Subscribe to RSS
Structure-preserving Interpolation of Temporal and Spatial Image Sequences Using an Optical Flow-based Method
20 January 2018 (online)
Objectives: Modern tomographic imaging devices enable the acquisition of spatial and temporal image sequences. But, the spatial and temporal resolution of such devices is limited and therefore image interpolation techniques are needed to represent images at a desired level of discretization. This paper presents a method for structure-preserving interpolation between neighboring slices in temporal or spatial image sequences.
Methods: In a first step, the spatiotemporal velocity field between image slices is determined using an optical flow-based registration method in order to establish spatial correspondence between adjacent slices. An iterative algorithm is applied using the spatial and temporal image derivatives and a spatiotemporal smoothing step. Afterwards, the calculated velocity field is used to generate an interpolated image at the desired time by averaging intensities between corresponding points. Three quantitative measures are defined to evaluate the performance of the interpolation method.
Results: The behaviorand capability of the algorithm is demonstrated by synthetic images. A population of 17 temporal and spatial image sequences are utilized to compare the optical flow-based interpolation method to linear and shape-based interpolation. The quantitative results show that the optical flow-based method outperforms the linear and shape-based interpolation statistically significantly.
Conclusions: The interpolation method presented is able to generate image sequences with appropriate spatial or temporal resolution needed for image comparison, analysis or visualization tasks. Quantitative and qualitative measures extracted from synthetic phantoms and medical image data show that the new method definitely has advantages over linear and shape-based interpolation.
- 1 Grevera GJ, Udupa JK. An objective comparison of 3-D image interpolation methods. IEEE Trans Med Imaging 1998; 17 (04) 642-652.
- 2 Lee T-Y, Lin C-H. Feature-guided shape-based image interpolation. IEEE Trans Med Imaging 2002; 21 (12) 1479-1489.
- 3 Raya SP, Udupa JK. Shape-based interpolation of multidimensional objects. IEEE Trans Med Imaging 1990; 09 (01) 32-42.
- 4 Lee T-Y, Wang W-H. Morphology-based three-dimensional interpolation. IEEE Trans Med Imaging 2000; 19 (07) 711-721.
- 5 Grevera GJ, Udupa JK. Shape-basedinterpolation of multidimensional grey-level images. IEEE Trans Med Imaging 1996; 15 (06) 881-892.
- 6 Lehmann TM, Goenner C, Spitzer K. Survey: Interpolation methods in medical image processing. IEEE Trans Medical Imaging. 1999 18. 11.
- 7 Meijering EHW, Niessen WJ, Viergever MA. Quantitative evaluation of convolution-based methods for medical image interpolation. Med Image Anal 2001; 05: 111-126.
- 8 Thevenaz P, Blu T, Unser M. Interpolation revisited. IEEE Trans Med Imaging 2000; 19 (07) 739-758.
- 9 Frangi A, Niessen W, Viergever MA. Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans Med Imaging 2001; 20 (01) 2-25.
- 10 Low DA, Nystrom M, Kalinin E, Parikh P, Dempsey JF, Bradley JD, Mutic S, Wahab SH, Islam T, Christensen G, Politte DG, Whiting BR. Amethod for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. Medical Physics 2003; 30 (06) 1254-1263.
- 11 Ehrhardt J, Werner R, Frenzel T, Säring D, Lu W, Low D, Handels H. Reconstruction of4D-CT data sets acquired during free breathing for the analysis of respiratory motion. Reinhardt JM, Pluim PW. SPIE Medical Imaging; 2006. San Diego: 2006: 2141-2148.
- 12 Handels H, Werner R, Frenzel T, Säring D, Lu W, Low D, Ehrhardt J. Generation of 4D CT Image Data and Analysis of Lung Tumour Mobility during the Breathing Cycle. Hasman A, Haux R, van der Lei J, Clercq E, France FHR. Proc Medical Informatics Europe, MIE 2006. 2006: 977-982.
- 13 Säring D, Ehrhardt J, Stork A, Bansmann PM, Lund G, Handels H. Computer-Assisted Analysis of 4D Cardiac MR Image Sequences after Myocardial Infarction. Methods Inf Med 2006; 04: 377-383.
- 14 Goshtasby A, Turner DA, Ackerman LV. Matching of tomographic slices for interpolation. IEEE Trans Med Imaging. 1992 11. 04.
- 15 Penney GP, Schnabel A, Rueckert D, Viergever MA. Registration-based interpolation. IEEE Trans Med Imaging. 2004 23. 07.
- 16 Barron JL, Fleet DJ, Beauchemin SS. Performance of optical flow techniques. Int J Comp Vision 1994; 12 (01) 43-77.
- 17 Beauchemin SS, Barron JL. The Computation of Optical Flow. ACM Computing Surveys 1995; 27 (03) 433-467.
- 18 Horn BKP, Schunck BG. Determining optical flow. Artificial Intelligence 1981; 17 1-3 185-203.
- 19 Thirion J-P. Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 1998; 02 (03) 243-260.
- 20 Cachier P, Pennec X, Ayache N. Fast non-rigid matching by gradient descent: Study and improvements of the demons algorithm: INRIA. 1999 June. Report No. 3706.
- 21 Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical recipes in C: Cambridge University Press. 1992
- 22 Danielsson P-E. Euclidean Distance Mapping. Comp Graph Img Proc 1980; 14: 227-248.
- 23 Matej S, Furuie S, Herman GT. Relevance of statistically significant differences between reconstruction algorithms. IEEE Trans Image Proc 1996; 05: 554-556.