Multi-scale and multi-spectral shape analysis: from 2d to 3d

Shape analysis is a fundamental aspect of many problems in computer graphics and computer vision, including shape matching, shape registration, object recognition and classification. Since the SIFT achieves excellent matching results in 2D image domain, it inspires us to convert the 3D shape analysis to 2D image analysis using geometric maps. However, the major disadvantage of geometric maps is that it introduces inevitable, large distortions when mapping large, complex and topologically complicated surfaces to a canonical domain. It is demanded for the researchers to construct the scale space directly on the 3D shape. To address these research issues, in this dissertation, in order to find the multiscale processing for the 3D shape, we start with shape vector image diffusion framework using the geometric mapping. Subsequently, we investigate the shape spectrum field by introducing the implementation and application of Laplacian shape spectrum. In order to construct the scale space on 3D shape directly, we present a novel idea to solve the diffusion equation using the manifold harmonics in the spectral point of view. Not only confined on the mesh, by using the point-based manifold harmonics, we rigorously derive our solution from the diffusion equation which is the essential of the scale space processing on the manifold. Built upon the point-based manifold harmonics transform, we generalize the diffusion function directly on the point clouds to create the scale space. In virtue of the multiscale structure from the scale space, we can detect the feature points and construct the descriptor based on the local neighborhood. As a result, multiscale shape analysis directly on the 3D shape can be achieved

Geodesic tractography segmentation for directional medical image analysis

Acknowledgements page removed per author's request, 01/06/2014.Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes. This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patrici

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Large Model Visualization : Techniques and Applications

The size of datasets in scientific computing is rapidly increasing. This increase is caused by a boost of processing power in the past years, which in turn was invested in an increase of the accuracy and the size of the models. A similar trend enabled a significant improvement of medical scanners; more than 1000 slices of a resolution of 512x512 can be generated by modern scanners in daily practice. Even in computer-aided engineering typical models eas-ily contain several million polygons. Unfortunately, the data complexity is growing faster than the rendering performance of modern computer systems. This is not only due to the slower growing graphics performance of the graphics subsystems, but in particular because of the significantly slower growing memory bandwidth for the transfer of the geometry and image data from the main memory to the graphics accelerator. Large model visualization addresses this growing divide between data complexity and rendering performance. Most methods focus on the reduction of the geometric or pixel complexity, and hence also the memory bandwidth requirements are reduced. In this dissertation, we discuss new approaches from three different research areas. All approaches target at the reduction of the processing complexity to achieve an interactive visualization of large datasets. In the second part, we introduce applications of the presented ap-proaches. Specifically, we introduce the new VIVENDI system for the interactive virtual endoscopy and other applications from mechanical engineering, scientific computing, and architecture.The size of datasets in scientific computing is rapidly increasing. This increase is caused by a boost of processing power in the past years, which in turn was invested in an increase of the accuracy and the size of the models. A similar trend enabled a significant improvement of medical scanners; more than 1000 slices of a resolution of 512x512 can be generated by modern scanners in daily practice. Even in computer-aided engineering typical models eas-ily contain several million polygons. Unfortunately, the data complexity is growing faster than the rendering performance of modern computer systems. This is not only due to the slower growing graphics performance of the graphics subsystems, but in particular because of the significantly slower growing memory bandwidth for the transfer of the geometry and image data from the main memory to the graphics accelerator. Large model visualization addresses this growing divide between data complexity and rendering performance. Most methods focus on the reduction of the geometric or pixel complexity, and hence also the memory bandwidth requirements are reduced. In this dissertation, we discuss new approaches from three different research areas. All approaches target at the reduction of the processing complexity to achieve an interactive visualization of large datasets. In the second part, we introduce applications of the presented ap-proaches. Specifically, we introduce the new VIVENDI system for the interactive virtual endoscopy and other applications from mechanical engineering, scientific computing, and architecture

Seeing the Big Picture: System Architecture Trends in Endoscopy and LED-Based hyperspectral Subsystem Intergration

Early-stage colorectal lesions remain difficult to detect. Early development of neoplasia tends to be small (less than 10 mm) and flat and difficult to distinguish from surrounding mucosa. Additionally, optical diagnosis of neoplasia as benign or malignant is problematic. Low rates of detection of these lesions allow for continued growth in the colorectum and increased risk of cancer formation. Therefore, it is crucial to detect neoplasia and other non-neoplastic lesions to determine risk and guide future treatment. Technology for detection needs to enhance contrast of subtle tissue differences in the colorectum and track multiple biomarkers simultaneously. This work implements one such technology with the potential to achieve the desired multi-contrast outcome for endoscopic screenings: hyperspectral imaging. Traditional endoscopic imaging uses a white light source and a RGB detector to visualize the colorectum using reflected light. Hyperspectral imaging (HSI) acquires an image over a range of individual wavelength bands to create an image hypercube with a wavelength dimension much deeper and more sensitive than that of an RGB image. A hypercube can consist of reflectance or fluorescence (or both) spectra depending on the filtering optics involved. Prior studies using HSI in endoscopy have normally involved ex vivo tissues or xiv optics that created a trade-off between spatial resolution, spectral discrimination and temporal sampling. This dissertation describes the systems design of an alternative HSI endoscopic imaging technology that can provide high spatial resolution, high spectral distinction and video-rate acquisition in vivo. The hyperspectral endoscopic system consists of a novel spectral illumination source for image acquisition dependent on the fluorescence excitation (instead of emission). Therefore, this work represents a novel contribution to the field of endoscopy in combining excitation-scanning hyperspectral imaging and endoscopy. This dissertation describes: 1) systems architecture of the endoscopic system in review of previous iterations and theoretical next-generation options, 2) feasibility testing of a LED-based hyperspectral endoscope system and 3) another LED-based spectral illuminator on a microscope platform to test multi-spectral contrast imaging. The results of the architecture point towards an endoscopic system with more complex imaging and increased computational capabilities. The hyperspectral endoscope platform proved feasibility of a LED-based spectral light source with a multi-furcated solid light guide. Another LED-based design was tested successfully on a microscope platform with a dual mirror array similar to telescope designs. Both feasibility tests emphasized optimization of coupling optics and combining multiple diffuse light sources to a common output. These results should lead to enhanced imagery for endoscopic tissue discrimination and future optical diagnosis for routine colonoscopy