Accuracy Validation for Medical Image Registration Algorithms: a Review

Chinese Medical Sciences Journal

Volumn 27, Issue 3, 2012, Pages 176-181

  Liu, Zhe ^a   Deng, Xiang ^b   Wang, Guang zhi ^a  

^a TSINGHUA UNIVERSITY   (China)

^b SIEMENS LTD   (China)

Author keywords

accuracy validation; digital phantom; non rigid registration

Indexed keywords

ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; HUMAN; IMAGE QUALITY; REVIEW; VALIDATION STUDY;

ALGORITHMS; DIAGNOSTIC IMAGING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PHANTOMS, IMAGING; VALIDATION STUDIES AS TOPIC;

EID: 84871886702     PISSN: 10019294     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1001-9294(14)60052-3     Document Type: Review

References (38)

1. Klein, S, Staring, M, Murphy, K, et al. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29 (2010), 196–205.
2. Balasubramanian, B, Porkumaran, K, Registration of PET and MR images of human brain using normalized cross correlation algorithm and spatial transformation techniques. J Theor Appl Info Technol 16 (2005), 1–8.
3. Balci, SK, Golland, P, Shenton, M, et al. Free-form B-spline deformation model for groupwise registration. Med Image Comput Comput Assist Interv 10 (2007), 23–30.
4. Metz, CT, Klein, S, Schaap, M, et al. Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach. Med Image Anal 15 (2011), 238–249.
5. Wang, H, Dong, L, O'Daniel, J, et al. Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy. Phys Med Biol 50 (2005), 2887–2905.
6. Crum, WR, Hartkens, T, Hill, DL, et al. Non-rigid image registration: theory and practice. Br J Radiol 77 (2004), S140–S153.
7. Maintz, JB, Viergever, MA, A survey of medical image registration. Med Image Anal 2 (1998), 1–36.
8. Hutton, BF, Braun, M, Software for image registration: algorithms, accuracy, efficacy. Semin Nucl Med 33 (2003), 180–192.
9. Wyawahare, MV, Patil, PM, Abhyankar, HK, et al. Image registration techniques: an overview. International Journal of Signal Process, Image Processing and Pattern Recognition 2 (2009), 11–27.
10. Rueckert, D, Sonoda, LI, Hayes, C, et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18 (1999), 712–721.
11. Scheys, L, Loeckx, D, Spaepen, A, et al. Atlas-based non-rigid image registration to automatically define line-of-action muscle models: a validation study. J Biomech 42 (2009), 565–572.
12. Schnabel, JA, Tanner, C, Castellano-Smith, AD, et al. Validation of nonrigid image registration using finite-element methods: application to breast MR images. IEEE Trans Med Imaging 22 (2003), 238–247.
13. Periaswamy, S, Farid, H, Elastic registration in the presence of intensity variations. IEEE Trans Med Imaging 22 (2003), 865–874.
14. Heinrich, MP, Schnabel, JA, Gleeson, FV, et al. Non-rigid multimodal medical image registration using optical flow and gradient orientation. Proc of Medical Image Understanding and Analysis (MIUA), 2010, Department of Computer Science, University of Warwick, Coventry.
15. Rogelj, P, Kovacic, S, Gee, JC, Validation of a non-rigid registration algorithm for multi-modal data. Proc SPIE 4684 (2002), 299–307.
16. Choe, AS, Gao, Y, Li, X, et al. Accuracy of image registration between MRI and light microscopy in the ex vivo brain. Magn Reson Imaging 29 (2011), 683–692.
17. Lange, T, Lamecker, H, Hunerbein, M, et al. Validation metrics for non-rigid registration of medical images containing vessel trees. Proc Workshop Bildverarbeitung für die Medizin (BVM), 2008, Springer-Verlag, Berlin.
18. Markelj, P, Likar, B, Pernus, F, Standardized evaluation methodology for 3D/2D registration based on the Visible Human data set. Med Phys 37 (2010), 4643–4647.
19. Ireland, RH, Dyker, KE, Barber, DC, et al. Nonrigid image registration for head and neck cancer radiotherapy treatment planning with PET/CT. Int J Radiat Oncol Biol Phys 68 (2007), 952–957.
20. Klein, A, Ghosh, SS, Avants, B, et al. Evaluation of volume-based and surface-based brain image registration methods. Neuroimage 51 (2010), 214–220.
21. Christensen, GE, Geng, X, Kuhl, JG, et al. Introduction to the non-rigid image registration evaluation project (NIREP). Proc SPIE 4057 (2006), 128–135.
22. Yin, LS, Tang, L, Hamarneh, G, et al. Complexity and accuracy of image registration methods in SPECT-guided radiation therapy. Phys Med Biol 55 (2010), 237–246.
23. West, J, Fitzpatrick, JM, Wang, MY, et al. Comparison and evaluation of retrospective inter-modality brain image registration techniques. J Comput Assist Tomogr 21 (1997), 554–566.
24. Seginer, A, Rigid-body point-based registration: The distribution of the target registration error when the fiducial registration errors are given. Med Image Anal 15 (2011), 397–413.
25. Fitzpatrick, JM, West, JB, Maurer, CR, Predicting error in rigid-body point-based registration. IEEE Trans Med Imaging 17 (1998), 694–702.
26. Tomazevic, D, Likar, B, Pernus, F, “Gold standard” data for evaluation and comparison of 3D/2D registration methods. Comput Aided Surg 9 (2004), 137–144.
27. Fitzpatrick, JM, Fiducial registration error and target registration error are uncorrelated. Proc SPIE, 7261, 2009, 7261021 7261012.
28. Rexilius, J, Hahn, HK, Schluter, M, et al. Evaluation of accuracy in MS lesion volumetry using realistic lesion phantoms. Acad Radiol 12 (2005), 17–24.
29. Hipwell, JH, Tanner, C, Crum, WR, et al. A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression. IEEE Trans Med Imaging 26 (2007), 1190–1200.
30. Kybic, J, Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Trans Image Process 19 (2010), 64–73.
31. Johnson, PB, Whalen, SR, Wayson, M, et al. Hybrid patient-dependent phantoms covering statistical distributions of body morphometry in the U.S. adult and pediatric population. Proc IEEE 97 (2009), 2060–2075.
32. Segars, WP, Tsui, BM, MCAT to XCAT: the evolution of 4D computerized phantoms for imaging research. Proc IEEE 97 (2009), 1954–1968.
33. Segars, WP, Sturgeon, G, Mendonca, S, et al. 4D XCAT phantom for multimodality imaging research. Med Phys 37 (2010), 4902–4915.
34. Vandemeulebroucke, J, Sarrut, D, Clarysse, P, et al. The POPI-model, a point-validated pixel-based breathing thorax model. Proc. of the 15th International Conference on the Use of Computer in Radiation Therapy (ICCR), 2007, Novel Digital Publishing, Oakville.
35. Dey, J, Pan, T, Choi, DJ, et al. Estimation of cardiac respiratory-motion by semi-automatic segmentation and registration of non-contrast-enhanced 4D-CT cardiac datasets. IEEE Trans Nucl Sci 56 (2009), 3662–3671.
36. Bauer, S, Seiler, C, Bardyn, T, et al. Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration. Conf Proc IEEE Eng Med Biol Soc 2010 (2010), 4080–4083.
37. Zhang, B, Ma, J, Liu, L, et al. CNMAN: a Chinese adult male voxel phantom constructed from color photographs of a visible anatomical data set. Radiat Prot Dosimetry 124 (2007), 130–136.
38. Zhang, G, Luo, Q, Zeng, S, et al. The development and application of the visible Chinese human model for Monte Carlo dose calculations. Health Phys 94 (2008), 118–125.