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Proceedings Paper

Java multi-histogram volume rendering framework for medical images
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Paper Abstract

This work extends the multi-histogram volume rendering framework proposed by Kniss et al. [1] to provide rendering results based on the impression of overlaid triangles on a graph of image intensity versus gradient magnitude. The developed method of volume rendering allows for greater emphasis to boundary visualization while avoiding issues common in medical image acquisition. For example, partial voluming effects in computed tomography and intensity inhomogeneity of similar tissue types in magnetic resonance imaging introduce pixel values that will not reflect differing tissue types when a standard transfer function is applied to an intensity histogram. This new framework uses developing technology to improve upon the Kniss multi-histogram framework by using Java, the GPU, and MIPAV, an open-source medical image processing application, to allow multi-histogram techniques to be widely disseminated. The OpenGL view aligned texture rendering approach suffered from performance setbacks, inaccessibility, and usability problems. Rendering results can now be interactively compared with other rendering frameworks, surfaces can now be extracted for use in other programs, and file formats that are widely used in the field of biomedical imaging can be visualized using this multi-histogram approach. OpenCL and GLSL are used to produce this new multi-histogram approach, leveraging texture memory on the graphics processing unit of desktops to provide a new interactive method for visualizing biomedical images. Performance results for this method are generated and qualitative rendering results are compared. The resulting framework provides the opportunity for further applications in medical imaging, both in volume rendering and in generic image processing.

Paper Details

Date Published: 15 March 2013
PDF: 7 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86712J (15 March 2013); doi: 10.1117/12.2006980
Show Author Affiliations
Justin Senseney, National Institutes of Health (United States)
Walter Reed National Military Medical Ctr. (United States)
Alexandra Bokinsky, Geometric Tools, Inc. (United States)
Ruida Cheng, National Institutes of Health (United States)
Evan McCreedy, National Institutes of Health (United States)
Matthew J. McAuliffe, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Ziv R. Yaniv, Editor(s)

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