Yoghurt is a fermented milk product which is produced by Streptococcus thermophilus and Lactobacillus delbrueckii spp. bulgaricus. The production of yoghurt has started in Middle East and spread all over the world. The aim of this study is to select the culture combination which is appropriate to Turkish taste and have the best yoghurt characteristics by means of post-acidification and whey separation properties, texture of gel formation, exopolysaccharide and acetaldehyde content / and to observe the effect of freeze-drying of cultures on these yoghurt properties. At the first part of this study, six L.delbrueckii spp. bulgaricus isolates and six S.thermophilus isolates were used with different combinations to produce 36 yoghurt samples. These isolates were selected among a strain collection which contains 111 L.delbrueckii spp. bulgaricus and 56 S.thermophilus isolates which were isolated from traditional Turkish yoghurt according to their acidification activity and acetaldehyde production properties. In addition, two commercial S.thermophilus isolates and one commercial L.delbrueckii spp. bulgaricus isolate were used to produce two commercial yoghurt samples. 38 yoghurt samples were examined in terms of pH and total titratable acidity changes during 21-day storage, syneresis and hardness. According to these three analyses, six yoghurt samples were chosen, which give the best results, for the determination of exopolysaccharide and acetaldehyde content. In addition, two yoghurt samples produced by commercial cultures and one sample, which gives average results in experiments, were also examined for these compounds to provide a good comparison. In the second part of the study the amount of exopolysaccharide and acetaldehyde of nine yoghurt samples were determined. In addition, sensory analysis was conducted to see consumer perception. According to the results, one culture combination was obtained as the best combination which produces the appropriate yoghurt to Turkish taste with the closest chemical analysis results to the commercial samples. In the last part, freeze drying process was examined if this has a significant effect on the selected LAB combination as well as yoghurt produced by using this.

Confocal microendoscopy is a promising new diagnostic imaging technique that is minimally invasive and provides in-vivo cellular-level images of tissue. In this study, we developed various image analysis techniques for ovarian cancer detection using the confocal microendoscope system. Firstly, we developed a technique for automatic classification of images based on focus, to prune out the out-of-focus images from the ovarian dataset. Secondly, we modified the texture analysis technique developed earlier to improve the stability of the textural features. The modified technique gives stable features and more consistent performance for ovarian cancer detection. Although confocal microendoscopy provides cellular-level resolution, it is limited by a small field of view. We present a fast technique for stitching the individual frames of the tissue to form a large mosaic. Such a mosaic will aid the physician in diagnosis, and also makes quantitative and statistical analysis possible on a larger field of view.

Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. It both serves as a routine diagnostic technique as well as an essential tool for detecting infectious agents in new and unusual disease outbreaks. The technique does not depend on virus specific targets and can therefore detect any virus present in the sample. New or reemerging viruses can be detected in EM images while being unrecognizable by molecular methods. One problem with diagnostic EM is its high dependency on experts performing the analysis. Another problematic circumstance is that the EM facilities capable of handling the most dangerous pathogens are few, and decreasing in number. This thesis addresses these shortcomings with diagnostic EM by proposing image analysis methods mimicking the actions of an expert operating the microscope. The methods cover strategies for automatic image acquisition, segmentation of possible virus particles, as well as methods for extracting characteristic properties from the particles enabling virus identification. One discriminative property of viruses is their surface morphology or texture in the EM images. Describing texture in digital images is an important part of this thesis. Viruses show up in an arbitrary orientation in the TEM images, making rotation invariant texture description important. Rotation invariance and noise robustness are evaluated for several texture descriptors in the thesis. Three new texture datasets are introduced to facilitate these evaluations. Invariant features and generalization performance in texture recognition are also addressed in a more general context. The work presented in this thesis has been part of the project Panvirshield, aiming for an automatic diagnostic system for viral pathogens using EM. The work is also part of the miniTEM project where a new desktop low-voltage electron microscope is developed with the aspiration to become an easy to use system reaching high levels of automation for clinical tissue sections, viruses and other nano-sized particles.

In our attempt to construct a semantic filter for satellite image content, we have built a software that allows user to indicate a few number of image regions that contains a specific geographical object, such as, a bridge, and to retrieve similar objects on the same satellite image. We are particularly interested in performing a data analysis approach based on user interaction. User can guide the classification procedure by interaction and visual observation of the results. We have applied a two step procedure for this and preliminary results show that we eliminate many true negatives while keeping most of the true positives.

