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Uncertainty-Aware Guided Volume Segmentation
November/December 2010 (vol. 16 no. 6)
pp. 1358-1365
ASCII Text
x 
 
Jörg-Stefan Praßni, Timo Ropinski, Klaus Hinrichs, "Uncertainty-Aware Guided Volume Segmentation," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1358-1365, November/December, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2010.208,
author = {Jörg-Stefan Praßni and Timo Ropinski and Klaus Hinrichs},
title = {Uncertainty-Aware Guided Volume Segmentation},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {16},
number = {6},
issn = {1077-2626},
year = {2010},
pages = {1358-1365},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.208},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Uncertainty-Aware Guided Volume Segmentation
IS - 6
SN - 1077-2626
SP1358
EP1365
EPD - 1358-1365
A1 - Jörg-Stefan Praßni,
A1 - Timo Ropinski,
A1 - Klaus Hinrichs,
PY - 2010
KW - volume segmentation
KW - uncertainty
KW - classification
KW - random walker
VL - 16
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Jörg-Stefan Praßni, University of Münster
Timo Ropinski, University of Münster
Klaus Hinrichs, University of Münster
Although direct volume rendering is established as a powerful tool for the visualization of volumetric data, efficient and reliable feature detection is still an open topic. Usually, a tradeoff between fast but imprecise classification schemes and accurate but time-consuming segmentation techniques has to be made. Furthermore, the issue of uncertainty introduced with the feature detection process is completely neglected by the majority of existing approaches.In this paper we propose a guided probabilistic volume segmentation approach that focuses on the minimization of uncertainty. In an iterative process, our system continuously assesses uncertainty of a random walker-based segmentation in order to detect regions with high ambiguity, to which the user's attention is directed to support the correction of potential misclassifications. This reduces the risk of critical segmentation errors and ensures that information about the segmentation's reliability is conveyed to the user in a dependable way. In order to improve the efficiency of the segmentation process, our technique does not only take into account the volume data to be segmented, but also enables the user to incorporate classification information. An interactive workflow has been achieved by implementing the presented system on the GPU using the OpenCL API. Our results obtained for several medical data sets of different modalities, including brain MRI and abdominal CT, demonstrate the reliability and efficiency of our approach.

