Brain Tumor Segmentation in MRI Images Based on Image Registration and Improved Fuzzy C-Means (IFCM) Method

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Registration and Segmentation are important aspects of medical image processing. This paper proposes an efficient Image registration and Improved Fuzzy c-means (IFCM) segmentation technique, to segment the tumor in an MRI medical image. The Affine transform and correlation method have been applied for the image registration, leading to sub entire pixel accuracy for the entire data set. Then, four clustering set models are generated for each registered image in the IFCM based segmentation process. One of the clusters set model is applied to a morphological process, to get the eroded image for extracting the tumor part accurately. The performance of the proposed method is validated both quantitatively and qualitatively, using performance metrics such as standard deviation and entropy.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Affine Transformation; Correlation Features; Clustering Methods; Image Registration; Image Segmentation

Full Text:



H. Gonçalves, J. A. Gonçalves, and L. Corte-Real, “HAIRIS: A Method for automatic image registration through histogram- based image segmentation”, IEEE Trans. Image Process. vol.20, no. page no. 776–789, 2011.

H. Gonçalves, Jose A. Gonçalves and L. Corte-Real, “Automatic Image Registration Based on Correlation and Hough Transform”, IEEE Trans. Image Process. vol. 20, page no 611- 621, 2012.

Noppodal Chumchob, Ke.Chen,”A robust affine image Registration method”, International journal of numerical analysis and Modeling. vol. 6,Page no.311-334,2000.

Colin Studholme, John A.Little, Graeme P.Pency, Derek L.G.Hill, David J.Hawkes,”Automated Multimodality Registration Using the Full Affine Transformation: Application to MR & CT Guided Skull Base Surgery,” Deviation of Radiological Surgery, page no 1571-1582, 2007.

Zijdenbos, A., Forghani, R. and Evans, A., “Automatic Pipeline analysis of 3D MRI data for clinical trials: Application to multiple sclerosis. “IEEE Trans.Med.Imaging, page no 1280–1291, 2002.

Barbara Zitova, Jan Flusser,”Image registration methods: a Survey. of the academic research”, Image and vision computing page no 977-1000,2003.

Hui Lin, Peigun Du, Weichang Zhao, Lianpeng Zhang, Huasheng Sun,” Image registration based on corner detection and affine transformation”, 3rd International congress on Image and Signal Processing, page no 152-160,2010.

Weaver, J. B., Yansun, X., Healy, D. and Cromwell. D.Magn Reson.”Filtering noise from images with wavelet transforms”, Journal of Medical Analysis,vol 21,page no, 288–295, 1991.

Jude hemanth.D, D.Selvathi and Juanita, “Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation”, International/Advance Computing Conference (IACC), page no.609-614, 2009.

Steven Eschrich, Jingwei Ke, Lawrence O. Hall and Dmitry,”Accurate Fuzzy Clustering through Data Reduction”, IEEE Conferences page no 1-18, 2002.

B. Caldairou, N. Passat, P. A. Habas, C. Studholme, and F.Rousseau, “A non-local fuzzy segmentation method: application to brain MRI,” journal of Pattern Recognition,vol. 44, page no.1916–1927, 2011.

L. Cheng, J. Gong, X. Yang, C. Fan, and P. Han, “Robust affine invariant feature extraction for image matching”, IEEE Geosci. Remote Sens. Lett., vol. 5, page no. 246–250, 2008.

E.D. Castro, C. Morandi,” Registration of translated and rotated images using finite Fourier transform”, IEEE Transactions on pattern Analysis and Machine Intelligence, page no. 700- 703, 1987.

J. B. Antoine Mainz_ and Max A. Viergever, “A Survey of Medical Image Registration”, A survey of medical image Registration, Image Sciences Institute, Utrecht University and Hospital, Utrecht, the Netherlands.

P. Zhilkin, and M.E. Alexander, “Affine registration: a comparison of several programs”, J. Magn. Reson. Imaging, Vol.22, page no .55-66, 2004.

A. Collignon, F. Maes, D. Delaere, D. andermeulen, P. Sue tens, and G. Marchal”, Automated multimodality image registration using information theory”, IEEE Transaction on Pattern analysis and machine intelligence, page no1421- 1431,2009.

Vatsa M, Singh, R. and Noore,” A Denoising and Segmentation process of 3D brain images”, Proceeding of Int. Conf. on Image processing, computer vision and pattern: (IPCV),page no.561–567,2009.

H. Chang and J.M. Fitzpatrick”, A technique for accurate MR Imaging in the presence of field In homogeneities”, IEEE Trans. Med. Imaging, page no.319– 329, 1992.

G. Stockman, S. Kopstein, and S. Benett, “Matching images to models for registration and object detection via Clustering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol 43, page no. 229–241, 1982.

E. Spedicato, and M.T. Vespucci, “Numerical experiments with variations of the Gauss-Newton algorithm for nonlinear least Squares”, J. Opt. Theory Appln, vol 57 , page no.323-339 ,1988.

Li, Q., Gray-level image threshold segmentation based on double set FCM and improved intra-class minimum, (2012) International Review on Computers and Software (IRECOS), 7 (4), pp. 1819-1824.

Sumathi, S., Sanavullah, M.Y., Recognition and classification system of cardiac arrhythmia using ANFIS, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2260-2265.

Rafeal C.Goncalez, Richard E.Woods (2nd Edition), Text Book of Digital Image Processing.

Sergious Theodoridis, Konstantinos Koutroumbas (2nd Edition), Text Book of Pattern Recognition.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize