Small animal positron emission tomography (PET) image analysis can be particularly challenging with heavily-lesioned animal disease models with limited tracer uptake such as the 6-hydroxydopamine (OHDA) lesioned rat model of Parkinson's disease. Methodology-related variations in measured values of 10% or 15% can obscure meaningful biological differences, so accurate analysis methods are essential. However, placing regions of interest (ROIs) on these images without additional guidance is unreliable, and can lead to significant errors in results. To address this problem, this work develops a partly atlas-guided method place ROIs on structures that lack specific binding with presynaptic dopaminergic tracers. The method is tested by correlation of PET binding potential (BP) with autoradiographic binding measurements, and with repeated PET scans of the same subjects, both with the presynaptic tracer ¹¹C-dihydrotetrabenazine (DTBZ). The method is found to produce reliable results. When directly comparing PET images of the same subject to detect changes, it is essential to minimize variations due to analysis method. To this end, a masking method for automated image registration (AIR) of PET images with dopaminergic tracer rat images is developed. Coregistration with AIR and separate ROI placement are compared and tested with repeated scans of the same rat with DTBZ, and are found to be equivalent. Kinetic modelling algorithms may also introduce bias or scatter to binding potentials (BP) calculated from TACs or in parametric images. To determine the optimal method for this step, algorithms for dopaminergic tracers are compared for small animal DTBZ, ¹¹C-methylphenidate (MP), and ¹¹C-raclopride (Rac) data. Among the tested methods is a new variant of the Logan graphical kinetic modelling method, developed in this work, that issignificantly less biased by target tissue TAC noise than the standard Logan approach. The modified graphical method is further compared with the Logan graphical algorithms with added-noise simulations. The simplified reference tissue model (SRTM) is found to have the best method for ROI TAC data, while the modified graphical algorithm may be preferred when generating parametric images.

Performance evaluation of document recognition systems is a difficult and practically-important problem. In this thesis, we contribute to the understanding of performance evaluation by studying some issues that arise in evaluation of systems for recognition of mathematical expressions. Issues that are discussed cover the reported performance evaluation experiments, the code availability, the nature of the mathematical notation, the extent of the coverage of mathematical recognition systems, and the quantification of performance evaluation results. For each issue, we discuss its impact on performance evaluation, give an overview of the state of the art for addressing it and point out open problems. / Thesis (Master, Computing) -- Queen's University, 2008-05-21 15:34:21.966

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New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening.  It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that  produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions.  HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.

Secondly, we incorporate knowledge of the physiology, or how we expect the anatomy to change due to treatment. We can represent these changes using the Jacobian of the deformation, which describes the local size and type of change. This is used to regularise the registration, and can be incorporated simultaneously with the iterations of the registration. The final result is an accurate and robust registration result that is clinically useful for finding corresponding features on pre- and post-treatment datasets.

Because of the ubiquitous use of contact lenses, there is considerable interest in better understanding the anatomy of the cornea, the part of the eye in contact with an exterior lens. The recent technology developments in high resolution Optical Coherence Tomography (OCT) devices allows for the in-vivo observation of the structure of the human cornea in 3D and at cellular level resolution. Prolonged wear of contact lenses, inflammations, scarring and diseases can change the structure and physiology of the human cornea. OCT is capable of in-vivo, non-contact, 3D imaging of the human cornea. In this research, novel image processing algorithms were developed to process OCT images of the human cornea, in order to determine the corneal optical scattering and transmission. The algorithms were applied to OCT data sets acquired from multiple subjects before, during and after prolonged (3 hours) wear of soft contact lenses and eye patches, in order to investigate the changes in the corneal scattering associated with hypoxia. Results from this study demonstrate the ability of OCT to measure the optical scattering of corneal tissue and to monitor its changes resulting from external stress (hypoxia).

Data based abnormality detection is a growing research field focussed on extracting information from feature rich data. They are considered to be non-intrusive and non-destructive in nature which gives them a clear advantage over conventional methods. In this study, we explore different streams of data based anomalies detection. We propose extension and revisions to existing valve stiction detection algorithm supported with industrial case study. We also explored the area of image analysis and proposed a complete solution for Malaria diagnosis. The proposed method is tested over images provided by pathology laboratory at Alberta Health Service. We also address the robustness and practicality of the solution proposed. / Process Control

Thesis (M.S.) in Spatial Information Science and Engineering--University of Maine, 2006. / Includes vita. Includes bibliographical references (leaves 97-105).

Thesis (M. Sc.) Uppsala : Sveriges lantbruksuniv., 2007.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2005. / Härtill 8 uppsatser.