, intensity changes caused by certain pathology can change the topology of image level-sets and are thus commonly referred to as topological changes. Topological changes cause false deformation in existing deformable registration algorithms, which in turn leads to unreliable observations in the clinical study that relies on the deformation fields, such as deformation based morphometry (DBM). In this work, we develop a new deformable registration algorithm for images with topological changes. In our proposed algorithm, 3D images are embedded as 4D surfaces in a Riemannian space. The registration is therefore conducted as a surface evolution, which is modeled by a diffusion process. Our algorithm differs from existing methods in the sense that it takes an a-priori estimation of areas with topological change as an additional input and generates dense deformation vector fields which are free of false deformation. In particular, the output of our algorithm is composed of a diffeomorphic deformation field and an intensity displacement which corrects intensity difference caused by topological changes. By conducting multiple sets of experiments, we demonstrate that our proposed algorithm is capable of accurately registering images with considerable topological changes. More importantly, the resulting deformation field is not impacted by topological changes, i.e., there is no false deformation. / Ph. D.

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Du Xin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 67-68). / Abstracts in English and Chinese. / Abstract --- p.i-ii / Acknowledgement --- p.iii / Table of Contents --- p.iv-v / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background information and history of VRM auctions in Hong Kong --- p.2 / Chapter 1.2 --- VRM auctions in other countries --- p.6 / Chapter 1.3 --- Chinese numerology in brief --- p.8 / Chapter Chapter 2 --- "Data Set, literature review and models" --- p.11 / Chapter 2.1 --- Data Set --- p.11 / Chapter 2.2 --- Literature review --- p.13 / Chapter 2.3 --- Variables --- p.14 / Chapter 2.4 --- Model set-up --- p.18 / Chapter 2.4.1 --- Sub-sample estimation --- p.18 / Chapter 2.4.1.1 --- LENGTH as the segmentation criterion --- p.18 / Chapter 2.4.1.2 --- Price range as the segmentation criterion --- p.20 / Chapter 2.4.2 --- Taking logarithm to smooth the fluctuation --- p.22 / Chapter Chapter 3 --- Estimations of hedonic pricing models using the entire sample --- p.23 / Chapter 3.1 --- Estimation results for sub-samples using LENGTH as the segmentation criterion --- p.23 / Chapter 3.2 --- Estimation results for sub-samples using price range as the segmentation criterion --- p.29 / Chapter 3.2.1 --- Estimation of models for the first sub-sample --- p.30 / Chapter 3.2.2 --- Estimation of models for the second sub-sample --- p.33 / Chapter 3.2.3 --- Estimation of models for the third sub-sample --- p.36 / Chapter Chapter 4 --- Estimations of hedonic pricing models using the 99% sample --- p.42 / Chapter 4.1 --- Estimation results for sub-samples using LENGTH as the segmentation criterion --- p.42 / Chapter 4.2 --- Estimation results for sub-samples using price range as the segmentation criterion --- p.49 / Chapter 4.2.1 --- Estimation of models for the first sub-sample --- p.50 / Chapter 4.2.2 --- Estimation of models for the second sub-sample --- p.52 / Chapter 4.2.3 --- Estimation of models for the third sub-sample --- p.52 / Chapter Chapter 5 --- Estimations of hedonic pricing models for LNP --- p.60 / Chapter Chapter 6 --- Conclusion --- p.65 / References --- p.67 / Appendix A Normality test for LNP --- p.69

陳潔珊. / 書名中之「貯」「履」二字原為金文. / 論文(碩士) -- 香港中文大學硏究院中國語言及文學學部, 1997. / 參考文獻: leaves 281-302. / Chen Jieshan. / Chapter 第一章 --- 引言 --- p.1 / 第一章注釋 --- p.4 / Chapter 第二章 --- 金文中的「？」字 --- p.5 / Chapter (1) --- 金文中「？」字及「？田」的解釋 --- p.5 / Chapter (a) --- 「？」隸定爲「？」（「貯」）字 --- p.5 / Chapter (b) --- 「？田」即「租田」說 --- p.16 / Chapter (c) --- 「？田」即「賈田」說 --- p.26 / Chapter (d) --- 「？田」即「予田」或「舍田」說 --- p.41 / Chapter (e) --- 「？田」即「償田」說 --- p.46 / Chapter (f) --- 「？田」即「除田」說 --- p.47 / Chapter (g) --- 「？」、「？」義近說 --- p.48 / Chapter (2) --- 小結 --- p.50 / 第二章 注釋 --- p.53 / Chapter 第三章 --- 金文中的「舍」字 --- p.78 / Chapter (1) --- 金文中「舍」字及「舍田」的解釋 --- p.78 / Chapter (a) --- 「舍」即「賜予」說 --- p.78 / Chapter (b) --- 「舍田」即「予田」說 --- p.88 / Chapter (2） --- 小結 --- p.94 / 第三章 注釋 --- p.104 / Chapter 第四章 --- 金文中的「？」字 --- p.114 / Chapter (1) --- 《散氏盤》「？」字的解釋 --- p.114 / Chapter (a) --- 「？」即「眉」字說 --- p.114 / Chapter (i) --- 「？」」即「堳埒」的「堳」 --- p.114 / Chapter (ii) --- 「？」即周初微國 --- p.120 / Chapter (b) --- 「？」即「履」字說 --- p.123 / Chapter (2) --- 金文「履田」考 --- p.129 / Chapter (3) --- 小結 --- p.144 / 第四章 注釋 --- p.152 / Chapter 第五章 --- 金文中的「封」字 --- p.167 / Chapter (1) --- 金文中「封」字的解釋 --- p.167 / Chapter (2) --- 「封」、「邦」二字同源考 --- p.172 / Chapter (3) --- 金文中有關封疆劃界的記載 --- p.180 / Chapter (4) --- 小結 --- p.191 / 第五章 注釋 --- p.194 / Chapter 第六章 --- 「？田」、「舍田」、「履田」、「立封」所反映的西 周土地交換情況 --- p.201 / Chapter (1) --- 土地交換並非私下買賣 --- p.201 / Chapter (2) --- 土地交換的原因 --- p.207 / Chapter (3) --- 土地交換的程序 --- p.210 / Chapter (4) --- 小結 --- p.223 / 第六章 注釋 --- p.226 / Chapter 第七章 --- 總結 --- p.236 / 第七章 注釋 --- p.242 / 附錄 --- p.243 / 附錄一 本文引用銘文的拓本及釋文 --- p.243 / 附錄二 《毛公鼎》銘「？」字考釋 --- p.273 / 附錄三 《牆盤》銘「？」字考釋 --- p.276 / 參考書目 --- p.281