[1] D. Bartz, D. Mayer, J. Fischer, S. Ley, A. d. Rio, S. Thust, C. P. Heussel, H.-U. Kauczor, and W. Strasser, Hybrid segmentation and exploration of the human lungs. In IEEE Visualization, pages 177–184, 2003.
[2] N. Ben-Zadok, T. Riklin-Raviv, and N. Kiryati, Interactive level set segmentation for image-guided therapy. In IEEE Int. Symp. on Biomedical Imaging, pages 1079–1082, 2009.
[3] Y. Boykov and V. Kolmogorov, Interactive organ segmentation using graph cuts. In Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, pages 276–286, 2000.
[4] Y. Boykov and V. Kolmogorov, Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In IEEE Int. Conf. on Computer Vision, pages 105–112, 2001.
[5] Y. Boykov and V. Kolmogorov, Computing geodesics and minimal surfaces via graph cuts. In IEEE Int. Conf. on Computer Vision, pages 26–33, 2003.
[6] Z. Chen, T. Qiu, and S. Ruan, A brain tissue segmentation approach integrating fuzzy information into level set method. Automation and Logistics (ICAL), pages 1216 –1221, 2008.
[7] C. D. Correa and K.-L. Ma, The occlusion spectrum for volume classification and visualization. IEEE TVCG, 15 (6): 1465–1472, 2009.
[8] D. Cremers, O. Fluck, M. Rousson, and S. Aharon, A probabilistic level set formulation for interactive organ segmentation. Medical Imaging 2007: Image Processing, 6512 (1): 120–129, 2007.
[9] S. Djurcilov, K. Kim, P. F.J. Lermusiaux, and A. Pang, Volume rendering data with uncertainty information. In Joint EUROGRAPHICS/IEEE TCVG Symp. on Visualization, pages 243–252, 2001.
[10] J. Fischer, D. Bartz, and W. Straßer, Illustrative Display of Hidden Iso-Surface Structures. In IEEE Visualization, pages 663–670, 2005.
[11] L. Grady, Random walks for image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28 (11): 1768–1783, 2006.
[12] L. Grady, T. Schiwietz, S. Aharon, and T. U. Munchen, Random walks for interactive organ segmentation in two and three dimensions: Implementation and validation. In MICCAI2005II, pages 773–780, 2005.
[13] G. Grigoryan and P. Rheingans, Point-based probabilistic surfaces to show surface uncertainty. IEEE TVCG, 10 (5): 564–573, 2004.
[14] L. Gu and T. Peters, Robust 3d organ segmentation using a fast hybrid algorithm. In Computer Assisted Radiology and Surgery, volume 1268, pages 69–74, 2004.
[15] Hastreiter, Peter and Ertl, Thomas, Fast and Interactive 3D-Segmentation of Medical Volume Data. In Computer Graphics International, pages 78–85, 1998.
[16] R. Huang and K.-L. Ma, Rgvis: Region growing based techniques for volume visualization. In Pacific Conf. on Computer Graphics and Applications, pages 355–363, 2003.
[17] V. Interrante, H. Fuchs, and S. M. Pizer, Conveying the 3d shape of smoothly curving transparent surfaces via texture. IEEE TVCG, 3 (2): 98–117, 1997.
[18] C. Johnson, Top scientific visualization research problems. IEEE Computer Graphics and Applications, 24 (4): 13–17, 2004.
[19] G. Kindlmann and J. W. Durkin, Semi-automatic generation of transfer functions for direct volume rendering. In IEEE Symp. on Volume Visualization, pages 79–86, 1998.
[20] J. M. Kniss, R. V. Uitert, A. Stephens, G.-S. Li, T. Tasdizen, and C. Hansen, Statistically quantitative volume visualization. In IEEE Visualization, page 37, 2005.
[21] P. Kohli and P. H. Torr, Measuring uncertainty in graph cut solutions. Computer Vision and Image Understanding, 112 (1): 30–38, 2008.
[22] D. Kontos, Q. Wang, V. Megalooikonomou, A. H. Maurer, L. C. Knight, S. Kantor, H. P. Simonian, and H. P. Parkman, A tool for handling uncertainty in segmenting regions of interest in medical images. Int. J. of Intelligent Systems Technologies and Applications, 1 (3/4): 194–210, 2006.
[23] C. Lundstrom, P. Ljung, A. Persson, and A. Ynnerman, Uncertainty visualization in medical volume rendering using probabilistic animation. IEEE TVCG, 13 (6): 1648–1655, 2007.
[24] A. M. Maceachren, A. Robinson, S. Gardner, R. Murray, M. Gahegan, and E. Hetzler, Visualizing geospatial information uncertainty: What we know and what we need to know. cartography and geographic. Information Science, 32: 137–160, 2005.
[25] E. N. Mortensen and W. A. Barrett, Intelligent scissors for image composition. In SIGGRAPH'95, pages 191–198, 1995.
[26] S. Olabarriaga and A. Smeulders, Interaction in the segmentation of medical images: A survey. Medical Image Analysis, 5 (2): 127–142, 2001.
[27] S. Owada, F. Nielsen, and T. Igarashi, Volume Catcher. In Symp. on Interactive 3D Graphics and Games, pages 111–116, 2005.
[28] A. Pang, Visualizing uncertainty in geo-spatial data. In Work. Intersections between Geospatial Information and Information Technology, 2001.
[29] A. T. Pang, C. M. Wittenbrink, and S. K. Lodh, Approaches to uncertainty visualization. The Visual Computer, 13: 370–390, 1996.
[30] M. Prastawa, E. Bullitt, and G. Gerig, Synthetic ground truth for validation of brain tumor mri segmentation. In MICCAI, pages 26–33, 2005.
[31] P. J. Rhodes, R. S. Laramee, R. D. Bergeron, and T. M. Sparr, Uncertainty visualization methods in isosurface volume rendering. In Eurographics 2003, pages 83–88, 2003.
[32] A. Saad, T. Moller, and G. Hamarneh, Probexplorer: Uncertainty-guided exploration and editing of probabilistic medical image segmentation. Joint Eurographics/IEEE-VGTC Symp. on Visualization, 29 (3), 2010.
[33] Y. Sato, C.-F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura, and R. Kikinis, Tissue classification based on 3d local intensity structures for volume rendering. IEEE TVCG, 6 (2): 160–180, 2000.
[34] J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials. Cambridge University Press, 2 edition, June 1999.
[35] D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, Online resource for validation of brain segmentation methods. NeuroImage, 45 (2): 431–439, 2009.
[36] A. Sherbondy, M. Houston, and S. Napel, Fast volume segmentation with simultaneous visualization using programmable graphics hardware. In IEEE Visualization, pages 171–176, 2003.
[37] B. Shneiderman, The eyes have it: A task by data type taxonomy for information visualizations. In IEEE Vis. Languages, pages 336–343, 1996.
[38] F.-Y. Tzeng, E. B. Lum, and K.-L. Ma, An intelligent system approach to higher-dimensional classification of volume data. IEEE TVCG, 11 (3): 273–284, 2005.
[39] C. Ware, Information Visualization: Perception for Design. Morgan Kaufmann Publishers Inc., 2000.
[40] S. C. Zhu and A. Yuille, Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans, on Pattern Analysis and Machine Intelligence, 18: 884–900, 1996.

Index Terms:
volume segmentation, uncertainty, classification, random walker
Citation:
Jörg-Stefan Praßni, Timo Ropinski, Klaus Hinrichs, "Uncertainty-Aware Guided Volume Segmentation," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1358-1365, Nov.-Dec. 2010, doi:10.1109/TVCG.2010.208
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