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Vertebra detection and identification in CT oncological data
Věžníková, Romana ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
Automated spine or vertebra detection and segmentation from CT images is a difficult task for several reasons. One of the reasons is unclear vertebra boundaries and indistinct boundaries between vertebra. Next reason is artifacts in images and high degree of anatomical complexity. This paper describes the design and implementation of vertebra detection and classification in CT images of cancer patients, which adds to the complexity because some of vertebrae are deformed. For the vertebra segmentation, the Otsu’s method is used. Vertebra detection is based on search of borders between individual vertebra in sagittal planes. Decision trees or the generalized Hough transform is applied for the identification whereas the vertebra searching is based on similarity between each vertebra model shape and planes of CT scans.
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Image Super-Resolution Using Deep Learning
Bublavý, Martin ; Juránková, Markéta (referee) ; Španěl, Michal (advisor)
The ability to identify and treat a variety of medical diseases is made possible by medical imaging, which is an essential component of contemporary healthcare. Yet, elements like noise and low resolution can have a negative impact on the quality of medical photographs. In this thesis, how to enhance the resolution and quality of medical images was investigated using MedSRGAN, a deep learning model built on generative adversarial networks (GANs). MedSRGAN was implemented and then applied to computed tomography (CT), one of the most utilized medical imaging methods.
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Scientific report. Research on the composition of inks in a historical book by means of the X-ray fluorescence spectroscopy (XRF)
Vopálenský, Michal
Computed tomography (CT) of a unique book from the National Library of the Czech Republic - Slavonic Library was performed in the TORATOM tomograph in Center Telč, Institute of Theoretical and Applied Mechanics, Czech Academy of Sciences. The book is a 17th century euchologion originating in Kiev. During the tomographic scanning, the possibility of measuring the composition of the inks used in the book was discussed. In general, there are parts printed in black and parts printed in red in the book. Therefore, the measurement described in this report was carried out, where several pages of the work were measured in the area of black ink, red ink and in the area without ink.
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Historical lingerie: 18th century corset
Kumpová, Ivana ; Vopálenský, Michal
The work was focused on the methodology using X-ray tomographical imaging for the exploration of the textile-based historical artifacts. The 18th-century corset was investigated by this method. The inspection was conducted to reveal the production technology, design solution, decoration and possible damage in the front area where the whalebones converge. The computed tomography performed on the TORATOM device enabled visualisation of the internal structure of the area of interest with high precision. Sewing details and details of the composition of different material layers are well visible. Fractures and frayed ends of the whalebones were observed. In the area of interest, it is possible to distinguish several materials used for padding of the plastic parts.
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Vertebra detection and identification in CT oncological data
Věžníková, Romana ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
Automated spine or vertebra detection and segmentation from CT images is a difficult task for several reasons. One of the reasons is unclear vertebra boundaries and indistinct boundaries between vertebra. Next reason is artifacts in images and high degree of anatomical complexity. This paper describes the design and implementation of vertebra detection and classification in CT images of cancer patients, which adds to the complexity because some of vertebrae are deformed. For the vertebra segmentation, the Otsu’s method is used. Vertebra detection is based on search of borders between individual vertebra in sagittal planes. Decision trees or the generalized Hough transform is applied for the identification whereas the vertebra searching is based on similarity between each vertebra model shape and planes of CT scans.
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