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Wyszukujesz frazę "Śramek, M." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Segmentation of tomographic data by hierarchical watershed transform
Autorzy:
Sramek, M.
Dimitrov, L. I.
Powiązania:
https://bibliotekanauki.pl/articles/332935.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
segmentacja obrazu
skala przestrzeni
image segmentation
hierarchical watershed transform
scale space
Opis:
The aim of the proposed watershed based image segmentation technique is to split images into spatially homogeneous regions, which can be further processed by different image analysis tools. The advantage of such approach, in comparison to pixel oriented processing, is its lower sensitivity to superimposed noise due to averaging of regions properties over their area. The watershed segmentation technique is based on interpretation of an image as a topographic relief and on simulation of flow of water along steepest descent paths called downstreams. Thus, for each local minimum of the image, a drainage region is defined, which, if computed for a gradient image, represents an area with approximately constant properties. The segmentation technique is further extended for multi-scale image analysis by means of Gaussian smoothing. The aim of smoothing is to suppress image details that are smaller than standard deviation of the Gaussian. However, smoothing results not only in the desired increase of region size, but it also affects position of region boundaries, at least for larger standard deviations of the Gaussian filter. Therefore a new technique is proposed, based on region hierarchies, which enables to transfer region contours with precise position from the levels with low smoothing to levels with higher smoothing. Thus, segmentation of an image into large regions, but with exact contours, is obtained.
Źródło:
Journal of Medical Informatics & Technologies; 2002, 3; MI161-169
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
3D watershed transform combined with a probabilistic atlas for medical image segmentation
Autorzy:
Straka, M.
La Cruz, A.
Kochl, A.
Sramek, M.
Groller, E.
Fleischmann, D.
Powiązania:
https://bibliotekanauki.pl/articles/333997.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
angiografia tomografii komputerowej
segmentacja oparta na wiedzy
probabilistyczny atlas
kategoria histogramu
CT angiography
knowledge based segmentation
probabilistic atlas
thin-plate-spline
histogram classification
Opis:
Recent advances in medical imaging technology using multiple detector-row computed tomography (CT) provide volumetric datasets with unprecedented spatial resolution. This has allowed for CT to evolve into an excellent non-invasive vascular imaging technology, commonly referred to as CT-angiography. Visualisation of vascular structures from CT datasets is demanding, however, and identification of anatomic objects in CT-datasets is highly desirable. Density and/or gradient operators have been used most commonly to classify CT data. In CT angiography, simple density/gradient operators do not allow precise and reliable classification of tissues due to the fact that different tissues (e.g. bones and vessels) possess the same density range and may lie in close spatial vicinity. We think, that anatomic classification can be achieved more accurately, if both spatial location and density properties of volume data are taken into account. We present a combination of two well-known methods for volume data processing to obtain accurate tissue classification. 3D watershed transform is used to partition the volume data in morphologically consistent blocks and a probabilistic anatomic atlas is used to distinguish between different kinds of tissues based on their density.
Źródło:
Journal of Medical Informatics & Technologies; 2003, 6; IT69-78
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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