Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 44-45). Also available on the World Wide Web.

Thesis (M.S.)--Ohio University, June, 2004. / Title from PDF t.p. Includes bibliographical references (p. 136-138)

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This paper presents a statistical framework which combines the registration of an atlas with the segmentation of MR images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 30 brain MR images. In addition, we show that the approach performs better than similar methods which separate the registration from the segmentation problem.

Thesis (M.S.)--University of Akron, Dept. of Computer Science, 2009. / "May, 2009." Title from electronic thesis title page (viewed 11/18/2009) Advisor, Kathy J. Liszka; Faculty Readers, Timothy W. O'Neil, Tim Margush; Department Chair, Wolfgang Pelz; Dean of the College, Chand Midha; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

Colorectal cancer (CRC) patients are stratified by the Tumour, Node and Metastasis (TNM) staging system for clinical decision making. Additional genomic markers have a limited utility in some cases where precise targeted therapy may be available. Thus, classical clinical pathological staging remains the mainstay of the assessment of this disease. Surgical resection is generally considered curative for Stage II patients, however 20-30% of these patients experience disease recurrence and disease specific death. It is imperative to identify these high risk patients in order to assess if further treatment or detailed follow up could be beneficial to their overall survival. The aim of the thesis was to categorise Stage II CRC patients into high and low risk of disease specific death through novel image based analysis algorithms. Firstly, an image analysis algorithm was developed to quantify and assess the prognostic value of three histopathological features through immuno-fluorescence: lymphatic vessel density (LVD), lymphatic vessel invasion (LVI) and tumour budding (TB). Image analysis provides the ability to standardise their quantification and negates observer variability. All three histopathological features were found to be predictors of CRC specific death within the training set (n=50); TB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57- 27.98). Only TB (HR=2.49; 95% CI, 1.03-5.99) and LVI (HR =2.46; 95%CI, 1 - 6.05), however, were significant predictors of disease specific death in the validation set (n=134). Image analysis was further employed to characterise TB and quantify intra-tumoural heterogeneity. Tumour subpopulations within CRC tissue sections were segmented for the quantification of differential biomarker expression associated with epithelial mesenchymal transition and aggressive disease. Secondly, a novel histopathological feature ‘Sum Area Large Tumour Bud’ (ALTB) was identified through immunofluorescence coupled to a novel tissue phenomics approach. The tissue phenomics approach created a complex phenotypic fingerprint consisting of multiple parameters extracted from the unbiased segmentation of all objects within a digitised image. Data mining was employed to identify the significant parameters within the phenotypic fingerprint. ALTB was found to be a more significant predictor of disease specific death than LVI or TB in both the training set (HR = 20.2; 95% CI, 4.6 – 87.9) and the validation set (HR = 4; 95% CI, 1.5 – 11.1). Finally, ALTB was combined with two parameters, ‘differentiation’ and ‘pT stage’, which were exported from the original patient pathology report to form an integrative pathology score. The integrative pathology score was highly significant at predicting disease specific death within the validation set (HR = 7.5; 95% CI, 3 – 18.5). In conclusion, image analysis allows the standardised quantification of set histopathological features and the heterogeneous expression of biomarkers. A novel image based histopathological feature combined with classical pathology allows the highly significant stratification of Stage II CRC patients into high and low risk of disease specific death.

The aim of the diploma thesis is the development and subsequent construction of the device whose function is measuring and evaluating physical activity of insects. This instrument is unique in the Czech Republic and will be able to record physical activity of insects by using a video camera. Later that it will be able in this video shot back to reconstruct kinematic and statistical data about the movement of insects with software which is developed with Visual Basic .NET. On the base sufficient od obtained data it can be used to verify the premise of biologists about nervous systém, brain function and behavioral of Drosophila Melangostar which is more than a century used as model for genetics experiments. Finally, the conclusion compares the result of previously known and available software solutions in the world of biology, which are still used as a source for the results of performed experiments.

