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On Validation of Algorithms for Dynamic Medical Data Separation
Tichý, Ondřej
The problem of dynamic medical image sequence separation is studied. We introduced the state of the art algorithms for medical sequence decomposition together with those that are proposed by us. The validation and the comparison of the algorithms are nontrivial and challenging task. We propose to use a synthetic data where a ground truth is available so it is possible to compute a significant statistics for comparison reason. Moreover, we proposed a comparison on 99 real data from renal scintigraphy where relative renal functions are automatically computed and compared with those obtained by physician.
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Inovační politika Německa a její vliv na vývoj obchodní bilance
Tichý, Ondřej ; Gullová, Soňa (vedoucí práce) ; Klosová, Anna (oponent)
Tato diplomová práce pojednává o inovační politice Spolkové republiky Německo, jejím vývoji a charakteristice. Čtenáři nabízí stručný popis role státu v inovační politice a orgánů činných v oblasti inovací. Zabývá se technologickou specializací v německém inovačním systému a následně postavením německého výzkumného a inovačního systému v mezinárodním srovnání. V závěrečné části tato práce nabízí analýzu vlivu inovační politiky na vývoj obchodní bilance státu.
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Convolution Model of Time-activity Curves in Blind Source Separation
Tichý, Ondřej ; Šmídl, Václav
Availability of input and organ functions is a prerequisite for analysis of dynamic image sequences in scintigraphy and positron emission tomography (PET) via kinetic models. In PET, the input function can be directly measured by sampling the arterial blood. This invasive procedure can be substituted by extraction of the input function from the observed images. Standard procedure for the extraction is based on manual selection of a region of interest (ROI) which is user-dependent and inaccurate. The aim of our contribution is to demonstrate a new procedure for simultaneous estimation of the input and organ functions from the observed image sequence. We design a mathematical model that integrates all common assumption of the domain, including convolution of the input function and tissue-specific kernels. The input function as well as the kernel parameters are considered to be unknown. They are estimated from the observed images using the Variational Bayes method.
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Automated Functional Analysis in Dynamic Medical Imaging
Tichý, Ondřej
Dynamic medical imaging is concerned with acquisition and analysis of a sequence of images of the same region of a body during time. In nuclear medicine, each pixel of an image is the sum of particles coming from an applied radioactive tracer from the body in a specific time-interval. Hence, each observed image is a superposition of an unknown number of underlaying organ images. The aim of functional analysis is to separate the images of biologic organs and related time-activity curves from the sequence of images.
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Model Consideration for Blind Source Separation of Medical Image Sequences
Tichý, Ondřej
The problem of functional analysis of medical image sequences is studied. The obtained images are assumed to be a superposition of images of underlying biological organs. This is commonly modeled as a Factor Analysis (FA) model. However, this model alone allows for biologically impossible solutions. Therefore, we seek additional biologically motivated assumptions that can be incorporated into the model to yield better solutions. In this paper, we review additional assumptions such as convolution of time activity, regions of interest selection, and noise analysis. All these assumptions can be incorporated into the FA model and their parameters estimated by the Variation Bayes estimation procedure. We compare these assumptions and discuss their influence on the resulting decomposition from diagnostic point of view. The algorithms are tested and demonstrated on real data from renal scintigraphy; however, the methodology can be used in any other imaging modality.
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Variational Bayes in Distributed Fully Probabilistic Decision Making
Šmídl, Václav ; Tichý, Ondřej
We are concerned with design of decentralized control strategy for stochastic systems with global performance measure. It is possible to design optimal centralized control strategy, which often cannot be used in distributed way. The distributed strategy then has to be suboptimal (imperfect) in some sense. In this paper, we propose to optimize the centralized control strategy under the restriction of conditional independence of control inputs of distinct decision makers. Under this optimization, the main theorem for the Fully Probabilistic Design is closely related to that of the well known Variational Bayes estimation method. The resulting algorithm then requires communication between individual decision makers in the form of functions expressing moments of conditional probability densities. This contrasts to the classical Variational Bayes method where the moments are typically numerical.
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Factor Analysis of Scintigraphic Image Sequences with Integrated Probabilistic Mask of Factor Images
Tichý, Ondřej
Factor analysis is a well established mathematical method for factor separation in the analysis of scintigraphical sequences. The results are typically an input to the next step, e.g. factor analysis for computing significant diagnostic coefficients. However, this computing highly depends on proper identification of factors and their biological meaning, which is not ensure only by factor analysis. The main issue is separation overlaping factors from themselves and from tissue background covering the whole sequence. Factor analysis highly depends on prior information which allows us to set biologically reasonable conditions to a mathematical model. In this paper, we propose a mathematical model which estimates the probability mask of each image factor and sets it as a prior information for the next step of iterative algorithm based on Variational Bayes method. The new proposed model provides more realistic estimates of factors than the standard factor analysis.
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Model metrik elektronického obchodu Mall.cz
Tichý, Ondřej ; Novotný, Ota (vedoucí práce) ; Táborský, Michal (oponent)
Cílem diplomové práce je návrh modelu metrik pro společnost Internet Mall, a.s., která podniká v oblasti prodeje zboží přes internet. Druhým cílem je praktická ukázka výstupů pro vybrané metriky s využitím nástroje Business Intelligence (BI). Pro návrh modelu byl zvolen koncept Balanced scorecard (BSC), představující systém měření a řízení podnikových aktivit. Koncept byl doplněn o studie zabývající se rozšířením BSC pro oblast e-commerce. Tyto zdroje posloužily primárně jako inspirace. Hlavní podněty vzešly z konzultací se zástupci společnosti Internet Mall. Cíl praktické části byl realizován na třech vybraných metrikách uplatněním BI analýzy. Byl prezentován jak postup technické implementace modelu metrik, tak konečný výsledek celého snažení, analytické reporty pro vedení společnosti.
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Komparativní postavení Bavorska v SRN
Tichý, Ondřej ; Steinmetzová, Dana (vedoucí práce) ; Hnát, Pavel (oponent)
Tato bakalářská práce pojednává o hospodářské situaci SRN a její největší spolkové země Bavorska. Dále čtenáři nabízí stručný historický vývoj Bavorska a popis jeho politického systému. Zabývá se mezinárodním obchodem této spolkové země s důrazem na export. S tím souvisí i popis opatření předložených bavorskou vládou na podporu exportu v době hospodářské krize. V závěrečné části tato práce nabízí srovnání těchto opatření s těmi, která byla v České republice představena Národní ekonomickou radou vlády.
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