Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Rigid Slice-To-Volume Medical Image Registration Through Markov Random Fields

Authors : Roque Porchetto, Franco Stramana, Nikos Paragios, Enzo Ferrante

Published in: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction. It is usually formulated as an optimization problem and solved using standard continuous methods. In this paper, we discuss how this task be formulated as a discrete labeling problem on a graph. Inspired by previous works on discrete estimation of linear transformations using Markov Random Fields (MRFs), we model it using a pairwise MRF, where the nodes are associated to the rigid parameters, and the edges encode the relation between the variables. We compare the performance of the proposed method to a continuous formulation optimized using simplex, and we discuss how it can be used to further improve the accuracy of our approach. Promising results are obtained using a monomodal dataset composed of magnetic resonance images (MRI) of a beating heart.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data
next chapter Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI Data
Footnotes
Literature
1.
Bao, P., Warmath, J., Galloway, R., Herline, A.: Ultrasound-to-computer-tomography registration for image-guided laparoscopic liver surgery. Surg. Endosc. 19, 424–429 (2005)CrossRef
2.
Birkfellner, W., Figl, M., Kettenbach, J., Hummel, J., Homolka, P., Schernthaner, R., Nau, T., Bergmann, H.: Rigid 2D/3D slice-to-volume registration and its application on fluoroscopic CT images. Med. Phys. 34(1), 246 (2007)CrossRef
3.
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE TPAMI 26(9), 1124–1137 (2004)CrossRefMATH
4.
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRef
5.
Fei, B., Duerk, J.L., Boll, D.T., Lewin, J.S., Wilson, D.L.: Slice-to-volume registration and its potential application to interventional MRI-guided radio-frequency thermal ablation of prostate cancer. IEEE Trans. Med. Imaging 22(4), 515–525 (2003)CrossRef
6.
7.
Ferrante, E., Fecamp, V., Paragios, N.: Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation. IJCARS 10, 791–800 (2015)
8.
Ferrante, Enzo, Paragios, Nikos: Non-rigid 2D-3D medical image registration using markov random fields. In: Mori, Kensaku, Sakuma, Ichiro, Sato, Yoshinobu, Barillot, Christian, Navab, Nassir (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 163–170. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-40760-4_​21 CrossRef
9.
10.
Glocker, B., Sotiras, A.: Deformable medical image registration: setting the state of the art with discrete methods. Annu. Rev. Biomed. Eng. 13, 219–244 (2011)CrossRef
11.
Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE TMI 32(7), 1239–1248 (2013)
12.
Huang, X., Moore, J., Guiraudon, G., Jones, D.L., Bainbridge, D., Ren, J., Peters, T.M.: Dynamic 2D ultrasound and 3D CT image registration of the beating heart. IEEE TMI 28(8), 1179–1189 (2009)
13.
Kim, B., Boes, J.L., Bland, P.H., Chenevert, T.L., Meyer, C.R.: Motion correction in fMRI via registration of individual slices into an anatomical volume. Magn. Reson. Med. 41(5), 964–972 (1999)CrossRef
14.
Komodakis, N., Tziritas, G., Paragios, N.: Fast, approximately optimal solutions for single and dynamic MRFs. In: CVPR (2007)
15.
16.
Lempitsky, V., Roth, S., Rother, C.: FusionFlow: discrete-continuous optimization for optical flow estimation. In: CVPR, pp. 1–22 (2008)
17.
Liao, R., Zhang, L., Sun, Y., Miao, S., Chefd’Hotel, C.: A review of recent advances in registration techniques applied to minimally invasive therapy. IEEE TMM 15(5), 983–1000 (2013)
18.
19.
20.
Park, H., Meyer, C.R., Kim, B.: Improved motion correction in fMRI by Joint mapping of slices into an anatomical volume. In: MICCAI, pp. 745–751 (2004)
21.
22.
Sotiras, A., Davatazikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32, 1153–1190 (2013)CrossRef
23.
Tadayyon, H., Lasso, A., Kaushal, A., Guion, P., Fichtinger, G.: Target motion tracking in MRI-guided transrectal robotic prostate biopsy. IEEE TBE 58(11), 3135–3142 (2011)
24.
25.
Xu, R., Athavale, P., Nachman, A., Wright, G.A.: Multiscale registration of real-time and prior MRI data for image-guided cardiac interventions. IEEE TBE 61, 2621–2632 (2014)
26.
Zikic, D., Glocker, B., Kutter, O., Groher, M., Komodakis, N., Khamene, A., Paragios, N., Navab, N.: Markov random field optimization for intensity-based 2D–3D registration. In: SPIE Medical Imaging, p. 762334. International Society for Optics and Photonics (2010)
27.
Zikic, D., Glocker, B., Kutter, O., Groher, M., Komodakis, N., Kamen, A., Paragios, N., Navab, N.: Linear intensity-based image registration by Markov random fields and discrete optimization. Med. Image Anal. 14(4), 550–562 (2010)CrossRef
Metadata
Title
Rigid Slice-To-Volume Medical Image Registration Through Markov Random Fields
Authors
Roque Porchetto
Franco Stramana
Nikos Paragios
Enzo Ferrante
Copyright Year
2017
Book
Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Print ISBN: 978-3-319-61187-7

Electronic ISBN: 978-3-319-61188-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-61188-4_16

Premium Partner

    Image Credits