Accounting for Uncertainty in Medical Data: A CUDA Implementation of Normalized Convolution

S. Lindholm
Department of Science and Technology, Linköping University

J. Kronander
Department of Science and Technology, Linköping University

Ladda ner artikel

Ingår i: Proceedings of SIGRAD 2011. Evaluations of Graphics and Visualization — Efficiency; Usefulness; Accessibility; Usability; November 17-18; 2011; KTH; Stockholm; Sweden

Linköping Electronic Conference Proceedings 65:6, s. 35-42

Visa mer +

Publicerad: 2011-11-21

ISBN: 978-91-7393-008-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


The domain of medical imaging is naturally moving towards methods that can represent; and account for; local uncertainties in the image data. Even so; fast and efficient solutions that take uncertainty into account are not readily available even for common problems such as gradient estimation. In this work we present a CUDA implementation of Normalized Convolution; an uncertainty-aware image processing technique; well established in the signal processing domain. Our results show that up to 100X speedups are possible; which enables full resolution CT images to be processed at interactive processing speeds; fulfilling demands of both efficiency and interactivity that exist in the medical domain.


Inga nyckelord är tillgängliga


[BA10] BRINK J. A.; AMIS JR E. S.: ImageWisely: a campaign to increase awareness about adult radiation protection. Radiology 257; 3 (2010); 601–602. 1

[BCM05] BUADES A.; COLL B.; MOREL J. M.: A review of image denoising algorithms; with a new one. Simul 4 (2005); 490–530. 2

[Can86] CANNY J.: A computational approach to edge detection. IEEE transactions on pattern analysis and machine intelligence (1986). 1

[Far02] FARNEBCK G.: Polynomial Expansion for Orientation and Motion Estimation. PhD thesis; Linkping University; Sweden; SE-581 83 Linkoping; Sweden; 2002. Dissertation No 790; ISBN 91-7373-475-6. 2; 4

[GK83] GRANLUND G. H.; KNUTSSON H.: Contrast of Structured and Homogenous Representations. In Physical and Biological Processing of Images; Braddick O. J.; Sleigh A. C.; (Eds.). Springer Verlag; Berlin; 1983; pp. 282–303. 1

[GK95] GRANLUND G. H.; KNUTSSON H.: Signal Processing for Computer Vision. Kluwer Academic Publishers; 1995. ISBN 0-7923-9530-1. 3

[Gra78] GRANLUND G. H.: In search of a general picture processing operator. Computer Graphics and Image Processing 8; 2 (1978); 155–173. 1

[HKRs 06] HADWIGER M.; KNISS J. M.; REZK-SALAMA C.; WEISKOPF D.; ENGEL K.: Real-time Volume Graphics. A. K. Peters; Ltd.; Natick; MA; USA; 2006. 1

[Joh04] JOHNSON C.: Top Scientific Visualization Research Problems. IEEE Computer Graphics and Applications 24; 4 (2004); 13–17. 1

[Kay01] KAY S. M.: Fundamentals o Statistical Signal Processing; Volume 1. Prentice Hall; 2001; 2001. 1

[KKH02] KNISS J.; KINDLMANN G.; HANSEN C.: Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics 8 (July 2002); 270–285. 1

[Knu89] KNUTSSON H.: Representing local structure using tensors. In The 6th Scandinavian Conference on Image Analysis (Oulu; Finland; June 1989); -; pp. 244–251. Report LiTH-ISY-I- 1019; Computer Vision Laboratory; Linköping University; Sweden; 1989. 1

[Kre89] KREYSIG E.: Introductory Functional Analysis with Applications. Wiley; 1989. 3

[KS08] KIRBY R. M.; SILVA C. T.: The need for verifiable visualization. IEEE Comput. Graph. Appl. 28 (September 2008); 78–83. 1

[KW93a] KNUTSSON H.; WESTIN C. F.: Normalized and differential convolution. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; x (1993); 515–523. 1

[KW93b] KNUTSSON H.; WESTIN C.-F.: Normalized and differential convolution. In Computer Vision and Pattern Recognition; 1993. Proceedings CVPR ’93.; 1993 IEEE Computer Society Conference on (jun. 1993); IEEE; pp. 515 –523. 2

[KWW93] KNUTSSON H.; WESTIN C.-F.; WESTELIUS C.-J.: Filtering of Uncertain Irregularly Sampled Multidimensional Data. In Twenty-seventh Asilomar Conf. on Signals; Systems & Computers (Pacific Grove; California; USA; November 1993); IEEE; pp. 1301–1309. 2

[LLPY07] LUNDSTRÖM C.; LJUNG P.; PERSSON A.; YNNERMAN A.: Uncertainty visualization in medical volume rendering using probabilistic animation. IEEE transactions on visualization and computer graphics 13; 6 (2007); 1648–1655. 1

[LP11] LUNDSTRÖM C.; PERSSON A.: Characterizing visual analytics in diagnostic imaging. International Workshop on Visual Analytics (2011).1

[LS81] LANCASTER P.; SALKAUSKAS K.: Surfaces Generated by Moving Least Squares Methods. Mathematics of Computation 37; 155 (1981); 141–158. 3

[Mil] MILANFAR P.: A tour of modern image filtering. To appear in IEEE Signal Processing Magazine. 2

[MM04] MÜHLICH M.; MESTER R.: A statistical extension of normalized convolution and its usage for image interpolation. Proceedings of European Confereance on Signal Processing; (EuraSip) (2004). 2

[Nvi09] NVIDIA CORPORATION: NVIDIA CUDA C Programming Best Practices Guide CUDA Toolkit 2.3. 4

[Nvi10] NVIDIA CORPORATION: Nvidia CUDA Programming Guide Version 2.3. 4

[TM98] TOMASI C.; MANDUCHI R.: Bilateral filtering for gray and color images. Computer Vision; IEEE International Conference on 0 (1998); 839. 3

Citeringar i Crossref