© The Institution of Engineering and Technology
This paper presents a novel non-rigid multimodal registration method that relies on three basic steps: first, an initial approximation of the deformation field is obtained by a parametric registration technique based on particle filtering; second, an intensity mapping based on local variability measures (LVM) is applied over the two images in order to overcome the multimodal restriction between them; and third, an optical flow method is used in an iterative way to find the remaining displacements of the deformation field. Hence the new methodology offers a solution for multimodal NRR by a quadratic optimisation over a convex surface, which allows independent motion of each pixel, in contrast to methods that parameterise the deformation space. To evaluate the proposed method, a set of magnetic resonance/computed tomography clinical studies (pre- and post-radiotherapy treatment) of three patients with cerebral tumour deformations of the brain structures was employed. The resulting registration was evaluated both qualitatively and quantitatively by standard indices of correspondence over anatomical structures of interest in radiotherapy (brain, tumour and cerebral ventricles). These results showed that one of the proposed LVM (entropy) offers a superior performance in estimating the non-rigid deformation field.
References
-
-
1)
-
2)
-
20. Mejia-Rodriguez, A., Arce-Santana, E.R., Scalco, E., Tresoldi, D., Mendez, M.O., et al: ‘Elastic registration based on particle filter in radiotherapy images with brain deformations’. Proc. Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, 2011, pp. 8049–8052.
-
3)
-
33. Heimann, T., Van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., et al: ‘Comparison and evaluation of methods for liver segmentation from CT datasets’, IEEE Trans. Med. Imaging, 2009, 28, (8), pp. 1251–1265 (doi: 10.1109/TMI.2009.2013851).
-
4)
-
P.W.P. Josien ,
J.B.M. Antoine ,
A.V. Max
.
Image registration by maximization of combined mutual information and gradient information.
IEEE Trans. Med. Imag.
,
8 ,
809 -
814
-
5)
-
15. Bin, L., Lianfang, T., Shanxing, O.: ‘Rapid multimodal medical image registration and fusion in 3D conformal radiotherapy treatment planning’. Proc. Int. Conf. Bioinformatics and Biomedical Engineering, 2010, pp. 1–5..
-
6)
-
11. Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: ‘A database and evaluation methodology for optical flow’, Int. J. Comput. Vis., 2011, 92, (1), pp. 1–31 (doi: 10.1007/s11263-010-0390-2).
-
7)
-
2. Modersitzki, J.: ‘Numerical methods for image registration’ (Oxford University Press, New York, 2004).
-
8)
-
4. Nocedal, J., Wright, S.J.: ‘Numerical optimization’ (Springer, New York, 2006).
-
9)
-
31. Faggiano, E., Fiorino, C., Scalco, E., Broggi, S., Cattaneo, M., et al: ‘An automatic contour propagation method to follow parotid glands deformation during head-and-neck cancer tomotherapy’, Phys. Med. Biol., 2011, 56, (3), pp. 775–791 (doi: 10.1088/0031-9155/56/3/015).
-
10)
-
34. Studholme, C., Hill, D.L., Hawkes, D.J.: ‘An overlap invariant entropy measure of 3d medical image alignment’, Pattern Recognit., 1999, 32, (1), pp. 71–86 (doi: 10.1016/S0031-3203(98)00091-0).
-
11)
-
B. Zitova
.
Image registration methods: a survey.
Image Vis. Comput.
,
977 -
1000
-
12)
-
3. Pluim, J.P., Fitzpatrick, J.M.: ‘Image registration’, IEEE Trans. Med. Imaging, 2003, 22, (11), pp. 1341–1343. (doi: 10.1109/TMI.2003.819272).
-
13)
-
23. Simon, D.: ‘Optimal state estimation’ (Wiley, New Jersey, 2006).
-
14)
-
16. Xuan, J., Wang, Y., Freedman, M.T., Adali, T., Shields, P.: ‘Nonrigid medical image registration by finite-element deformable sheet-curve models’, Int. J. Biomed. Imaging, 2006, 2006, pp. 1–9 (doi: 10.1155/IJBI/2006/73430).
-
15)
-
13. Arce-Santana, E.R., Campos-Delgado, D.U., Alba, A.: ‘Affine image registration guided by particle filter’, IET Image Process., 2012, 6, (5), pp. 455–462 (doi: 10.1049/iet-ipr.2011.0083).
-
16)
-
21. Reducindo, I., Arce-Santana, E.R., Campos-Delgado, D.U., Alba, A.: ‘Evaluation of multimodal medical image registration based on particle filter’. Proc. Seventh Int. Conf. Electrical Engineering, Computing Science and Automatic Control, 2010, pp. 406–411.
