IMPLEMENTATION OF MODIFIED CONTROL POINT IMAGE REGISTRATION METHOD
Abstract
Control Point Image Registration (CPIR) method is semi-automatic image registration technique in which control points are selected and matched manually. CPIR method is best suited for images that have distinct features; however, it needs highly skillful expert to select and match control points. This paper describes a modified CPIR method using fine tuning to register images which makes the control point selection and matching nearly independent of expert’s skills. Fine tuning is achieved by applying normalized cross correlation which selects an 11x11 window around the input image control point and a 21x21 template across the reference image control point. The results of modified CPIR method are analyzed and it is found that modified method is more suitable and has low spatial dispersion values as compared to CPIR method.References
S. Damas, O. Cordón and J. SantamarÃa,
IEEE Computational Intelligence Magazine 6.
(2011) 26.
M.V. Wyawahare, P.M. Patil and H.K.
Abhyankar, International Journal of Signal
Processing, Image Processing and Pattern
Recognition 2 (2009) 11.
R.C. Gonzalez, R.E. Woods and S.L. Eddins,
Digital Image Processing Using MATLAB,
nd ed. (2010).
J.B.A. Maintz and M.A. Viergever, Medical
Image Analysis 2 (1998) 1.
M.J. Sullivan, A MATLAB-Based Image
Registration Graphical User Interface System
for P NMR and H MR Images of the Lower
Leg, Proc. of the 2010 IEEE 36th Annual
Bioengineering Conference, Northeast
(2010) 1-2.
MATLAB Documentation Web site. [Online].
http://www.mathworks.com/help/images/regis
tering-an-image.html (2012).
J.M. Fitzpatrick, D.L.G. Hill and C.R. Maurer,
Handbook of Medical Imaging – Medical
Image Processing and Analysis, SPIE Press,
(2009) 449.
B. Zitova´ and J. Flusser, Image and Vision
Computing 21 (2003) 977.
Y. Matsushita, K. Nishino, K. Ikeuchi and M.
Sakauchi, IEEE Transactions on Pattern
Analysis and Machine Intelligence 26 (2004)
L.A. Teverovskiy and O.T. Carmichael,
Feature-Based vs. Intensity-Based Brain
Image Registration: Voxel Level and
Structure Level Performance Evaluation,
Carnegie Mellon University (2006)