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:
Analysis of Link Prediction in Directed and Weighted Social Network Structure Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Structural Matching of Control Points Using V-D-L-A Approach for MLS Based Registration of Brain MRI/CT Images and Image Graph Construction Using Minimum Radial Distance

Authors : Hema P. Menon, A. S. Nitheesh

Published in: Intelligent Systems Technologies and Applications

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Image registration using Moving Least Squares (MLS) is a point based method and requires the selection of control points from the source and target images. The selected points need not necessarily be the corresponding points. These points have to be matched to identify the corresponding points between the two images. For this, a new structural method for control point matching method is proposed. For structural matching the control points are represented using a graph structure and the structural properties like the degree of the vertex, length of edges and the angle between edges are used for finding the corresponding points in the source image and the target image. This method is found to be efficient for both mono-modal and multi-modal image registrations, as the topological property represented by the control points are exploited instead of the traditional intensity feature. The accuracy of the registration is computed using the standard Target Registration Error (TRE) Measure and compared with the registration using Thin Plate Splines (TPS). This work also proposes a new approach for constructing image graphs called as Minimum-Radial Distance (MRD) method.

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 Performance Assessment Framework for Computational Models of Visual Attention
next chapter An Interactive and Intelligent Tool for Circuit Component Recognition Through Virtual Reality
Literature
1.
Samavati, N.: Deformable multi-modality image registration based on finite element modeling and moving least squares, MS thesis, Department of Electrical and Computer Engineering and the School of Graduate Studies, Mcmaster University (2009)
2.
Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. In: SIGGRAPH 06, ACM Transactions on Graphics, pp. 533–540 (2006)
3.
Igarashi, T., Hughes, J.: As-rigid-as-possible shape manipulation. In: ACM Transactions on Graphics, vol. 24, pp. 1134–1141 (2005)
4.
Rohr, K., Fornefett, M., Stiehl, H.S.: Spline-based elastic image registration: integration of landmark errors and orientation attributes. Comput. Vis. Image Underst. 90(2), 153–168 (2003)CrossRef
5.
Park, H., Bland, P.H., Meyer, C.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans. Med. Imaging 22(4), 483–492 (2003)CrossRef
6.
Auer, M., Regitnig, P., Holzapfel, G.A.: An automatic non-rigid registration for stained histological sections. IEEE Trans. Image Process. 14(4), 475–486 (2005)CrossRef
7.
Johnson, H.J., Christensen, G.E.: Landmark and intensity based, consistent thin-plate spline image registration. In: Proceedings of International Conference on Information Processing in Medical Imaging, vol. 2082, pp. 329–343 (2001)
8.
Crum, W., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and practice. Br. J. Radiol. 77, 140–152 (2004)CrossRef
9.
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16, 187–198 (1997)CrossRef
10.
Nyúl, L.G., Udupa, J.K., Saha, P.K.: Task-specific comparison of 3-D image registration methods. Med. Imaging Image Process. 4322, 1588–1598 (2001)
11.
Rodríguez-Carranza, C.E., Loew, M.H.: A weighted and deterministic entropy measure for image registration using mutual information. Med. Imaging Image Process. 3338, 155–166 (1998)
12.
Ioannides, A.A., Liu, L.C., Kwapien, J., Drozdz, S., Streit, M.: Coupling of regional activations in a human brain during an object and face affect recognition task. Hum. Brain Mapp. 11(2), 77–92 (2000)CrossRef
13.
Pompe, B., Blidh, P., Hoyer, D., Eiselt, M.: Using mutual information to measure coupling in the cardio respiratory system. IEEE Eng. Med. Biol. Mag. 17, 32–39 (1998)CrossRef
14.
Harris, C.G., Stephens, M.J.: A combined corner and edge detector. In: Proceedings of Fourth Alvey Vision Conference, Manchester, pp. 147–151 (1988)
15.
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)
16.
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Proceedings of 9th European Conference on Computer Vision, Part 1, pp. 404–417 (2006)
17.
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986)CrossRef
18.
Rube, I.E., Sharkas, M., Salman, A., Salem, A.: Automatic selection of control points for remote sensing image registration based on multi-scale SIFT. In: Proceedings of 2011 International Conference on Signal, Image Processing and Applications (SIA 2011). Chennai, India, 17–18 December 2011
19.
Tian, J., Lee, N., Theodore, R., Smith, A., Laine, L.: A partial intensity invariant feature descriptor for multimodal retinal image registration. IEEE Trans. Biomed. Eng. 57(5), 1707–1718 (2010)
20.
Zheng, J., Tian, J., Deng, K., Dai, X., Min, X.U.: Salient feature region: a new method for retinal image registration. IEEE Trans. Inf. Technol. Biomed. 15(2), 221–232 (2011)CrossRef
21.
Tsai, C., Li, C., Yang, G., Lin, K.: The edge-driven dual-bootstrap iterative closest point algorithm for registration of multimodal fluorescein angiogram sequence. IEEE Trans. Med. Imaging 29(3), 636–649 (2010)CrossRef
22.
Holden, M., Hill, D.L.G., Denton, E.R.E., Jarosz, J.M., Cox, T.C.S., Rohlfing, T., Goodey, J., Hawkes, D.J.: Voxel similarity measures for 3-D serial MR brain image registration. IEEE Trans. Med. Imaging 19, 94–102 (2000)CrossRef
23.
Freeborough, P.A., Fox, N.C.: Modelling brain deformations in Alzheimer disease by fluid registration of serial MR images. J. Comput. Assist. Tomogr. 22(5), 838–843 (1998)CrossRef
24.
Talairach, J., Tournoux, P.: Coplanar Stereotaxic Atlas of the Human Brain, p. 1988. Thieme Medical, New York (1998)
25.
Arathi, T., Parameswaran, L.: Image reconstruction from 2D stack of MRI/CT to 3D using shapelets. Int. J. Eng. Technol. (IJET) 6, 2595–2603 (2014)
26.
Bhavana, V., Krishnappa, H.K.: A survey on multi-Modality medical image fusion. In: Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2016, pp. 1326–1329 (2016)
Metadata
Title
Structural Matching of Control Points Using V-D-L-A Approach for MLS Based Registration of Brain MRI/CT Images and Image Graph Construction Using Minimum Radial Distance
Authors
Hema P. Menon
A. S. Nitheesh
Copyright Year
2018
Book
Intelligent Systems Technologies and Applications

Print ISBN: 978-3-319-68384-3

Electronic ISBN: 978-3-319-68385-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-68385-0_30

Premium Partner

    Image Credits