-
17)
-
27. Biswal, P.C.: ‘Numerical analysis’ (Prentice-Hall of India Pvt. Ltd., New Delhi, 2008).
-
18)
-
M.S. Arulampalam ,
S. Maskell ,
N. Gordon ,
T. Clapp
.
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking.
IEEE Trans. Signal Process.
,
2 ,
174 -
188
-
19)
-
W.M. Wells ,
P.A. Viola ,
H. Atsumi ,
S. Nakajima ,
R. Kikinis
.
Multi-modal volumen registration by maximization of mutual information.
Med. Image Anal.
,
35 -
51
-
20)
-
26. Van Trees, H.L.: ‘Detection, estimation and modulation theory: part 1’ (Wiley, New Jersey, 2001).
-
21)
-
30. McAuliffe, M.J., Lalonde, F.M., McGarry, D., Gandler, W., Csaky, K., et al: ‘Medical image processing, analysis and visualization in clinical research’. Proc. 14th IEEE Symp. on Computer-Based Medical Systems, 2001, pp. 381–386.
-
22)
-
25. Horn, B.K., Schunck, B.G.: ‘Determining optical flow: a retrospective’, Artif. Intell., 1993, 59, (1–2), pp. 81–87 (doi: 10.1016/0004-3702(93)90173-9).
-
23)
-
14. Rueckert, D., Aljabar, P.: ‘Nonrigid registration of medical images: theory, methods, and applications’, IEEE Signal Process. Mag., 2010, 27, (4), pp. 113–119 (doi: 10.1109/MSP.2010.936850).
-
24)
-
10. Man, K.F., Tang, K.S., Kwong, S.: ‘Genetic algorithms concepts and designs’ (Springer, London, 2001).
-
25)
-
9. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: ‘Digital image processing using MATLAB’ (Gatesmark Publishing, 2009).
-
26)
-
12. Das, A., Bhattacharya, M.: ‘Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization’, Neural Comput. Appl., 2010, 20, (2), pp. 223–237 (doi: 10.1007/s00521-010-0374-8).
-
27)
-
17. Serifovic-Trbalic, A., Demirovic, D., Prljaca, N., Szekely, G., Cattin, P.C.: ‘Intensity-based elastic registration incorporating anisotropic landmark errors and rotational information’, Int. J. Comput. Assist. Radiol. Surgery, 2009, 4, (5), pp. 463–468 (doi: 10.1007/s11548-009-0358-2).
-
28)
-
29. Schaefer, S., McPhail, T., Warren, J.: ‘Image deformation using moving least squares’. ACM Trans. Graph., Proc. ACM SIGGRAPH2006, 25, (3), pp. 533–540 (doi: 10.1145/1141911.1141920).
-
29)
-
22. Reducindo, I., Arce-Santana, E.R., Campos-Delgado, D.U., Vigueras-Gomez, F.: ‘Non-rigid multimodal image registration based on local variability measures and optical flow’. Proc. Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, 2012, pp. 1133–1136..
-
30)
-
24. Reducindo, I., Arce-Santana, E.R., Campos-Delgado, D.U., Alba, A., Vigueraz-Gomez, F.: ‘An exploration of multimodal similarity metrics for parametric image registration based on particle filtering’. Proc. Eighth Int. Conf. Electrical Engineering, Computing Science and Automatic Control, 2011, pp. 1–6.
-
31)
-
19. Arce-Santana, E.R., Campos-Delgado, D.U., Alba, A.: ‘A non-rigid multimodal image registration method based on particle filter and optical flow’. Proc. Sixth Int. Conf. Advances in Visual Computing, 2010, pp. 35–44.
-
32)
-
32. Wang, H., Garden, A.S., Zhang, L., Wei, X., Ahamad, A., et al: ‘Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method’, Int. J. Radiat. Oncol. Biol. Phys., 2008, 72, (1), pp. 210–219 (doi: 10.1016/j.ijrobp.2008.05.008).
-
33)
-
F. Maes ,
A. Collignona ,
A. Vandermenlen ,
G. Marchal
.
Multimodality image registration by maximization of mutual information.
IEEE Trans. Med. Imaging
,
2 ,
187 -
198
-
34)
-
P.W.P. Josien ,
J.B.M. Antoine ,
A.V. Max
.
Mutual-information-based registration of medical images: a survey.
IEEE Trans. Med. Imag.
,
8 ,
986 -
1004
-
35)
-
18. Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., et al: ‘Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration’, Neuroimage, 2009, 46, (3), pp. 786–802 (doi: 10.1016/j.neuroimage.2008.12.037).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2013.0705
Related content
content/journals/10.1049/iet-ipr.2013.0705
pub_keyword,iet_inspecKeyword,pub_concept
6
6