CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Determination of Gray Matter (GM) and White Matter (WM) Volume in Brain Magnetic Resonance Images (MRI)

by E. A. Zanaty
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 3
Year of Publication: 2012
Authors: E. A. Zanaty
10.5120/6759-9021

E. A. Zanaty . Determination of Gray Matter (GM) and White Matter (WM) Volume in Brain Magnetic Resonance Images (MRI). International Journal of Computer Applications. 45, 3 ( May 2012), 16-22. DOI=10.5120/6759-9021

@article{ 10.5120/6759-9021,
author = { E. A. Zanaty },
title = { Determination of Gray Matter (GM) and White Matter (WM) Volume in Brain Magnetic Resonance Images (MRI) },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 3 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number3/6759-9021/ },
doi = { 10.5120/6759-9021 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:39.060070+05:30
%A E. A. Zanaty
%T Determination of Gray Matter (GM) and White Matter (WM) Volume in Brain Magnetic Resonance Images (MRI)
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 3
%P 16-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present a hybrid approach based on combining fuzzy clustering, seed region growing, and Jaccard similarity coefficient algorithms to measure gray (GM) and white matter tissue (WM) volumes from magnetic resonance images (MRIs). The proposed algorithm incorporates intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different regions using fuzzy clustering. These regions are fed to seed region growing (SRG) method to isolate the suitable closed region. The seeds of SRG are selected as the output centers of the fuzzy clustering method. To compare the performance of various outputs of seed region technique Jaccard similarity coefficient is used to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results.