Cytotoxic T lymphocytes (CTL) are major effector cells in the adaptive immune response against intracellular pathogens and cancers, killing targets with high precision. Precision is achieved through the specificity of the clonally expressed T cell receptor (TCR). TCRs recognise a specific peptide chain loaded into a major-histocompatability complex, triggering signalling, inducing the CTL to attach and kill target cells. Key stages in this attack are the initial conjugation followed by polarisation and docking of the centrosome to the junction of the two cells, the immune synapse (IS). This focuses secretion of the cytolytic components, perforin and granzyme, from modified lysosomes to kill the target cell. My PhD has utilised amino acid substitutions in the target peptide to alter its signal strength and shown this alters the subsequent killing efficiency of a target population. I developed new imaging and analysis techniques to investigate the effect of TCR signal strength at each step of the killing process. I show the first step, conjugation, is reduced for a percentage of cells with dwell times decreasing as TCR signal strength decreased. The next key step of centrosome polarisation and docking at the IS was also impaired for an increasing proportion of cells as TCR signalling reduced. Impaired centrosome docking reduced efficient granule recruitment to the IS, necessary for target killing. Centrosome docking was linked with the TCR-induced intracellular calcium flux, the duration of which increases with the strength of TCR signalling. This demonstrates how the process of CTL killing can be fine-tuned by the quality of antigen.

A flourishing World Wide Web dramatically increases the amount of images up­loaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the seman­tic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrieval, it is necessary to dig the deep concept and semantic meaning of the image as well as to overcome the semantic gap. By exploiting the context of Web images, knowledge base and ontology-based similarities, through the analysis of user behavior of image similarity evaluation, we established a set of formulas which allows e.cient and accurate semantic similarity measurement of images. When jointly applied with ontology-based query expansion approaches and an adaptive image search engine for deep knowledge indexing, they are able to produce a new level of meaningful automatic image annotation, from which semantic image search may be performed. Besides, the semantic concept can be automatically enriched in MPEG-7 Structured Image Annotation approach. The system is evaluated quantitatively using more than thousands of Web images with associated human tags with user subjective test. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good precision e.ciency. This approach enables an advanced degree of semantic richness to be automatically associated with images and e.cient image concept similarity measurement which could previously only be performed manually. Keywords: Image Index, Image Retrieval, Semantic Similarity, Relevance Feed­back, Knowledge Base, Ontology, Query Expansion, MPEG-7 . . .

Este trabalho faz um estudo do uso da entropia como ferramenta para o reconhecimento de padrões em imagens. A entropia é um conceito utilizado em termodinâmica para medir o grau de organização de um meio. Entretanto, este conceito pode ser ampliado para outras áreas do conhecimento. A adoção do conceito em Teoria da Informação e, por consequência, em reconhecimento de padrões foi introduzida por Shannon no trabalho intitulado \"A Mathematical Theory of Communication\", publicado no ano de 1948. Neste mestrado, além da entropia clássica de Boltzman-Gibbs-Shannon, são investigadas a entropia generalizada de Tsallis e suas variantes (análise multi-escala, múltiplo índice q e seleção de atributos), aplicadas ao reconhecimento de padrões em imagens. Utilizando bases de dados bem conhecidas na literatura, realizou-se estudos comparativos entre as técnicas. Os resultados mostram que a entropia de Tsallis, através de análise multi-escala e múltiplo índice q, tem grande vantagem sobre a entropia de Boltzman-Gibbs-Shannon. Aplicações práticas deste estudo são propostas com o intuito de demonstrar o potencial do método. / This work studies the use of entropy as a tool for pattern recognition in images. Entropy is a concept used in thermodynamics to measure the degree of organization of a system. However, this concept can be extended to other areas of knowledge. The adoption of the concept in information theory and, consequently, in pattern recognition was introduced by Shannon in the paper entitled \"A Mathematical Theory of Communication\", published in 1948. In this master thesis, the classical Boltzmann-Gibbs-Shannon entropy, the generalized Tsallis entropy and its variants (multi-scale analysis, multiple q index, and feature selection) are studied, applied to pattern recognition in images. Using well known databases, we performed comparative studies between the techniques. The results show that the Tsallis entropy, through multiscale analysis and multiple q index has a great advantage over the classical Boltzmann-Gibbs- Shannon entropy. Practical applications of this study are proposed in order to demonstrate the potential of the method.