Multiple Sclerosis (MS) is an autoimmune disease of central nervous system. It may result in a variety of symptoms from blurred vision to severe muscle weakness and degradation, depending on the affected regions in brain. To better understand this disease and to quantify its evolution, magnetic resonance imaging (MRI) is increasingly used nowadays. Manual delineation of MS lesions in MR images by human expert is time-consuming, subjective, and prone to inter-expert variability. Therefore, automatic segmentation is needed as an alternative to manual segmentation. However, the progression of the MS lesions shows considerable variability and MS lesions present temporal changes in shape, location, and area between patients and even for the same patient, which renders the automatic segmentation of MS lesions a challenging problem. In this dissertation, a set of segmentation pipelines are proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. These techniques use a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional views segmentation to produce verified segmentation. The multi-sectional views pipeline is customized to provide better segmentation performance and to benefit from the properties and the nature of MS lesion in MRI. These customization and enhancement leads to development of the customized MV-T-SVM. The MRI datasets that were used in the evaluation of the proposed pipelines are simulated MRI datasets (3 subjects) generated using the McGill University BrainWeb MRI Simulator, real datasets (51 subjects) publicly available at the workshop of MS Lesion Segmentation Challenge 2008 and real MRI datasets (10 subjects) for MS subjects acquired at the University of Miami. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

Texture analysis is an important problem in machine vision, with applications in many fields including medical imaging, remote sensing (SAR), automated flaw detection in various products, and document analysis to name but a few. Over the last four decades many techniques for the analysis of textured images have been proposed in the literature for the purposes of classification, segmentation, synthesis and compression. Such approaches include analysis the properties of individual texture elements, using statistical features obtained from the grey-level values of the image itself, random field models, and multichannel filtering. The wavelet transform, a unified framework for the multiresolution decomposition of signals, falls into this final category, and allows a texture to be examined in a number of resolutions whilst maintaining spatial resolution. This thesis explores the use of the wavelet transform to the specific task of texture classification and proposes a number of improvements to existing techniques, both in the area of feature extraction and classifier design. By applying a nonlinear transform to the wavelet coefficients, a better characterisation can be obtained for many natural textures, leading to increased classification performance when using first and second order statistics of these coefficients as features. In the area of classifier design, a combination of an optimal discriminate function and a non-parametric Gaussian mixture model classifier is shown to experimentally outperform other classifier configurations. By modelling the relationships between neighbouring bands of the wavelet trans- form, more information regarding a texture can be obtained. Using such a representation, an efficient algorithm for the searching and retrieval of textured images from a database is proposed, as well as a novel set of features for texture classification. These features are experimentally shown to outperform features proposed in the literature, as well as provide increased robustness to small changes in scale. Determining the script and language of a printed document is an important task in the field of document processing. In the final part of this thesis, the use of texture analysis techniques to accomplish these tasks is investigated. Using maximum a posterior (MAP) adaptation, prior information regarding the nature of script images can be used to increase the accuracy of these methods. Novel techniques for estimating the skew of such documents, normalising text block prior to extraction of texture features and accurately classifying multiple fonts are also presented.

In response to recent declines, forested riparian wetland areas have become an increased conservation and management area of concern focusing on increasing biodiversity and promoting healthy ecosystem services. Additionally, passerine birds have also experienced a sharp global decline in that associated habitat. To mitigate further declines of both habitat and species numbers government programs and agencies have intensified conservation efforts. However, the practices employed are often assumed to be beneficial without conducting dedicated surveys to measure efficacy and practicality of current approaches. As such, visual evidence and statistics are often needed to promote or validate further support and funding for continuing with current polices or creating new focal areas and practices. This study strives to provide an inexpensive, efficient way to assess conservation areas based on a target species through a generalized and adaptive methodology. The Cypress Creek National Wildlife Refuge in southern Illinois provides an opportunity to do just that with a focus on songbirds. The methodology outlined in this study implements multiple remote sensing land use and land cover classification techniques utilizing Landsat imagery from 1984 to 2014 to create a temporal analysis of the region from pre-refuge era to current refuge designated era. Field surveys from the 2015 songbird summer breeding and fall migration seasons as well as vegetation surveys for field-truthing supplement the remote sensing results. The classification methodology incudes a combination of pan-sharpening Landsat images to a 15 m x 15 m spatial resolution, texture analysis, object based image analysis, and Random Forests to produce land use and land cover maps. For the sake of comparison the same classification process is performed with the untransformed, source images at 30 m x 30 m spatial resolution. Landscape metrics such as the interspersion and juxtaposition index and the contiguity index also provide further insight to temporal landscape patterns. At the completion of the study it was found that there was a minimal difference between the overall classification accuracy of transformed and untransformed images and that lowest overall accuracy in the study was 91% while the highest was 98%. The key survey statistics concluded that during the summer and fall observation periods songbirds in forested wetland areas had a propensity to utilize areas closest to the wetland edge as opposed to inland areas. Furthermore, during fall migration it was concluded that the mixed forest habitat type had a direct effect on observation numbers. Overall, with the aid of multiple landscape metrics, it was shown that the region was increasing in forested area, patch density, and contiguity; in response the passerines were using the area at a high rate, especially near wetland edges creating a sustainable focal area for conservation and management. The methodology and results in this study contribute to an ongoing effort to provide visual and statistical evidence that is reliable and accessible for policy making. The potential to manipulate the generalized methods used in this study to enhance any land use and land class classifications and apply to any targeted species certainly exists. Future studies will want to investigate the use of higher spatial resolution images or actively take reflectance recordings in the field and supplement the temporal maps with a multi-year dedicated species dataset for maximum benefit.

