Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(485.23KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Knowledge
Hassan Tavakkoli, Ali Sadeqi
Pages - 1 - 10     |    Revised - 15-01-2012     |    Published - 21-02-2012
Volume - 6   Issue - 1    |    Publication Date - February 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Electrodes, Electroencephalography (EEG), Neuro Image Coding, IRIS Patterns & Brain Waves , Signal Processing
ABSTRACT
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
CITED BY (1)  
1 Rajan, R. E., & Prasadh, K. (2012). Feature Selection on Segmented Image using Automatic Subjective Optimality Model. International Journal of Computer Applications, 56(1).
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 Bielefeld Academic Search Engine (BASE) 
6 Scribd 
7 SlideShare 
8 PdfSR 
9 PDFCAST 
1 R.W. Brown, M.R. Thompson, E.M. Haacke, R Venkatesan. Magnetic Resonance Imaging: Physical Principles and equence Design. New York, NY: John Wiley & Sons Inc, 1999.
2 E. Berry. A Practical Approach to Medical Image Processing. Leeds, UK: Elizabeth Berry Ltd, 2007.
3 D. L. Pham, C. Y. Xu, J. L. Prince. "Current methods in medical image segmentation". Annu. Rev. Biomed. Eng., vol. 2, pp. 315 - 337, 2000.
4 T. M. Peters, D. L. Collins, P. Neelin, A. C. Evans. “Automatic 3D intersubject registration from volumetric data in standardized talairach space”. Journal of Computer Assisted Tomography, vol. 18, pp. 192-205, 1994.
5 A. Evans, P. Fox, J. Mazziotta, A. Toga, J. Lancaster. “A proabilistic atlas of the human brain: Theory and rational for its development”. NeuroImaging, vol. 2, pp. 89-101, 1995.
6 A. W. Toga. Brain Warping. Burlington, MA: Academic Press, 1998.
7 A. W. Toga. Brain Warping. Burlington, MA: Academic Press, 1998.
8 S.G. Sclan, S. Kanowski. “Alzheimer’s disease: Stage-related interventions”. Lippincotts Case Manag, vol. 6(2), pp. 61-63 , 2001.
9 B. L. Roth. “Neuronal Signal Transduction Pathways: Wasteland or the Promised Land?”. Sci. STKE vol. 45, pp. pe1, 2000.
10 H. Anisman, Z. Merali, S. Hayley. “Neurotransmitter, peptide and cytokine processes in relation to depressive disorder: Comorbidity between depression and neurodegenerative disorders”. Prog Neurobiol, vol. 85, pp. 1-74, 2008.
11 C. Brayne, H. Brodaty, L. Fratiglioni, M. Ganguli, k. Hall, K. Hasegawa, H. Hendrie, Y. Huang, C. ferri, M. Prince. “Global prevalence of dementia: a delphi consensus study”. The Lancet, vol. 366, pp. 2112-2117, 2005.
12 C. R. Jack, M. Slomkowski, S. Gracon, T. M. Hoover, J. P. Felmlee, K. Stewart, Y. Xu, M. Shiung, P. C. O'Brien, R. Cha, D. Knopman, R. C. Petersen. “MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD”. Neurology, vol. 60(2), pp. 253-260, 2003.
13 T. Song, E. Angelini, B. Mensh, A. Laine. " Comparison study of clinical 3D MRI brain segmentation evaluation". In Proc. IEEE EMBS '04, 2004, vol. 3, pp. 1671-4.
14 J. Talairach, P. Tournoux. Co-planar stereotactic atlas of the human brain: 3-dimensional proposal system: an approach to cerebral imaging. Stuttgart, UK: Thieme, 1988.
15 A. Toga, P. Thompson. “The role of image registration in brain mapping”. Image and Vision Computing, vol. 19(1-2), pp. 3-24, 2001.
16 Passalis, et al." Evaluation of face recognition in presence of facial expressions: an annoted deformable model approach, in: IEEE Workshop on FRGC Expermints, June,2005.
17 D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, A. C. Evans. “Design and construction of a realistic digital brain phantom”. IEEE Trans. on Med. Img., vol. 13(3), pp. 463-468, 1998.
18 J. V. Hajnal, D. L. G. Hill, D. J. Hawkes. Medical image registration, Boca Raton, FL: CRC Press, 2001.
19 D. Ruckert, L. I. Sonoda, C. Hayes, et al. “Nonrigid registration using free-form deformations: Application to breast MR images”. IEEE Trans. Med. Imaging, vol. 18(8), pp. 712-721, 1999.
20 J. Kybic, M. Unser. “Fast parametric elastic image registration”. IEEE Trans. Med. Imaging, vol. 12(11), pp. 1427-1441, 2003.
21 T. Rohlfing, C. R. Maurer, W. G. O'Dell, et al. “Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images”. Med. Phys., vol. 31(3), pp.427-432, 2004.
22 J. R. McClelland, A. G. Chandler, J. M. Blackall, et al. “4D motion models over the respiratory cycle for use in lung cancer radiotherapy planning”, In Proc. SPIE 5744, 2005, pp. 173–183.
23 J. R. McClelland, J. M. Blackall, S. Tarte. “A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy”. Med. Phys., vol. 33(9), pp. 3348-58, 2006.
24 S. Lee, G. Wolberg, S. Y. Shin. “Scattered data interpolation with multilevel B-splines”. IEEE Trans. Visualization Comput. Graph., vol. 3, pp. 228–244, 1997.
25 K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens. “Automated model-based bias field correction of MR images of the brain”. IEEE Trans. Med. Img., vol. 18(10), pp. 885- 896, 1999.
26 K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens. "Automated model-based tissue classification of MR images of the brain". IEEE Trans. Med. Imag., vol. 18(10), pp. 897- 908 , 1999.
27 B. Flury. A first course in multivariate statistics. New York, NY: Springer- Verlag, 1997.
Dr. Hassan Tavakkoli
Applied Neuroscience Research Center, Baqiyatallah University of Medical Sciences - Iran
tavakoli@ibb.ut.ac.ir
Mr. Ali Sadeqi
Applied Neuroscience Research Center - Iran