Khoa Tan Nguyen
Scientific Visualization Group, Linköping University, Sweden
Timo Ropinski
Scientific Visualization Group, Linköping University, Sweden
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Ingår i: Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden
Linköping Electronic Conference Proceedings 94:2, s. 11-16
Publicerad: 2013-11-04
ISBN: 978-91-7519-455-4
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Recent advances in medical imaging technology enable dynamic acquisitions of objects under movement. The acquired dynamic data has shown to be useful in different application scenarios. However; the vast amount of timevarying data put a great demand on robust and efficient algorithms for extracting and interpreting the underlying information. In this paper; we present a gpu-based approach for feature tracking in time-varying volumetric data set based on the Scale Invariant Feature Transform (SIFT) algorithm. Besides; the improved performance; this enables us to robustly and efficiently track features of interest in the volumetric data over the time domain. As a result; the proposed approach can serve as a foundation for more advanced analysis on the features of interest in dynamic data sets. We demonstrate our approach using a time-varying data set for the analysis of internal motion of breathing lungs.
[AKB?08] ALLAIRE S.; KIM J. J.; BREEN S. L.; JAFFRAY D. A.; PEKAR V.: Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In Computer Vision and Pattern Recognition Workshops; 2008. CVPRW’08. IEEE Computer Society Conference on (2008); IEEE; pp. 1–8. 2; 3
[BL97] BEIS J.; LOWE D.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Computer Vision and Pattern Recognition; 1997. Proceedings.; 1997 IEEE Computer Society Conference on (1997); pp. 1000–1006.
3
[CCG?09] CASTILLO R.; CASTILLO E.; GUERRA R.; JOHNSON V. E.; MCPHAIL T.; GARG A. K.; GUERRERO T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Physics in Medicine and Biology 54; 7 (2009); 1849. 4; 5
[CCZG09] CASTILLO E.; CASTILLO R.; ZHANG Y.; GUERRERO T.: Compressible image registration for thoracic computed tomography images. Journal of Medical and Biological Engineering 29; 5 (2009); 222–233. 4; 5
[CH07] CHEUNG W.; HAMARNEH G.: n-SIFT: N-dimensional scale invariant feature transform for matching medical images. In Biomedical Imaging: From Nano to Macro; 2007. ISBI 2007. 4th IEEE International Symposium on (2007); IEEE; pp. 720–723. 2
[CH09] CHEUNG W.; HAMARNEH G.: n-SIFT: n-dimensional scale invariant feature transform. IEEE Transactions on Image Processing 18; 9 (2009); 2012–2021. 2
[FB81] FISCHLER M. A.; BOLLES R. C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the
ACM 24; 6 (1981); 381–395. 3
[FBMB] FLITTON G.; BRECKON T.; MEGHERBI BOUALLAGU N.: Object Recognition using 3D SIFT in Complex CT Volumes. In British Machine Vision Conference 2010; British Machine Vision Association; pp. 11.1–11.12. 2
[HMS?07] HEYMANN S.; MULLER K.; SMOLIC A.; FROHLICH B.; WIEGAND T.: Sift implementation and optimization for general-purpose gpu. In Proceedings of the international conference in Central Europe on computer graphics; visualization and computer vision (2007); p. 144. 2; 3
[Lin94] LINDEBERG T.: Scale-space theory: A basic tool for analyzing structures at different scales. Journal of applied statistics 21; 1-2 (1994); 225–270. 2
[Low99] LOWE D. G.: Object recognition from local scaleinvariant features. Computer Vision; 1999; The Proceedings of the Seventh IEEE International Conference on 2 (1999); 1150–1157. 1
[Low04] LOWE D. G.: Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision 60; 2 (2004); 91–110. 1; 2; 3; 4
[MTS?05] MIKOLAJCZYK K.; TUYTELAARS T.; SCHMID C.; ZISSERMAN A.; MATAS J.; SCHAFFALITZKY F.; KADIR T.; GOOL L. V.: A comparison of affine region detectors. International journal of computer vision 65; 1 (2005); 43–72. 2
[MW47] MANN H. B.; WHITNEY D. R.: On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics 18; 1 (1947); 50–60. 4
[NQY?08] NI D.; QU Y.; YANG X.; CHUI Y. P.; WONG T.-T.; HO S. S.; HENG P. A.: Volumetric Ultrasound Panorama Based on 3D SIFT. In MICCAI ’08: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention; Part II (Sept. 2008); Springer-Verlag. 2
[PPP?12] PAGANELLI C.; PERONI M.; PENNATI F.; BARONI G.; SUMMERS P.; BELLOMI M.; RIBOLDI M.: Scale invariant feature transform as feature tracking method in 4d imaging: A feasibility study. In Engineering in Medicine and Biology Society (EMBC); 2012 Annual International Conference of the IEEE (2012); pp. 6543–6546. 2; 4; 5
[SAS07] SCOVANNER P.; ALI S.; SHAH M.: A 3-dimensional SIFT descriptor and its application to action recognition. In Proceedings of the 15th international conference on Multimedia (2007); ACM; pp. 357–360. 2
[TWICA10] TOEWS M.; WELLS III W.; COLLINS D. L.; ARBEL T.: Feature-based morphometry: Discovering group-related anatomical patterns. NeuroImage 49; 3 (2010); 2318–2327. 2
[YWC12] YU T.-H.; WOODFORD O. J.; CIPOLLA R.: A Performance Evaluation of Volumetric 3D Interest Point Detectors. International Journal of Computer Vision 102; 1-3 (Sept. 2012); 180–197. 2
