Thesis (Ph. D.)--University of Wisconsin--Madison, 1984. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 143-156).

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

A new method for the segmentation of 3D breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images, using parallel programming with general purpose computing on graphics processing units (GPGPUs), is proposed. The method has two main parts: a pre-processing step and a segmentation algorithm. In the pre-processing step, DCE-MRI images are registered using an intensity-based rigid transformation algorithm based on gradient descent. After the registration, voxels that correspond to breast lesions are enhanced using the Naïve Bayes machine learning classifier. This classifier is trained to identify four different classes inside breast images: lesion, normal tissue, chest and background. Training is performed by manually selecting 150 voxels for each of the four classes from images in which breast lesions have been confirmed by an expert in the field. Thirteen attributes obtained from the kinetic curves of the selected voxels are later used to train the classifier. Finally, the classifier is used to increase the intensity values of voxels labeled as lesions and to decrease the intensities of all other voxels. The post-processed images are used for volume segmentation of the breast lesions using a level set method based on the active contours without edges (ACWE) algorithm. The segmentation algorithm is implemented in OpenCL for the GPGPUs to accelerate the original model by parallelizing two main steps of the segmentation process: the computation of the signed distance function (SDF) and the evolution of the segmented curve. The proposed framework uses OpenGL to display the segmented volume in real time, allowing the physician to obtain immediate feedback on the current segmentation progress. The proposed implementation of the SDF is compared with an optimal implementation developed in Matlab and achieves speedups of 25 and 12 for 2D and 3D images, respectively. Moreover, the OpenCL implementation of the segmentation algorithm is compared with an optimal implementation of the narrow-band ACWE algorithm. Peak speedups of 55 and 40 are obtained for 2D and 3D images, respectively. The segmentation algorithm has been developed as open source software, with different versions for 2D and 3D images, and can be used in different areas of medical imaging as well as in areas within computer vision, such like tracking, robotics and navigation. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester 2015. / November 2, 2015. / GPU, Level Sets, OpenCL, OpenGL, Segmentation / Includes bibliographical references. / Anke Meyer-Baese, Professor Directing Dissertation; Mark Sussman, University Representative; Gordon Erlebacher, Committee Member; Dennis Slice, Committee Member; Xiaoqiang Wang, Committee Member.

Machine learning algorithms along with magnetic resonance imaging (MRI) provides promising techniques to overcome the drawbacks of the current clinical screening techniques. In this study the resting-state functional magnetic resonance imaging (fMRI) to see the level of activity in a patient's brain and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to explore the level of improvement of neo-adjuvant chemotherapy in patients with locally advanced breast cancer were considered. As the first project, we considered fMRI of patients before and after they underwent a double-blind smoking cessation treatment. For the first time, this study aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction in nicotine-dependent patients and future treatment efficacy. In this regards, two classes of patients have been studied, one took the drug N-acetylcysteine and the other took a placebo. Our goal was to classify the patients as treatment or non-treatment, based on their fMRI scans. The image slices of brain are used as the variable. We have applied different voxel selection schemes and data reduction algorithms on all images. Then, we compared several multivariate classifiers and deep learning algorithms and also investigated how the different data reductions affect classification performance. For the second part, we have employed multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this study. we determined the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death employing different machine learning algorithms. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2018. / July 6, 2018. / Breast Cancer, Data Mining, Machine Learning, Medical Imaging, Neuroimaging / Includes bibliographical references. / Anke Meyer-Baese, Professor Directing Dissertation; Simon Y. Foo, University Representative; Katja Pinker-Domenig, Committee Member; Peter Beerli, Committee Member; Dennis Slice, Committee Member.

Thesis (MTech (Radiography))--Peninsula Technikon, 2003. / Word processed copy. Summary in English. Includes bibliographical references. Also available online.

Thesis (M.D.S.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 69-85).

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

This thesis describes the development and applications of a new, high-speed, non-invasive, radiographic method by which the orientational and configurational characteristics of an internal human organ can be determined. The application of the method requires the organ to possess a radiologically recognizable contour seen simultaneously by two X-ray sources. / The method does not rely on the existence of anatomical landmarks or implanted artifacts, neither is any prior information about the general orientation of the contour necessary, nor does the operator need to have stereoscopic acuity. / In the presentation, first the developed algorithm is analyzed then the method is described in terms of general requirements and execution. Subsequent chapters deal with the sensitivity and accuracy considerations and the potential of the technique is demonstrated in a series of simulation experiments showing that the accuracy of the method is basically limited only by the digital process employed. / The practical feasibility of the method is demonstrated in a series of in-vivo experiments involving vertebrae of scoliotic and non-scoliotic subjects. The results of these experiments show that, in the case of vertebral bodies, such characteristics as inclination, torsion, wedge angle and true shape can be routinely determined with an accuracy of about 2 degrees angular and 0.2 cm linear.

Thesis (PhD) -- University of Canberra, 2006. / Thesis submitted in fulfilment of the requirements for the degree of Master of Information Science in the School of Information Sciences and Engineering under the Division of Business, Law and Sciences at the University of Canberra, May 2006. Bibliography: leaves 120-132.

Thesis (Ph. D.)--Lehigh University, 1997. / Includes vita. Includes bibliographical references (leaves 152-171).