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Signed-distance function based non-rigid registration of image series with varying image intensity
a. | Department of Mathematics, FNSPE, Czech Technical University in Prague, Trojanova 13,120 00 Prague, Czech Republic |
b. | Department of Radiology, Institute for clinical and experimental medicine, Vídeňská 1958/9, Praha 4,140 21, Czech Republic |
c. | Inria, France |
d. | LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, France |
e. | School of Biomedical Engineering & Imaging Sciences, St Thomas' Hospital, King's College London, UK |
In this paper we propose a method for locally adjusted optical flow-based registration of multimodal images, which uses the segmentation of object of interest and its representation by the signed-distance function (OF$ ^{dist} $ method). We deal with non-rigid registration of the image series acquired by the Modiffied Look-Locker Inversion Recovery (MOLLI) magnetic resonance imaging sequence, which is used for a pixel-wise estimation of $ T_1 $ relaxation time. The spatial registration of the images within the series is necessary to compensate the patient's imperfect breath-holding. The evolution of intensities and a large variation of image contrast within the MOLLI image series, together with the myocardium of left ventricle (the object of interest) typically not being the most distinct object in the scene, makes the registration challenging. The paper describes all components of the proposed OF$ ^{dist} $ method and their implementation. The method is then compared to the performance of a standard mutual information maximization-based registration method, applied either to the original image (MIM) or to the signed-distance function (MIM$ ^{dist} $). Several experiments with synthetic and real MOLLI images are carried out. On synthetic image with a single object, MIM performed the best, while OF$ ^{dist} $ and MIM$ ^{dist} $ provided better results on synthetic images with more than one object and on real images. When applied to signed-distance function of two objects of interest, MIM$ ^{dist} $ provided a larger registration error, but more homogeneously distributed, compared to OF$ ^{dist} $. For the real MOLLI image series with left ventricle pre-segmented using a level-set method, the proposed OF$ ^{dist} $ registration performed the best, as is demonstrated visually and by measuring the increase of mutual information in the object of interest and its neighborhood.
References:
[1] |
J. F. Aujol and G. Aubert, Signed distance functions and viscosity solutions of discontinuous Hamilton-Jacobi Equations, INRIA Res. Rep, 4507 (2002). Google Scholar |
[2] |
V. Caselles, R. Kimmel and G. Sapiro, Geodesic active contours, Proceedings of Fifth International Conference on Computer Vision, (1995), 694-699. Google Scholar |
[3] |
S. Chen and A. Rahman, Contrast enhancement using recursive mean-separate histogram equalisation for scalable brightness preservation, IEEE Transactions on Consumer Electronics, 49 (2003), 1301-1309. Google Scholar |
[4] |
L. C. Evans and J. Spruck,
Motion of level sets by mean curvature. Ⅰ, Journal of Differential Geometry, 33 (1991), 635-681.
doi: 10.4310/jdg/1214446559. |
[5] |
M. A. Fischler and R. A. Elschlager,
The representation and matching of pictorial structures, IEEE Transactions on computers, C-22 (1973), 67-92.
doi: 10.1109/T-C.1973.223602. |
[6] |
I. M. Gelfand, R. A. Silverman and et al., Calculus of Variations, Courier Corporation, 2000. Google Scholar |
[7] |
A. Handlovičová, K. Mikula and F. Sgallari,
Semi-implicit complementary volume scheme for solving level set like equations in image processing and curve evolution, Numerische Mathematik, 93 (2003), 675-695.
doi: 10.1007/s002110100374. |
[8] |
B. K. P. Horn and B. G. Schunck,
Determining optical flow, Artificial Intelligence, 17 (1981), 185-203.
doi: 10.1016/0004-3702(81)90024-2. |
[9] |
J. Jost, Postmodern Analysis, Third edition, Universitext, Springer-Verlag, Berlin, 2005. |
[10] |
V. Klement, T. Oberhuber and D. Ševčovič,
Application of the level-set model with constraints in image segmentation, Numerical Mathematics: Theory, Methods and Applications, 9 (2016), 147-168.
