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MRI Brain Image Registration using Hybrid Model

K. Selvanayaki, Dr.M. Karnan

Abstract


Image Registration is an important and challenging factor in the Medical Image Processing. This paper describes a new hybrid model for Image Registration through Magnetic Resonance Image (MRI). This model consists of three phases. In the first phase, film artifacts and unwanted portions of MRI Brain image are removed. . Secondly, the noise and high frequency components are removed using weighted median filter (WM). Finally, Hybrid model is applied for registration phase in which this phase comprises of two methods namely non-rigid and rigid. In non-rigid ,block based technique is implemented in which the   reference image and normal images are split in to number of blocks of size 64×64.Each and every corresponding blocks from reference image and normal images are compared using Ant colony optimization(ACO).if the images are similar, the resultant image obtained is given to rigid method as input. In rigid method, similarity measures are calculated for the given image which includes 1. Contrast checking (CC) 2. Sum of Square Difference (SSD) 3.Calculation of White cells and 4.point mapping.

 


Keywords


Brain tumor, Block Based, Enhancement, Magnetic Resonance Image (MRI), Pre processing, Registration, Rigid and Non -Rigid Technique, Similarity Measure..

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References


Alexandra Flowers MD, “Brain Tumors in t he Older Person “,CancerControl, Volume 7, No.6, pages 523-538,November/December 2000.

André Collignon,Dirk Vandermeulen, Paul Suetens, Guy Marchal,” 3D multi-modality medical image registration using feature space clustering “,SpringerLink, Volume 905/1995, pages 193-204 Berlin 1995.

Alexis Roche, Gregoire Malandain, Nicholas Ayache, Sylvain prima, “Towards a better comprehension Medical Image Registration”, Medical Image Computing and Computer-Assisted Intervention-MICCAI’99,Volume 1679,pages 555-566, 1999.

Ceylan.C, Van der Heide U.A, Bol G.H, Lagendijk .J.J.W,Kotte A.N.T.J,”Assessment of rigid multi-modality image registration consistency using the multiple subvolume registration(MSR) method”, Physics in Medicine Biology, pages 101-108,2005.

Darryl de cunha, Leila Eadie, Benjamin Adams, David Hawkes, “Medical Ultrasound Image similarity measurement by human visual system(HVS) Modelling”, spingerlink, volume 2525,pages 143-164 January, berlin, 2002.

Dirk-Jan Kroon,” Multimodality non-rigid demon algorithm image registration “, Robust Non-Rigid Point Matching, Volume 14, pages 120-126, 16 Sep 2008.

John Ashburner , Karl J. Friston,” Rigid body registration “, The welcome dept of image neuro science , 12 queen square, London.

Konstantinos G. Denpanis,”Relationship Between the Sum of Squared Difference(SSD) and Cross Correlation for Template Matching”, York University , Version 1.0,pages 01, Dec 23,2005.

Kovalev, V.A, Kruggel, F, Gertz, H.-J, von Cramon, D.Y.,” Three-dimensional texture analysis of MRI brain datasets”, Medical Imaging, IEEE Transactions on Texture analysis, Volume 20, Issue 5, pages .424-432,May 2001.

Krzysztof Wrobel, Piotr Porwik,”Comparative Investigations of similarity measure exploited in medical images preselection”, Journal of medical informatics & Technologies, Volume 8,pages 25-31,2004.

Leonid Teverovskiy, Owen Carmichael, Howard Aizenstein,Nicole Lazer, Yanxi Liu,” Feature based Vs intensity-based brain image registration: Voxel level and structure level performance evaluation”, School of Computer Science Carnegie Mellon University, Volume 06,pages 1-30,Nov 2006.

Panos kotsas,” Non-rigid Registration of medical images using an Automated method”,World academy of science, Engineering and Technology , 2005.

Peter Rogeli, Stanislav, Kovacic, James C.Gee, “ Point similarity measures for non-rigid registration of multi-model data source”, Elsevier Science Inc, volume 92, issue1, Pages 112-140 ,October, USA 2003.

Po Lai-Man, Kai Guo,” Simple noncircular correlation method for exhaustive sum square difference matching”,Society of photo-optimal instrumentation engineering,Vol 46, issue 10,2007.

Survival Rate for Brain tumor and Cancer Society, Cancer Facts and Figures, American Cancer Society, US 2004.

Survival Rate for Brain tumor and Cancer, Canadian Cancer Statistics, National Cancer Institute of Canada, 2004.

Thomas Pfluger, Christian Vollmar, Axel Wismuller, Stefan Dresel, Frank Berger, Patrick Suntheim,Gerda Leinsinger,Klaus Hahn,”Quantitative Comparisonof Automatic and Interactive Methods for MRI-SPECT image Registration of the Brain based on 3-Dimensional calculation of error”,The journal of Nuclear Medicine, Vol 41, pages 1823-1829,2000.(16).

Tsutomu Soma, Akihiro Takaki, Satomi Teraoka, Yasushi Ishikawa, Kenya Murase , Kiyoshi Koizumi,” Behaviors of cost functions in image registration between Tl brain tumor single-photon emission computed tomography and magnetic resonance images “,Annals of Nuclear Medicine, SpringerLink, Volume 22, Number 9 / November, 2008.

Wang J., Jiang T, Tianzi Jiang, "Nonrigid registration of brain MRI using NURBS" Pattern Recognition Letters, Volume 28, Pages 214-223, January 2007.

Wilbert McClay,” Comparison of Methods of Registration for MRI Brain Images”,Engineering Research and Technology Report, volume 925,pages 423-4153 .

Xinhui yang, Wolf gang Brik fellner, Peter Niederer,” Automatic Registration of MRI brain “, Springer,International Congress Series, Volume 47,pages 165-172 ,Junoe 2004.

Yeit Han, Hyun Wook park,”Automatic brain MR images registration based on Talairach reference system”,Proceedings of international conference on image processing(ICIP) , Volume 1, Issue , 14-17 Sept. 2003 Page(s): I - 1097-100 vol.1 September 2003.

Yubei Shimada , Koji Uemura Babak A. Ardekani, Tsukasa Nagaoka, Kiichi Ishiwata, Hinako Toyama, Kenichirou Ono, Michio Senda,” Application of PET-MRI registration techniques to cat brain imaging “,Journal of Neuroscience Methods, Volume 101, Issue 1, 15, Pages 1-7 ,August 2000.


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