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Interactive Visual Analysis of Contrast-enhanced Ultrasound Data based on Small Neighborhood Statistics

Paolo Angelelli, Kim Nylund, Odd Helge Gilja, Helwig Hauser

ARTICLE, Computers & Graphics - Special Issue on Visual Computing in Biology and Medicine, 2011

Abstract

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization in cancer diagnosis. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper we present a pipeline that enables interactive visual exploration and semi-automatic segmentation and classification of CEUS data. For the visual analysis of this challenging data, with characteristic noise patterns and residual movements, we propose a robust method to derive expressive enhancement measures from small spatio-temporal neighborhoods. We use this information in a staged visual analysis pipeline that leads from a more local investigation to global results such as the delineation of anatomic regions according to their perfusion properties. To make the visual exploration interactive, we have developed an accelerated framework based on the OpenCL library, that exploits modern many-cores hardware. Using our application, we were able to analyze datasets from CEUS liver examinations, being able to identify several focal liver lesions, segment and analyze them quickly and precisely, and eventually characterize them.

Published

Computers & Graphics - Special Issue on Visual Computing in Biology and Medicine

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BibTeX

@article{angelelli11ultrasoundStatistics,
  author = {Paolo Angelelli and Kim Nylund and Odd Helge Gilja and Helwig Hauser},
  title = {Interactive Visual Analysis of Contrast-enhanced Ultrasound Data
based on Small Neighborhood Statistics},
  year = {2011},
  abstract = {Contrast-enhanced ultrasound (CEUS) has recently become an important 
technology for lesion detection and characterization in cancer diagnosis. CEUS is 
used to investigate the perfusion kinetics in tissue over time, which relates to 
tissue vascularization. In this paper we present a pipeline that enables interactive 
visual exploration and semi-automatic segmentation and classification of CEUS data.

For the visual analysis of this challenging data, with characteristic noise patterns 
and residual movements, we propose a robust method to derive expressive enhancement 
measures from small spatio-temporal neighborhoods. We use this information in a staged
visual analysis pipeline that leads from a more local investigation to global results 
such as the delineation of anatomic regions according to their perfusion properties. 
To make the visual exploration interactive, we have developed an accelerated framework
based on the OpenCL library, that exploits modern many-cores hardware. Using our 
application, we were able to analyze datasets from CEUS liver examinations, being able 
to identify several focal liver lesions, segment and analyze them quickly and 
precisely, and eventually characterize them.},
  journal = {Computers \& Graphics - Special Issue on Visual Computing in Biology and Medicine},
  volume = {35},
  number = {2},
  pages = {218--226},
  URL = {http://dx.doi.org/10.1016/j.cag.2010.12.005},


}






 Last Modified: Jean-Paul Balabanian, 2014-04-09