Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Local Histograms for Design of Transfer Functions in Direct Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Visualization and exploration of time-varying medical image data sets
GI '07 Proceedings of Graphics Interface 2007
Interactive Visual Analysis of Perfusion Data
IEEE Transactions on Visualization and Computer Graphics
Survey of the Visual Exploration and Analysis of Perfusion Data
IEEE Transactions on Visualization and Computer Graphics
PACIFICVIS '09 Proceedings of the 2009 IEEE Pacific Visualization Symposium
Consistent and elastic registration of histological sections using vector-spline regularization
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
IEEE Transactions on Image Processing
Editorial: Special Section on Visual Computing in Biology and Medicine
Computers and Graphics
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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.