Wang, Tianming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 59-62). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2009. / Title from PDF title page (viewed on May 28, 2009). Includes bibliographical references.

Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature death, in the developed world. The early diagnosis and treatment of CVDs is therefore of great importance. Modern imaging modalities enable the quantification and analysis of the cardiovascular system and provide researchers and clinicians with valuable tools for the diagnosis and treatment of CVDs. In particular, echocardiography offers a number of advantages, compared to other imaging modalities, making it a prevalent tool for assessing cardiac morphology and function. However, cardiac ultrasound images can suffer from a range of artifacts reducing their image quality and diagnostic value. As a result, there is great interest in the development of processing techniques that address such limitations. This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac ultrasound data by utilising information which until now has been predominantly disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated instances of a cardiac cycle in order to acquire the information required to suppress or enhance certain image features. No filtering out of information is performed at any stage throughout the processing. This constitutes the main differentiation to previous data enhancement approaches which tend to filter out information based on some static or adaptive selection criteria. The first two image enhancement methods utilise spatial averaging of partially uncorrelated data acquired through a single acoustic window. More precisely, Temporal Compounding enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged structure acquired over a number of consecutive cardiac cycles. An extension to the notion of spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a novel image enhancement method by acquiring and compounding spatially adjacent (along the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by averaging decorrelated instances of the same cardiac structure, both compounding methods can enhance tissue structures, which are masked out by high levels of noise and shadowing, increasing their corresponding tissue/cavity detectability. The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity Mapping (DHBIM), investigates the temporal variations within image histograms of consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and (ii) derive the tissue/cavity intensity threshold within the processed frame sequence. Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM introduces cavity noise suppression, enhancement of tissue speckle information as well as considerable increase in tissue/cavity contrast and detectability. A data acquisition and analysis protocol for integrating the dynamic intensity mapping along with spatial compounding methods is also investigated. The linear integration of DHBIM and Temporal Compounding forms the fourth and final implemented method, which is also quantitatively assessed. By taking advantage of the benefits and compensating for the limitations of each individual method, the integrated method suppresses cavity noise and tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts. Finally, a novel protocol for the quantitative assessment of the effect of each data enhancement method on image quality and diagnostic value is employed. This enables the quantitative evaluation of each method as well as the comparison between individual methods using clinical data from 32 patients. Image quality is assessed using a range of quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability index. Diagnostic value is assessed through variations in the repeatability level of routine clinical measurements performed on patient cardiac ultrasound scans by two experienced echocardiographers. Commonly used clinical measures such as the wall thickness of the Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for assessing diagnostic value.

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

No description available.

