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2 Journal of Medical Informatics & Technologies

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The f3d tools for processing and visualization of volumetric data

100%

Šrámek M. , Dimitrov L. I. , Straka M. , Červeňanský M.

Journal of Medical Informatics & Technologies

2004

tom Vol. 7

MIP69--78

In this paper we introduce the f3d format for storage of volumetric data together with a suite of tools for processing, segmentation and visualization of such data. Both the format and tools were developed for a highly variable and rapidly evolving academic environment, where new data processing and visualization tasks emerge very often. The tools address all the steps of a volume visualization pipeline: starting with import of external formats, over preprocessing, filtering, segmentation to interactive visualization.

3D watershed transform combined with a probabilistic atlas for medical image segmentation

75%

Straka M. , La Cruz A. , Köchl A. , Šrámek M. , Gröller E. , Fleischmann D.

Journal of Medical Informatics & Technologies

2003

tom Vol. 6

IT69--78

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.