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Segmentation of carbon nanocones in TEM images using generalized Hough transform
Sladký, Vladimír ; Odstrčilík, Jan (referee) ; Walek, Petr (advisor)
Bachelor thesis deals with automatic detection of carbon nanostructures in TEM images using generalized Hough transform. There is described the theory of Hough transform in order to detect analytic as well as general structures in images. According to the characteristics of carbon nanostructures TEM images synthetic test images, that are preprocessed by morphological operations and thresholding with global threshold, are created. Hough transform algorithm that detects nanocones in synthetic images of carbon nanocone structures is created. Success and accuracy of detection is tested changing the parameters of artificial images or level of preprocessing.
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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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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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Segmentation of carbon nanocones in TEM images using generalized Hough transform
Sladký, Vladimír ; Odstrčilík, Jan (referee) ; Walek, Petr (advisor)
Bachelor thesis deals with automatic detection of carbon nanostructures in TEM images using generalized Hough transform. There is described the theory of Hough transform in order to detect analytic as well as general structures in images. According to the characteristics of carbon nanostructures TEM images synthetic test images, that are preprocessed by morphological operations and thresholding with global threshold, are created. Hough transform algorithm that detects nanocones in synthetic images of carbon nanocone structures is created. Success and accuracy of detection is tested changing the parameters of artificial images or level of preprocessing.
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