doi: 10.4208/nmtma.2015.m1418. |
[11] |
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal and P. Suetens, Multimodality image registration by maximisation of mutual information, IEEE Transactions on Medical Imaging, 16 (1997), 187-198. Google Scholar |
[12] |
J. A. Maintz and M. A. Viergever,
A survey of medical image registration, Medical Image Analysis, 2 (1998), 1-36.
doi: 10.1016/S1361-8415(01)80026-8. |
[13] |
T. Makela, P. Clarysse, O. Sipila, N. Pauna, Q. C. Pham, T. Katila and I. E. Magnin,
A review of cardiac image registration methods, IEEE Transactions on Medical Imaging, 21 (2002), 1011-1021.
doi: 10.1109/TMI.2002.804441. |
[14] |
S. Osher and J. A. Sethian,
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, 79 (1988), 12-49.
doi: 10.1016/0021-9991(88)90002-2. |
[15] |
D. P. Peng, B. Merriman, S. Osher, H. K. Zhao and M. Kang,
A PDE-based fast local level set method, Journal of Computational Physics, 155 (1999), 410-438.
doi: 10.1006/jcph.1999.6345. |
[16] |
P. Perona and J. Malik,
Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639.
doi: 10.1109/34.56205. |
[17] |
G. Peyré, M. Péchaud, R. Keriven, L. D. Cohen and et al., Geodesic methods in computer vision and graphics, Foundations and Trends® in Computer Graphics and Vision, 5 (2010), 197–397. Google Scholar |
[18] |
R. Precup, Methods in Nonlinear Integral Equations, Kluwer Academic Publishers, Dordrecht, 2002.
doi: 10.1007/978-94-015-9986-3. |
[19] |
D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach and D. J. Hawkes,
Nonrigid registration using free-form deformations: Application to breast MR images, IEEE Transactions on Medical Imaging, 18 (1999), 712-721.
doi: 10.1109/42.796284. |
[20] |
J. A. Sethian,
Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws, Journal of Differential Geometry, 31 (1990), 131-161.
doi: 10.4310/jdg/1214444092. |
[21] |
J. A. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge Monographs on Applied and Computational Mathematics, 3. Cambridge University Press, Cambridge, 1996. |
[22] |
T. W. Tang and A. C. Chung, Non-rigid image registration using graph-cuts, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, (2007), 916-924. Google Scholar |
show all references
References:
[1] |
J. F. Aujol and G. Aubert, Signed distance functions and viscosity solutions of discontinuous Hamilton-Jacobi Equations, INRIA Res. Rep, 4507 (2002). Google Scholar |
[2] |
V. Caselles, R. Kimmel and G. Sapiro, Geodesic active contours, Proceedings of Fifth International Conference on Computer Vision, (1995), 694-699. Google Scholar |
[3] |
S. Chen and A. Rahman, Contrast enhancement using recursive mean-separate histogram equalisation for scalable brightness preservation, IEEE Transactions on Consumer Electronics, 49 (2003), 1301-1309. Google Scholar |
[4] |
L. C. Evans and J. Spruck,
Motion of level sets by mean curvature. Ⅰ, Journal of Differential Geometry, 33 (1991), 635-681.
doi: 10.4310/jdg/1214446559. |
[5] |
M. A. Fischler and R. A. Elschlager,
The representation and matching of pictorial structures, IEEE Transactions on computers, C-22 (1973), 67-92.
doi: 10.1109/T-C.1973.223602. |
[6] |
I. M. Gelfand, R. A. Silverman and et al., Calculus of Variations, Courier Corporation, 2000. Google Scholar |
[7] |
A. Handlovičová, K. Mikula and F. Sgallari,
Semi-implicit complementary volume scheme for solving level set like equations in image processing and curve evolution, Numerische Mathematik, 93 (2003), 675-695.
