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Národní úložiště šedé literatury Nalezeno 3 záznamů.  Hledání trvalo 0.03 vteřin. 
On Sparsity in Bayesian Blind Source Separation for 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 blind source separation is to separate the images of biologic organs and related time-activity curves from the sequence of images.
Úplný záznam
Mathematics in image processing
 Šorel, Michal
In this tutorial we explained a unified view of many image processing and computer vision problems based on Bayesian frawork. To find solution this approach uses either methods of continuous optimization or discrete optimization methods on Markov random fields.
Úplný záznam
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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