East Africa is central to many aspects of human evolution and diversification. At the same time, diet is a key aspect of the ecology of any population. Therefore, one is often interested in the diets of past populations. To assess human diet in the past, stable isotope ratio and dental microwear analyses are often perceived as the only semi-quantitative and objective techniques. However, there are still many unknowns on how isotopic and microwear signals change in response to dietary variation, because few controlled studies have been carried out in modern populations. To investigate this issue, this study targeted living humans from African ethnic groups (El Molo, Turkana, Luhya, and Luo, from Kenya, and Baka, from Cameroon) that practise a wide range of traditional subsistence strategies (pastoralism, fishing, and agriculture), with the objective of building a framework in which to consider past diet in an East African context. This study analysed human hair (n = 143), nail (n = 83), and breath (n = 186) for δ$^{13}$C and δ$^{15}$N from the six different communities, and dental moulds (n = 150) from five of those communities (no moulds were collected from the Baka), and related the findings to dietary information. Dental microwear analyses had a low success rate because microwear features were obscured by the biofilm produced by mouth bacteria. Nevertheless, a visual analysis of the results suggested that the El Molo have the hardest and the toughest diet among all the groups studied, possibly through the inclusion of abrasives in the diet during food processing. In turn, the isotopic analyses revealed the ways in which agriculturalists and hunter gatherers differ from pastoralists and fishers in their isotopic values, although the variation in δ$^{13}$C and δ$^{15}$N did not distinguish between pastoralists and fishers. The results emphasise recent changes in the diet of these groups, the importance of local factors in isotope values, and the variable sensitivity of isotopes to dietary practices. In conclusion, although each technique could provide complementary data that would contribute to a more inclusive view of diet, dental microwear analyses are not easily applied to modern human groups, due to the difficulty in acquiring comparative in vivo data, and in distinguishing between patterns caused by food items, or food processing techniques.

abstract: Blur is an important attribute in the study and modeling of the human visual system. In this work, 3D blur discrimination experiments are conducted to measure the just noticeable additional blur required to differentiate a target blur from the reference blur level. The past studies on blur discrimination have measured the sensitivity of the human visual system to blur using 2D test patterns. In this dissertation, subjective tests are performed to measure blur discrimination thresholds using stereoscopic 3D test patterns. The results of this study indicate that, in the symmetric stereo viewing case, binocular disparity does not affect the blur discrimination thresholds for the selected 3D test patterns. In the asymmetric viewing case, the blur discrimination thresholds decreased and the decrease in threshold values is found to be dominated by the eye observing the higher blur. The second part of the dissertation focuses on texture granularity in the context of 2D images. A texture granularity database referred to as GranTEX, consisting of textures with varying granularity levels is constructed. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective index that automatically measures the perceived granularity level of textures is also presented. It is shown that the proposed granularity metric correlates well with the subjective granularity scores and outperforms the other methods presented in the literature. A subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. A logarithmic function model is proposed as a fit to the subjective test data. It is demonstrated that the proposed model can be used for rate-distortion control by allowing the automatic selection of the needed compression ratio for a target visual quality. The proposed model can also be used for visual quality assessment by providing a measure of the visual quality for a target compression ratio. The effect of texture granularity on the quality of synthesized textures is studied. A subjective study is presented to assess the quality of synthesized textures with varying levels of texture granularity using different types of texture synthesis methods. This work also proposes a reduced-reference visual quality index referred to as delta texture granularity index for assessing the visual quality of synthesized textures. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2017