Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.

Master of Science / Food Science Institute / David L. Wetzel / Grain processing and handling requires quality determinations to ensure wholesome products that meet or surpass legal standards and specifications required by the end consumer. Near infrared spectroscopy has proven to be a useful and versatile tool to enable grain processers to make adjustments as needed. Near infrared chemical imaging also provides spatial information within the image and relative composition of chemically distinct components within the product. The potential use of chemical imaging to determine extraneous material in bread baking quality flour was addressed. A specimen preparation technique was developed. Insect fragment spiked specimens were imaged to determine their imaging effectiveness for application near the allowable limit of insect fragment concentration. Imaging was achieved using indium antimonide array detection of diffusely reflected radiation. The detector array of 81,920 pixels collected radiation from an area of 30.72 mm by 38.4 mm with a pixel size of 120 µm². Spectra were collected simultaneously from each pixel without moving parts by scanning with a liquid crystal tunable filter. Partial least squares analysis of each pixel within the sample allowed a summation of the insect quantity. The chemical structural distinction of chitin in the high protein matrix of the insect residue was in contrast to the non-digested carbohydrate residue in the lesser protein matrix of the flour. The method developed provided a linear response for a concentration range from approximately half the allowable limit to twice the limit for two insects that commonly contaminate flour. For the two insects studied the slopes are comparable with a slight off-set over a practical working range, so that insect concentration can be determined independently of species recognition.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第17600号 / 工博第3759号 / 新制||工||1573(附属図書館) / 30366 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 濵地 格, 教授 松田 建児, 教授 秋吉 一成 / 学位規則第4条第1項該当

A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Electromagnetic radiation reflectance increases dramatically around 700 nm for vegetation. This increase in reflectance is known as the vegetation red edge. The NDVI (Normalized Difference Vegetation index) is an imaging technique for quantifying red edge contrast for the identification of vegetation. This imaging technique relies on reflectance values for radiation with wavelength equal to 680 nm and 830 nm. The imaging systems required to obtain this precise reflectance data are commonly space-based; limiting the use of this technique due to satellite availability and cost. This thesis presents a robust and inexpensive new terrestrial-based method for identifying the vegetation red edge. This new technique does not rely on precise wavelengths or narrow wavelength bands and instead applies the NDVI to the visible and NIR (near infrared) spectrums in toto. The measurement of vegetation fluorescence has also been explored, as it is indirectly related to the efficiency of photochemistry and heat dissipation and provides a relative method for determining vegetation health. The imaging methods presented in this thesis represent a unique solution for the real time monitoring of vegetation growth and senesces and the determination of qualitative vegetation health. A single, inexpensive system capable of field and greenhouse deployment has been developed. This system allows for the early detection of variations in plant growth and status, which will aid production of high quality horticultural crops. / Master of Science

<p> Oral cavity cancer (OCC) is a type of cancer of the lip, tongue, salivary glands and other sites in the mouth <i>(buccal or oral cavity)</i> and is the sixth leading cause of cancer worldwide. Patients with OCC are treated based on a staging system: low-stage patients typically receive less aggressive therapy compared to high-stage patients. Unfortunately, low-stage patients are sometimes at risk for locoregional recurrence. Recently, a semi-quantitative risk scoring system has been developed to assess the locoregional recurrence risk for low-stage patients. This risk scoring system is based on tissue characteristics determined on 2D histopathology images under a microscope. This modality limits the appreciation of the 3D architecture of the tumor and its associated morphological features. This thesis aims to visualize 3D models of the tumor-host interface reconstructed from serially-sectioned histopathology slides and quantify their clinically validated morphological features to predict locoregional recurrence after treatment. The 3D models are developed and quantified for 6 patient cases using readily available tools. This pilot study provides a framework for an automated diagnostic technique for 3D visualization and morphological analysis of tumor biology which is traditionally done using 2D analysis.</p>

No description available.

The search to directly image and characterize exoplanets that are initially hidden below the stellar and instrumental noise relies on the use of both extreme adaptive optics (AO) and a subsequent point spread function (PSF) subtraction pipeline. In this thesis I present my research on both real-time AO techniques and post-processing PSF subtraction techniques. First, I present a new PSF subtraction algorithm designed to image the HR 8799 debris disk using the Hubble Space Telescope. I find an over-luminosity after PSF subtraction that may be from the inner disk and/or planetesimal belt components of this system, but ultimately conclude that this is likely a non-detection as a result of telescope stability and broadband chromatic effects. Thus, assuming a non-detection, I derive upper limits on the HR 8799 dust belt mass in small grains, consistent with measurements of other debris disk halos. This important result suggests that a detection may not be possible until the rise of future, more stable space telescopes. Next, I present a new PSF subtraction algorithm applied to current campaign data from the Gemini Planet Imager (GPI), designed to optimize the GPI planet detection sensitivity of narrow orbit planets. My results, while still being investigated, seem to show that current algorithms are already optimized, and that limited gains can be achieved with my new algorithm. Finally, I apply a new real-time AO nulling technique, called super-Nyquist wavefront control (SNWFC), to be used on future 30 m class telescopes to image wide-orbit exoplanets. I demonstrate application of SNWFC in both a deterministic laboratory experiment and coronagraphic simulations using an interferometric nulling technique, suggesting that this technique would allow higher SNR characterization of wide-orbit exoplanets on future telescopes. / Graduate / 0606, 0752

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Brain operates on a network level. Magnetic resonance imaging (MRI) provides structural and functional images noninvasively with large field of view and at high spatial resolution and thus assumes an extremely valuable role in studying brain networks. The objectives of this doctoral work were to develop and apply novel MRI methods on human and rodent brains, for in vivo and global assessments of functional brain networks at resting and task-evoked states. Firstly, the feasibility of passband balanced steady-state free precession (bSSFP) imaging for distortion-free and high-resolution resting-state fMRI (rsfMRI) was investigated. Resting-state networks (RSNs) derived from bSSFP images were shown spatially and spectrally comparable to those derived from conventional gradient-echo echo-planar imaging (GE-EPI) with considerable intra- and inter-subject reproducibility. High-resolution bSSFP corresponded well to the anatomical images, with RSNs exquisitely co-localized to gray matter. Furthermore, RSNs at areas of severe susceptibility were proved accessible including human anterior prefrontal cortex and rat piriform cortex. These findings demonstrated for the first time that passband bSSFP approach can be a promising alternative to GE-EPI for rsfMRI. It offers distortion-free and high-resolution RSNs and is potentially suited for high field studies. Secondly, to examine the macrovascular contributions to the spatial and spectral prosperities of resting-state networks, spin-echo echo-planar imaging (SE-EPI) with moderate diffusion weighting (DW) was proposed for rsfMRI. SE and DW suppressed the extravascular and intravascular contributions from macrovessels respectively. Significantly lower functional connectivity strength was observed in the posterior cingulate cortex of the default mode network derived from DW SE-EPI data comparing to that derived from SE-EPI, suggesting a confounding role played by the intravascular component from large veins, whereas no significant spectral difference was detected. Therefore, the DW SEEPI approach for rsfMRI may assist in better identifying and interpreting largescale brain networks with future improvement in temporal resolution by acceleration techniques and in sensitivity at higher field. Thirdly, rsfMRI was performed to evaluate the intrinsic functional networks in the corresponding anatomical visual brain connections traced by Mn-enhanced MRI (MEMRI). Strengths of resting-state functional connectivity appeared to couple with structural connectivity in MEMRI, demonstrating the sensitivity of these structural and functional connectivity MRI techniques for assessing the neuroarchitecture, neurophysiology and structural-functional relationships in the visual brain in vivo. Fourthly, the hypothesis that a regional activation identified via general linear model analysis of fMRI data reflects the summation of multiple distinct networks that carry different functional purposes was tested. Overlapping frontoparietal networks engaged in a simple single-digit multiplication task were found and their functional roles were evaluated through independent components analysis and contributive source analysis. Future studies incorporating different arithmetic tasks and resting state will shed more light upon how brain accomplishes arithmetic and more complex tasks in general. Lastly, benefiting from higher SNR, better spatial and temporal resolution at higher field, exploratory fMRI studies were conducted on rats at 7 T for in vivo assessments of 1) the effect of dark-rearing on postnatal visual development, 2) sound amplitude modulations and 3) sound frequency modulation sweep direction selectivity in auditory system. ( / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy