Thesis (M.S.)--University of Florida, 2002. / Title from title page of source document. Includes vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Chicago, Dept. of Radiology. / Includes bibliographical references. Also available on the Internet.

Thesis (M. Sc.)--University of Canterbury, 2006. / Typescript (photocopy). "Year: 2004-2006." Includes bibliographical references (leaves 108-112). Also available via the World Wide Web.

Thesis (MSc)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

M.Tech. (Radiography (Diagnostic)) / In order to meet the needs of the country South African radiographers need to become multi skilled radiographers. Empowering diagnostic radiographers with pattern recognition skills to enable them to comment on images could address the problem in many South African departments where a shortage of radiologists’ results in delayed reports or no reports to referring doctors. The research assessed the ability of qualified diagnostic radiographers in two Gauteng Government Hospital’s to apply pattern recognition and provide a comment on a radiograph after training in musculoskeletal pattern recognition. The study employed a pre- and post-test model with an intervention which comprised training of radiographers in musculoskeletal pattern recognition. The post- test results showed a significant improvement in the accuracy of identifying abnormal images and the comments provided were more complete than before training. Thus the intervention was successful in improving the ability of the radiographers to recognize normal and abnormal images, however, the training would need to be more extensive for an accurate comment to be provided on musculoskeletal images.

The shoulder is a complex anatomical structure and imaging plays an important role in the diagnosis and management of shoulder conditions. The complexity of the shoulder’s anatomy has led to the development of multiple radiographic projections and techniques within plain film imaging with each projection intended to demonstrate specific aspects of the anatomy of the shoulder. However, reproducing the required projections can be difficult especially if radiographers are not familiar with the projections and their evaluation criteria. Literature has revealed the importance of a comprehensive knowledge and understanding of anatomy, patient positioning, beam direction and centring point, and evaluation criteria to ensure a quality projection for accurate diagnoses. The aim of the study was to determine the knowledge and understanding of radiographers with regard to the supraspinatus outlet projection (SOP) for shoulder impingement syndrome (SIS) and its evaluation criteria. The inferences derived from the research findings were used to develop guidelines for a structured in-service training programme for practising radiographers to optimise their knowledge and understanding of the supraspinatus outlet projection in shoulder impingement syndrome. The proposed study followed a quantitative approach. Furthermore, a descriptive, exploratory, contextual design was employed. The research population consisted of practising radiographers working in the public and private hospitals of the Nelson Mandela Bay Municipality. The data were collected by means of a structured self-administered questionnaire. The questionnaire comprised of three sections. The first section requested demographic information from the participants. The second section assessed their knowledge and understanding regarding the scapular ‘Y’ and the supraspinatus outlet projections and shoulder impingement syndrome. The third section assessed their knowledge and understanding of anatomy and image evaluation/critiquing. The reliability and validity of the data collection instrument was ensured by conducting a pilot study and comparing the results with those of the main study. In addition, the expertise and guidance of a radiographer experienced in the clinical training of radiographers, the supervisor (who has twenty years’ experience in the teaching of radiographers) and a statistician was obtained. Descriptive and inferential statistical analyses were performed by means of a statistical programme and with the guidance of a statistician. The researcher ensured that the study was conducted in an ethical manner by adhering to the ethical principles of beneficence, justice and respect for persons.

Lung cancer has the highest mortality rate of all cancers in both men and women in the United States. The algorithmic detection, characterization, and diagnosis of abnormalities found in chest CT scan images can aid radiologists by providing additional medically-relevant information to consider in their assessment of medical images. Such algorithms, if robustly validated in clinical settings, carry the potential to improve the health of the general population. In this thesis, we first give an analysis of publicly available chest CT scan annotation data, in which we determine upper bounds on expected classification accuracy when certain radiological features are used as inputs to statistical learning algorithms for the purpose of inferring the likelihood of a lung nodule as being either malignant or benign. Second, a statistical extension of the level set method for image segmentation is introduced and applied to both synthetically-generated and real three-dimensional image volumes of lung nodules in chest CT scans, obtaining results comparable to the current state-of-the-art on the latter. / A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2018. / April 16, 2018. / computer-aided diagnosis, image segmentation, level set method, lung nodule, machine learning / Includes bibliographical references. / Jerry Magnan, Professor Directing Dissertation; Dennis Duke, University Representative; Monica Hurdal, Committee Member; Washington Mio, Committee Member.

Diffusion tensor imaging (DTI) provides a map of diffusional anisotropy based on the Brownian motion of water within the restricted boundaries of tissues. In this work, a high angular resolution DTI acquired with 18 diffusion directions and four unweighted images was acquired using a 11.75-T, 500-MHz MRI scanner located at the FAMU-FSU College of Engineering in order to perform tractography in amyloid precursor protein/presenilin 1 (APP/PS1) mouse models of familial Alzheimer’s Disease. Evaluating phenotype (APP/PS1 versus wild type) and age (1, 2, 4 and 6 months), a structural network analysis was employed to assess DTI datasets acquired at an in-plane resolution was 100 x 100 microns with a matrix size of 256 x 256, repetition time of 2 s, echo time of 30 ms, diffusion gradient separation of 21 ms and diffusion gradient time of 3 ms. With 15 averages, high signal-to-noise ratios were achieved over an approximate acquisition time of 47 h per sample. This study used all female brains fixed with 4% paraformaldehyde. The five main neural areas of focus were the piriform area of the cortex, temporal cortex, parietal cortex, and left and right hippocampus. A significant decrease in FA of the temporal cortex was identified. Changes in the network metrics of weighted degree, eccentricity, clustering, betweenness centrality, and closeness centrality were observed as a function of age and phenotype. / A Thesis submitted to the Department of Chemical and Biomedical Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester 2018. / April 20, 2018. / Alzheimer's Disease, DTI, Graph Theory, MRI / Includes bibliographical references. / Samuel C. Grant, Professor Directing Thesis; Jingjiao Guan, Committee Member; Yan Li, Committee Member.

With the increasing popularity of information technology, especially electronic imaging techniques, large amount of high dimensional data such as 3D shapes become pervasive in science, engineering and even people's daily life, in the recent years. Though the data quantity is huge, the extraction of relevant knowledge on those data is still limited. How to understand data in a meaningful way is generally an open problem. The specific challenges include finding adequate mathematical representations of data and designing proper algorithms to process them. The existing tools for analyzing high-dimensional data, including 3D shape data, are found to be insufficient as they usually suffer from many factors, such as misalignments, noise, and clutter. This thesis attempts to develop a framework for processing, analyzing and understanding high-dimensional data, especially 3D shapes, by proposing a set of statistical tools including theory, algorithms and optimization applied to practical problems. In particular, the following aspects of shape analysis are considered: 1. A framework adopting the SRNF representation, based on parallel transport of deformations across surfaces in the shape space, leads to statistical analysis on shape data. Three main analyses are conducted under this framework: (1) computing geodesics when either two end surfaces or the starting surface and an initial deformation are given; (2) parallel transporting deformation across surfaces; and (3) sampling random surfaces. 2. Computational efficiency plays an important role in performing statistical shape analysis on large datasets of 3D objects. To speed up the previous method, a framework with numerical solution is introduced by approximating the inverse mapping, and it reduces the computational cost by an order of magnitude. 3. The geometrical and morphological information, or their shapes, of 3D objects can be analyzed explicitly using boundaries extracted from original image scans. An alternative idea is to consider variability in shapes directly from their embedding images. A novel framework is proposed to unify three important tasks, registering, comparing and modeling images. 4. Finally, the spatial deformations learned from registering images are modeled using the GRID based decomposition. This specific model provides a way to decompose a large deformation into local and fundamental ones so that shape differences between images are easily interpretable. We conclude this thesis with conclusions drawn in this research and discuss potential future directions of statistical shape analysis in the last chapter, both from methodological and application aspects. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester, 2015. / March 20, 2015. / Parallel transport, Pattern Recognition, Registration, Riemannian geometry, shape analysis, Statistics / Includes bibliographical references. / Anuj Srivastava, Professor Directing Dissertation; Eric P. Klassen, University Representative; Fred W. Huffer, Committee Member; Wei Wu, Committee Member; Jinfeng Zhang, Committee Member.