Zhao, Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 111-117) and index. / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Objective of the study --- p.2 / Chapter 1.3 --- Challenges and our approach --- p.4 / Chapter 1.4 --- Original contributions --- p.6 / Chapter 1.5 --- Organization of this dissertation --- p.6 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Critical configuration for motion estimation and projective reconstruction --- p.9 / Chapter 2.1.1 --- Point feature --- p.9 / Chapter 2.1.2 --- Line feature --- p.12 / Chapter 2.2 --- Camera motion estimation --- p.14 / Chapter 2.2.1 --- Line tracking --- p.15 / Chapter 2.2.2 --- Determining camera motion --- p.19 / Chapter 3 --- Preliminaries on Three-View Geometry and Trifocal Tensor --- p.23 / Chapter 3.1 --- Projective spaces P3 and transformations --- p.23 / Chapter 3.2 --- The trifocal tensor --- p.24 / Chapter 3.3 --- Computation of the trifocal tensor-Normalized linear algorithm --- p.31 / Chapter 4 --- Linear Line Structures --- p.33 / Chapter 4.1 --- Models of line space --- p.33 / Chapter 4.2 --- Line structures --- p.35 / Chapter 4.2.1 --- Linear line space --- p.37 / Chapter 4.2.2 --- Ruled surface --- p.37 / Chapter 4.2.3 --- Line congruence --- p.38 / Chapter 4.2.4 --- Line complex --- p.38 / Chapter 5 --- Critical Configurations of Three Views Revealed by Line Correspondences --- p.41 / Chapter 5.1 --- Two-view degeneracy --- p.41 / Chapter 5.2 --- Three-view degeneracy --- p.42 / Chapter 5.2.1 --- Introduction --- p.42 / Chapter 5.2.2 --- Linear line space --- p.44 / Chapter 5.2.3 --- Linear ruled surface --- p.54 / Chapter 5.2.4 --- Linear line congruence --- p.55 / Chapter 5.2.5 --- Linear line complex --- p.57 / Chapter 5.3 --- Retrieving tensor in critical configurations --- p.60 / Chapter 5.4 --- Rank classification of non-linear line structures --- p.61 / Chapter 6 --- Camera Motion Estimation Framework --- p.63 / Chapter 6.1 --- Line extraction --- p.64 / Chapter 6.2 --- Line tracking --- p.65 / Chapter 6.2.1 --- Preliminary geometric tracking --- p.65 / Chapter 6.2.2 --- Experimental results --- p.69 / Chapter 6.3 --- Camera motion estimation framework using EKF --- p.71 / Chapter 7 --- Experimental Results --- p.75 / Chapter 7.1 --- Simulated data experiments --- p.75 / Chapter 7.2 --- Real data experiments --- p.76 / Chapter 7.2.1 --- Linear line space --- p.80 / Chapter 7.2.2 --- Linear ruled surface --- p.84 / Chapter 7.2.3 --- Linear line congruence --- p.84 / Chapter 7.2.4 --- Linear line complex --- p.91 / Chapter 7.3 --- Empirical observation: ruled plane for line transfer --- p.93 / Chapter 7.4 --- Simulation for non-linear line structures --- p.94 / Chapter 8 --- Conclusions and Future Work --- p.97 / Chapter 8.1 --- Summary --- p.97 / Chapter 8.2 --- Future work --- p.99 / Chapter A --- Notations --- p.101 / Chapter B --- Tensor --- p.103 / Chapter C --- Matrix Decomposition and Estimation Techniques --- p.104 / Chapter D --- MATLAB Files --- p.107 / Chapter D.1 --- Estimation matrix --- p.107 / Chapter D.2 --- Line transfer --- p.109 / Chapter D.3 --- Simulation --- p.109

This thesis introduces novel methods for estimating the poses of a reference plane from its reflections for specular surface recovery. Traditional methods for specular surface recovery usually introduce a second camera and an auxiliary calibration pattern to calibrate the poses of the reference plane with respect to the camera. The calibration procedures are comparatively tedious. Auto-estimating the poses of the reference plane is therefore an appealing problem. In the first part of this thesis, two novel and practical methods are proposed to recover the poses of a moving reference plane from its reflections produced on the specular surface given its initial position. As for the first approach, the reference plane is constrained to undergo an unknown pure translation. By observing the reflections of the moving reference plane produced on the specular surface, a closed form solution is derived for recovering the unknown translation. Degenerate cases in which the proposed method fails are studied. As for the second approach, the constraint on the motion of the reference plane is removed, and the reference plane can move freely. Again, a closed form solution is derived for the unknown motion defined by a rotation matrix and a translation vector, and the degenerate cases are also presented. The thesis finally considers the problem for auto-calibrating the reference plane against the camera for specular surface recovery without prior knowledge of the reference plane’s initial position. By only observing its reflections produced on the specular surface, the poses of the reference planes can be recovered in two steps. First, by applying a collinearity constraint to the specular correspondences, a simple closed form solution is derived for recovering the poses of the reference plane relative to its initial pose. Second, by applying a ray incidence constraint to the incident rays formed by the specular correspondences and the visual rays cast from the image, a closed form solution is derived for recovering the poses of the reference plane relative to the camera. The shape of the specular surface then follows. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Thesis (M.S.)--Rochester Institute of Technology, 1991. / Typescript. Includes bibliographical references (leaves 48-50).

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaf 51). Also available via the Internet.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 97-102).