30 November 2012 Framework for cognitive analysis of dynamic perfusion computed tomography with visualization of large volumetric data
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Abstract
The proposed framework for cognitive analysis of perfusion computed tomography images is a fusion of image processing, pattern recognition, and image analysis procedures. The output data of the algorithm consists of: regions of perfusion abnormalities, anatomy atlas description of brain tissues, measures of perfusion parameters, and prognosis for infracted tissues. That information is superimposed onto volumetric computed tomography data and displayed to radiologists. Our rendering algorithm enables rendering large volumes on off-the-shelf hardware. This portability of rendering solution is very important because our framework can be run without using expensive dedicated hardware. The other important factors are theoretically unlimited size of rendered volume and possibility of trading of image quality for rendering speed. Such rendered, high quality visualizations may be further used for intelligent brain perfusion abnormality identification, and computer aided-diagnosis of selected types of pathologies.
© 2012 SPIE and IS&T 0091-3286/2012/$25.00 © 2012 SPIE and IS&T
Tomasz Hachaj and Marek R. Ogiela "Framework for cognitive analysis of dynamic perfusion computed tomography with visualization of large volumetric data," Journal of Electronic Imaging 21(4), 043017 (30 November 2012). https://doi.org/10.1117/1.JEI.21.4.043017
Published: 30 November 2012
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CITATIONS
Cited by 25 scholarly publications.
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KEYWORDS
Visualization

Computed tomography

Tissues

Detection and tracking algorithms

Brain

Image processing

3D image processing

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