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Volumetric data processing for CT enterography
Horáček, Jan ; Pelikán, Josef (advisor) ; Czanner, Silvester (referee) ; Schier, Jan (referee)
Title: Volumetric data processing for CT enterography Author: Jan Horáček Department: Department of Software and Computer Science Education Supervisor: RNDr. Josef Pelikán, Department of Software and Computer Sci- ence Education Abstract: The overall goal of our work is to develop algorithms for efficient processing, segmentation and tracking of the small intestine in CT enterography scans. The small intestine is a complex organ, the shape of which can vary con- siderably between patients: and in addition to this, its location and shape can change significantly between subsequent scans of the same person. The CT en- terography process uses contrast agents to improve the visibility of the intestine, so that various potentially problematic features, such as inflammations, obstruc- tions and so on, can be properly seen. However, due to the convoluted shape of the organ, manual diagnosis of raw CT enterography data is still a difficult and time-consuming task, and is prone to diagnostic errors. We have prepared a set of methods for automatic preprocessing, segmentation and tracking of such data that aims at providing a much clearer data visualization: such tools can greatly improve the diagnostic process. Our first contribution is to make a high quality denoising method for volumet- ric data practically usable: so...
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Interactive Processing of Volumetric Data
Kolomazník, Jan ; Pelikán, Josef (advisor) ; Czanner, Silvester (referee) ; Dokládal, Petr (referee)
Title: Interactive Processing of Volumetric Data Author: Jan Kolomazník Department: Department of Software and Computer Science Education Supervisor: RNDr. Josef Pelikán, Department of Software and Computer Science Education Abstract: Interactive visualization and segmentation of volumetric data are quite lim- ited due to the increased complexity of the task and size of the input data in comparison to two-dimensional processing. A special interactive segmentation workflow is presented, based on minimal graph-cut search. The overall execution time was lowered by implementing all the computational steps on GPU, which required a design of massively parallel algorithms (using thousands of threads). To lower the computational burden even further the graph is constructed over the image subregions com- puted by parallel watershed transformation. As a suitable formalism for a range of massively parallel algorithms was chosen cellular automata. A set of cellular automata extensions was defined, which allows efficient mapping and computation on GPU. Several variants of parallel watershed transformation are then defined in the form of cellular automaton. A novel form of 2D transfer function was presented, to improve direct volume visualization of the input data, suited for discriminating image features by their shape and...
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Volumetric data processing for CT enterography
Horáček, Jan ; Pelikán, Josef (advisor) ; Czanner, Silvester (referee) ; Schier, Jan (referee)
Title: Volumetric data processing for CT enterography Author: Jan Horáček Department: Department of Software and Computer Science Education Supervisor: RNDr. Josef Pelikán, Department of Software and Computer Sci- ence Education Abstract: The overall goal of our work is to develop algorithms for efficient processing, segmentation and tracking of the small intestine in CT enterography scans. The small intestine is a complex organ, the shape of which can vary con- siderably between patients: and in addition to this, its location and shape can change significantly between subsequent scans of the same person. The CT en- terography process uses contrast agents to improve the visibility of the intestine, so that various potentially problematic features, such as inflammations, obstruc- tions and so on, can be properly seen. However, due to the convoluted shape of the organ, manual diagnosis of raw CT enterography data is still a difficult and time-consuming task, and is prone to diagnostic errors. We have prepared a set of methods for automatic preprocessing, segmentation and tracking of such data that aims at providing a much clearer data visualization: such tools can greatly improve the diagnostic process. Our first contribution is to make a high quality denoising method for volumet- ric data practically usable: so...
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