References
  1. Peterson, J. W. , Bo, L. , Mork, S. , et al. , " Transected neuritis, apoptotic neurons, and Reduced inflammation in cortical multiple sclerosis lesions", Ann. Neurol. 50, 389–400, 2001.
  2. Kutzelnigg, A. , Lassmann, H. ," Cortical lesions and brain atrophy in MS. J. Neurol", Sci. 233, 55–59. , 2005.
  3. Bakshi, R. , Ariyaratana, S. , Benedict, R. H. B. , Jacobs, L. ,"Fluid-attenuated inversion Recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions", Arch. Neurol. 58, 742–748, 2001.
  4. Geurts, J. J. , Pouwels, P. J. , Uitdehaag, B. M. , Polman, C. H. , Barkhof, F. , Castelijns, J. A. ," Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion recovery MR imaging", Radiology 236 (1), 254–260, 2005.
  5. Bagnato, F. , Butman, J. A. , Gupta, S. , Calabrese, M. , Pezawas, L. , Ohayon, J. M. , Tovar Moll, F. , Riva, M. , Cao, M. M. , Talagala, S. L. , McFarland, H. F. ,"In vivo detection of Cortical plaques by MR imaging in patients with multiple sclerosis", Am. J. Neuroradiol. 27, 2161 2167, 2006.
  6. Nelson, F. , Poonawalla, A. H. , Hou, P. , Huan, F. , Wolinsky, J. S. , Narayana, P. A. , "Improved identification of intracortical lesions in multiple sclerosis with phase sensitive Inversion recovery in combination with fast double inversion recovery MRI", Am. J. Neuroradiol. 28 (9), 1645–1649,2007.
  7. Pirko, I. , Lucchinetti, C. F. , Sriram, S. , Bakshi, R. ," Gray matter involvement in multiple Sclerosis", Neurology 68 (9), 634–642,2007.
  8. Chard, D. T. , Griffin, C. M. , Rashid, W. , Davies, G. R. , Altmann, D. R. , Kapoor, R. , Barker, G. J. ,Thompson, A. J. , Miller, D. H. ," Progressive grey matter atrophy in clinically early relapsing-remitting multiple sclerosis", Mult. Scler. 10 (4), 387–391, 2004.
  9. Ge, Y. , Grossman, R. I. , Udupa, J. K. , Babb, J. S. , Nyul, L. G. , Kolson, D. L. ," Brain Atrophyin relapsing-remitting multiple sclerosis: fractional volumetric analysis of gray matter and white matter", Radiology 220 (3), 606–610, 2001.
  10. Sastre-Garriga, J. , Ingle, G. T. , Chard, D. T. , Ramio-Torrenta, L. , Miller, D. H. , Thompson, A. J. ,"Grey and white matter atrophy in early clinical stages of primary progressive multiple sclerosis", NeuroImage 22 (1), 353–359, 2004.
  11. Tiberio, M. , Chard, D. T. , Altmann, D. R. , Davies, G. , Griffin, C. M. , Rashid,W. , SastreGarriga,J. , Thompson, A. J. , Miller, D. H. ," Gray and white matter volume changes inEarly RRMS: a 2-year longitudinal study", Neurology 64 (6), 1001–1007, 2005.
  12. Chen, J. T. , Narayanan, S. , Collins, D. L. , Smith, S. M. , Matthews, P. M. , Arnold, D. L. ,"Relating neocortical pathology to disability progression in multiple sclerosis usingMRI",NeuroImage 23 (3), 1168–1175, 2004.
  13. De Stefano, N. , Matthews, P. M. , Filippi, M. , Agosta, F. , De Luca, M. , Bartolozzi, M. L. , Guidi,L. , Ghezzi, A. , Montanari, E. , Cifelli, A. , Federico, A. , Smith, S. M. ," Evidence of early cortical atrophy in MS: relevance to white matter changes and disability", Neurology 60 (7), 1157–1162, 2003.
  14. Schnack, H. G. , Hulshoff Pol, H. E. , Baaré, W. F. C. , Staal, W. G. , Viergever, M. A. , Kahn, R. S. ," Automated separation of gray and white matter from MR images of the human Brain", NeuroImage 13, 230–237, 2001.
  15. Chalana, V. , Ng, L. , Rystrom, L. R. , Gee, J. C. , Haynor, D. R. ," Validation of brain segmentation and tissue classification algorithm for T1-weighted MR images",. Med. Imag. 2001: Image Process. 4322, 1873–1882, 2001.
  16. Van Leemput, K. , Maes, F. , Vandermeulen, D. , Suetens, P. ," Automated model-base Tissue classification of MR images of the brain", IEEE Trans. Med. Imag. 18 (10), 897 908, 1999.
  17. Davatzikos, C. A. , Prince, J. L. ," An active contour model for mapping the cortex", IEEE Trans. Med. Imag. 14 (1), 65–80, 1995.
  18. Xu, C. , Pham, D. L. , Rettmann, M. E. , Yu, D. N. , Prince, J. L. ," Reconstruction of the Human cerebral cortex from magnetic resonance images", IEEE Trans. Med. Imag. 18(6), 467 480,1999.
  19. Zeng, X. , Staib, L. H. , Schultz, R. T. , Duncan, J. S. , "Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation", IEEE Trans. Med. Imag. 18 (10), 927–937, 1999.
  20. Ashburner, J. , Friston, K. J. . ,"Unified segmentation", NeuroImage 26 (3), 839–851, 2005.
  21. Andersen, A. H. , Zhang, Z. , Avison, M. J. , Gash, D. M. , "Automated segmentation of multispectral brain MR images", J. Neurosci. Methods 122, 13–23, 2002.
  22. Marroquin, J. L. , Vemuri, B. C. , Botello, S. , Calderon, F. , Fernandez-Bouzas, A. , "An accurate and efficient bayesian method for automatic segmentation of brain MRI", IEEE Trans. Med. Imag. 21 (8), 934–945, 2002.
  23. Zhang, Y. , Brady, M. , Smith, S. ,"Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm", IEEE Trans. Med. Imag. 20 (1), 45–57, 2001.
  24. Amato, U. , Larobina, M. , Antoniadis, A. , Alfano, B. ,"Segmentation of magnetic Resonance brain images through discriminant analysis", J. Neurosci. Methods 131,65–74, 2003.