Alzheimer's disease (AD) is one of the top 10 leading causes of death in the US; it debilitates memory and impairs cognition. The current core clinical criteria for diagnosis of AD are based on functional deficits and cognitive impairments that do not include the advanced imaging techniques or cerebrospinal fluid analysis; the final confirmation of the disease is only possible at the time of autopsy when neurofibrillary tangles and beta-amyloids are present in a brain tissue examination. The distributions of these particular pathogens (neurofibrillary tangles and beta-amyloids) follow a pattern that is useful to identify different stages of AD at the time of the autopsy by looking at the presence of pathogens in the areas of the brain. The pathogens are first seen in entorhinal/perirhinal cortex, and then spread to hippocampus cornu ammonis subfields, followed by association cortex and finally the rest of the brain. This disease progression is standard and described in NIA-RI guidelines. In the last decades, with the introduction of advanced imaging techniques in research settings, many in vivo based research methods have been focusing on the volumetric measurements of the hippocampus and its subfields in MRI images and using them as additional information for early diagnosis of AD. While the hippocampal volume provides excellent diagnostic aid, it doesn't address both the pathogens associated with AD and the progression of the pathogens within the different subregions of the hippocampus. The hippocampus formation is a complex circuit that spans the temporal lobes and found to have distinctive subregions. These subregions are subject to different influence by AD at different stages. Since the disease progression as seen in pathogen distributions follows a pattern, studying the pattern of the regional changes will allow us to predict which stage the disease is at. These pathological shifts in regions of the brain are studied extensively in ex vivo MRI as well as during autopsy but not in in vivo MRI. Considering that the brain areas with neurofibrillary tangles and beta-amyloids show hypointensity (PD-weighted) and hyperintensity voxels (T2-weighted) in the MRI images, we suggest an in vivo study using normalized MRI images taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We analyze the pattern of changes in the hippocampal region using a volume-based method in normalized T1-weighted MRI and an intensity-based analysis in normalized PD-weighted MRI. We use the volume-based method to calculate the changes in the volume of each of the hippocampus subfields. For the intensity-based method, we count the number of hypointensity (intensity value < 125) voxel combinations from the Gray-Level Co-occurrence Matrix (GLCM); a normalized MRI images are used inorder to minimize intensity variation. We then use the data (volume and intensity changes) to construct decision trees which classify the MRI images into three categories: normal control (NC), mild cognitive impairment (MCI) and AD. We have found that the volume-based decision trees detect AD MRI images with an accuracy of 75 % but failed to detect NC and MCI MRI images with the same level of accuracy. Whereas, with the intensity-based decision trees, we were able to classify MRI images into NC, MCI and AD categories each with an equally high level of accuracy (above 86 %). To find out how reliable the intensity-based method is in classifying MRI images, we introduced noises to our images. The addition of noises forced some adjustments in our decision trees. The accuracy of decision tree classification decreased in the presence of noises. However, even in the presence of the additional noises, we noticed that the intensity-based method outperforms volume-based method. The classification of MRI images improves when both measures (intensity-based and volume-based) are used in constructing our decision trees. This study has demonstrated that the inclusion of the intensity measurements of PD-weighted MRI images in AD studies may provide a more accurate way to model the natural progression of AD in vivo and contribute to the early diagnosis of AD. / A Dissertation submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2017. / April 7, 2017. / ADNI, Alzheimer Disease, GLCM, Hippocampus Subfield, Intensity, Multimodel / Includes bibliographical references. / Xiuwen Liu, Professor Directing Dissertation; Samuel Grant, University Representative; Gary Tyson, Committee Member; Piyush Kumar, Committee Member.