ABSTRACT
Volume data is usually generated by measuring devices (eg. CT scanners, MRI scanners), mathematical functions (eg., Marschner/Lobb function), or by simulations. While all these sources typically generate 12 bit integer or floating point representations, commonly used displays are only capable of handling 8 bit gray or color levels. In a typical medical scenario, a 3D scanner will generate a 12 bit dataset, from which a subrange of the active full accuracy data range of 0 up to 4096 voxel values will be downsampled to an 8 bit per-voxel accuracy. This downsampling is usually achieved by a linear mapping operation and by clipping of value ranges left and right of the chosen subrange.In this paper, we propose a novel windowing operation that is based on methods from high dynamic range image mapping. With this method, the contrast of mapped 8 bit volume datasets is significantly enhanced, in particular if the imaging modality allows for a high tissue differentiation (eg., MRI). Thus, it also allows better and easier segmentation and classification. We demonstrate the improved contrast with different error metrics and a perception-driven image difference to indicate differences between three different high dynamic range operators.
- Adams, A. 1980. The Camera. The Ansel Adams Photography Series. Little, Brown and Company.Google Scholar
- Adams, A. 1983. The Print. The Ansel Adams Photography series. Little, Brown and Company.Google Scholar
- Ashikhmin, M. 2002. A Tone Mapping Algorithm for High Contrast Images. In Proc. of Eurographics Workshop on Rendering. Google ScholarDigital Library
- Barco, 2003. MeDis Grayscale CRT Display Systems Product Brochure. Barco.Google Scholar
- Bardera, A., Feixas, M., and Boada, I. 2004. Normalized Similarity Measures for Medical Image Registration. In Proc. of SPIE Medical Imaging.Google Scholar
- Bartz, D. 2002. Advanced Virtual Medicine: Techniques and Applications for Virtual Endoscopy. In ACM SIGGRAPH Course 52.Google Scholar
- Blommaert, F., and Martens, J. 1990. An Object-Oriented Model for Brightness Perception. Spatial Vision 5, 1, 15--41.Google ScholarCross Ref
- Chiu, K., Herf, M., Shirley, P., Swamy, S., Wang, C., and Zimmerman, K. 1993. Spatially Nonuniform Scaling Functions for High Contrast Images. In Proc. of Graphics Interface, 245--253.Google Scholar
- Devlin, K. 2002. A Review of Tone Reproduction Techniques. Tech. Rep. 117, Department of Computer Science, University of Bristol, November.Google Scholar
- Drago, F., Myszkowski, K., Annen, T., and Chiba, N. 2003. Adaptive Logarithmic Mapping for Displaying High Contrast Scenes. Computer Graphics Forum 22, 3, 419--426.Google ScholarCross Ref
- Durand, F., and Dorsey, J. 2002. Fast Bilateral Filtering for the Display of High Dynamic Range Image. In Proc. of ACM SIGGRAPH, 257--265. Google ScholarDigital Library
- Fattal, R., Lischinski, D., and Werman, M. 2002. Gradient Domain High Dynamic Range Compression. In Proc. of ACM SIGGRAPH, 249--256. Google ScholarDigital Library
- Gonzalez, R., and Woods, R. 1992. Digital Image Processing. Addison-Wesleym, Reading. Google ScholarDigital Library
- Kugler, A., Grunert, T., Becker, E., and Strasser, W. 1998. MEDStation: Bringing Together Medical Imaging and Diagnosis. In Proc. of European Multimedia, Microprocessor Systems and Electronic Commerce.Google Scholar
- Mitchell, J., accessed 2004. Real-Time 3D Scene Post-Processing. Game Developer Conference 2003: http://www.ati.com/developer/gdc/GDC2003_ ScenePost-processing.pdf.Google Scholar
- Petterson, H., accessed 2005. Window (The Encyclopaedia of Medical Imaging website). http://www.amershamhealth.com/medcyclopaedia/ medical.Google Scholar
- R. Mantiuk and K. Myszkowski and H. Seidel. 2004. Visible Difference Predicator for High Dynamic Range Images. In Proc. of IEEE International Conference on Systems. Man and Cybernetics, 2763--2769.Google Scholar
- Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. 2002. Photographic Tone Reproduction for Digital Images. In Proc. of ACM SIGGRAPH, 267--276. Google ScholarDigital Library
- Steinmetz, R. 2000. Multimedia Technologie - Grundlagen, Komponenten und Systeme, 3rd ed. Springer-Verlag, Heidelberg.Google Scholar
- Tumblin, J., and Turk, G. 1999. LCIS: A Boundary Hierarchy for Detail-Preserving Contrast Reduction. In Proc. of ACM SIGGRAPH, 83--90. Google ScholarDigital Library
- Tumblin, J., Hodgins, J. K., and Guenter, B. 1999. Two Methods for Display of High Contrast Images. ACM Transactions on Graphics 1, 18 (January), 56--94. Google ScholarDigital Library
- Yuan, X., Nguyen, M., Chen, B., and Porter, D. 2005. High Dynamic Range Volume Visualization. In Proc. of IEEE Visualization, 327--334.Google Scholar
Index Terms
- Volumetric high dynamic range windowing for better data representation
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