  25. Mohamed, F. B. , Vinitski, S. , Faro, S. H. , Gonzalez, C. F. , Mack, J. , Iwanaga, T. , "Optimization of tissue segmentation of brain MR images based on multispectral 3D Feature maps", Magn. Reson. Imaging 17, 403–409, 1999.
  26. Pham, D. L. , Prince, J. L. , "Adaptive fuzzy segmentation of magnetic resonance images",. IEEE Trans. Med. Imag. 18 (9), 737–752, 1999.
  27. Suckling, J. , Sigmundsson, T. , Greenwood, K. , Bullmore, E. T. , "A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images", Magn. Reson. Imaging 17, 1065–1076, 1999.
  28. Ahmed, M. N. , Yamany, S. M. , Mohamed, N. , Farag, A. A. , Moriarty, T. , "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data", IEEE Trans. Med. Imag. 21 (3), 193–199, 2002.
  29. Zhu, C. , Jiang, T. ,"Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images", NeuroImage 18 (3), 685–696, 2003.
  30. Zhou, Y. , Bai, J. , "Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI", IEEE Trans. Biomed. Eng. 54 (1), 122 129, 2007.
  31. Sajja, B. R. , Datta, S. , He, R. , Mehta, M. , Gupta, R. K. , Wolinsky, J. S. , Narayana, P. A. ,"Unified approach for multiple sclerosis lesion segmentation on brain MRI", Ann. Biomed. Eng. 34 (1), 142–151, 2006.
  32. Smith, S. M. , Jenkinson, M. , Woolrich, M. W. , Beckmann, C. F. , Behrens, T. E. J. , Johansen Berg, H. , Bannister, P. R. , De Luca, M. , Drobnjak, I. , Flitney, D. E. , Niazy, R. K. , Saunders, J. , Vickers, J. , Zhang, Y. , De Stefano, N. , Brady, J. M. , Matthews, P. M. , "Advances in functional and structural MR image analysis and implementation as FSL", Neuro-Image 23, S208–S219, 2004.
  33. JaccardP. , "The distribution of the flora in the alpine zone". New phytol. 11 (2),37 50,1912.
  34. ChunD. N. , H. S. Yang, "Robust image segmentation using genetic algorithm with Fuzzy measure", Pattern Recognition, 29, 7, 1195–1211, 1996.
  35. HsiehF-Y, C-C, Han, N-S. Wu, T. C. Chuangc, K-C,Fana, "A novel approach to the detection of small objects with low contrast", Signal Processing, 86, 71–83, 2006.
  36. ChakeresD. W. , SchmalbrockP. , "Fundamentals of magnetic resonance imaging", Williams and Wilkins, Baltimore, 1992.
  37. Buxton R. B. "Introduction to functional magnetic resonance imaging-principles and techniques", Cambridge University Press, 2002.
  38. LiewA. W. C. , Yan H. , "Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images", Medical Imaging Reviews, 291-103, 2006.
  39. Yan H. , J. C. Gore, "An efficient algorithm for MR image reconstruction without low spatial frequencies", IEEE Trans. Med. Imag. , TMI-9,. 179-184, 1990.
  40. Angela G. E. M. de Boer, T. T. Taskila, A. O. , Frank,Jos H. A. , " Cancer survivors and Unemployment". The Journal of the American medical association JAMA, . 30, 7,2009.
  41. BezdekJ. C. ,"Pattern recognition with fuzzy objective function algorithms", PlenumPress,NewYork,1981.
  42. Liew A. Yan H. , "An adaptive spatial fuzzy clustering algorithm for MR imagesegmentation", IEEE Trans. Med. Image. ,. 22,1063-1075, 2003.
  43. Su M. C. and Chou C. H. ,"A modified version of the k-means algorithm with a distance Basedon cluster symmetry," IEEE Transactions Pattern Analysis and Machine Intelligence,. 2. 6,680, 2001.
  44. Fan, J. , Zeng, G. , Body, M. ,Hacid, M. , "Seeded region growing: an extensive and comparative Study". Pattern Recognition Letters. 26, 1139-1156, 2005.
  45. Dice,L. "Measures of the amount of ecologic association between species", Ecology. . 26,:297-302,1945.
  46. Gardner MJ. andAltman DG. , "Calculating confidence intervals for proportions and theirdifferences". BMJ Publishing Group, 28-33,1989.
  47. ZijdenbosA. P. , MRI segmentation and the quantification of white matter lesionsPhD thesis, Vanderbilt University, Electrical Engineering Department, Nashville, Tennessee; December 1994
  48. Brain Web, "Simulated Brain Database", McConnell Brain Imaging Centre, MontrealNeurological Institute, McGill.
  49. E. A. Zanaty, S. Aljahdali "Fuzzy algorithms for automatic magnetic resonance image segmentation", International Arab Journal of Information Technology (IAJIT), 7,. 3, 271-279, 2009.
  50. ZanatyE. A. , Aljahdali,S. ,Debnath N. , "Improving fuzzy algorithms for automatic magnetic resonance image segmentation", Proceedings of seventeenth International Conference of Software Engineering and Data Engineering, pp. 60-66, Los Angeles, California, USA, June 2008.
  51. Zanaty E. A. , Aljahdali,S. , Debnath N. , "A kernelized fuzzy c-means algorithm for Automatic magnetic resonance image segmentation", Journal of Computational Methods in Science and engineering (JCMSE), . 123-136, 2009.
  52. Del-Fresno M. ,Vénere M. , and A. Clausse, A combined region growing and deformablemodel method for extraction of closed surfaces in 3D CT and MRI scans, ComputerizMedical Imaging and Graphics, . 33, . 369–376, 2009
  53. Yu Z. Q. ,. Zhu Y,Yang J. , Y. M. Zhu, A hybrid region-boundary model for cerebralcortical segmentation in MRI, Computerized Medical Imaging and Graphics, . 30,197208, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Clustering Seed Region Growing Performance Measure Mri Brain Database

Powered by PhDFocusTM