doi: 10.1007/s002110100374. |
[8] |
B. K. P. Horn and B. G. Schunck,
Determining optical flow, Artificial Intelligence, 17 (1981), 185-203.
doi: 10.1016/0004-3702(81)90024-2. |
[9] |
J. Jost, Postmodern Analysis, Third edition, Universitext, Springer-Verlag, Berlin, 2005. |
[10] |
V. Klement, T. Oberhuber and D. Ševčovič,
Application of the level-set model with constraints in image segmentation, Numerical Mathematics: Theory, Methods and Applications, 9 (2016), 147-168.
doi: 10.4208/nmtma.2015.m1418. |
[11] |
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal and P. Suetens, Multimodality image registration by maximisation of mutual information, IEEE Transactions on Medical Imaging, 16 (1997), 187-198. Google Scholar |
[12] |
J. A. Maintz and M. A. Viergever,
A survey of medical image registration, Medical Image Analysis, 2 (1998), 1-36.
doi: 10.1016/S1361-8415(01)80026-8. |
[13] |
T. Makela, P. Clarysse, O. Sipila, N. Pauna, Q. C. Pham, T. Katila and I. E. Magnin,
A review of cardiac image registration methods, IEEE Transactions on Medical Imaging, 21 (2002), 1011-1021.
doi: 10.1109/TMI.2002.804441. |
[14] |
S. Osher and J. A. Sethian,
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, 79 (1988), 12-49.
doi: 10.1016/0021-9991(88)90002-2. |
[15] |
D. P. Peng, B. Merriman, S. Osher, H. K. Zhao and M. Kang,
A PDE-based fast local level set method, Journal of Computational Physics, 155 (1999), 410-438.
doi: 10.1006/jcph.1999.6345. |
[16] |
P. Perona and J. Malik,
Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639.
doi: 10.1109/34.56205. |
[17] |
G. Peyré, M. Péchaud, R. Keriven, L. D. Cohen and et al., Geodesic methods in computer vision and graphics, Foundations and Trends® in Computer Graphics and Vision, 5 (2010), 197–397. Google Scholar |
[18] |
R. Precup, Methods in Nonlinear Integral Equations, Kluwer Academic Publishers, Dordrecht, 2002.
doi: 10.1007/978-94-015-9986-3. |
[19] |
D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach and D. J. Hawkes,
Nonrigid registration using free-form deformations: Application to breast MR images, IEEE Transactions on Medical Imaging, 18 (1999), 712-721.
doi: 10.1109/42.796284. |
[20] |
J. A. Sethian,
Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws, Journal of Differential Geometry, 31 (1990), 131-161.
doi: 10.4310/jdg/1214444092. |
[21] |
J. A. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge Monographs on Applied and Computational Mathematics, 3. Cambridge University Press, Cambridge, 1996. |
[22] |
T. W. Tang and A. C. Chung, Non-rigid image registration using graph-cuts, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, (2007), 916-924. Google Scholar |








9462.47 | 3130.83 | 2173.93 | 2607.66 |
9462.47 | 3130.83 | 2173.93 | 2607.66 |
6654.44 | 2199.07 | 4121.95 | 3602.27 |
6654.44 | 2199.07 | 4121.95 | 3602.27 |
0.849915 | 0.239367 | 0.2950385 |
0.849915 | 0.239367 | 0.2950385 |
6654.44 | 5062.1 | 4121.95 |
6654.44 | 5062.1 | 4121.95 |
1 | 1.1556 | 1.2184 | 1.2273 | 1.2170 |
2 | 1.1012 | 1.2034 | 1.2052 | 1.1964 |
1 | 1.1556 | 1.2184 | 1.2273 | 1.2170 |
2 | 1.1012 | 1.2034 | 1.2052 | 1.1964 |
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