A modified probabilistic neural network for partial volume segmentation in brain MR image

IEEE Transactions on Neural Networks

Volumn 18, Issue 5, 2007, Pages 1424-1432

  Song, Tao ^a   Jamshidi, Mo M ^b   Lee, Roland R ^a,c   Huang, Mingxiong ^a,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b The University of Texas at San Antonio   (United States)

^c VA SAN DIEGO HEALTHCARE SYSTEM   (United States)

Author keywords

Magnetic resonance (MR) image; Partial volume effect; Self organizing map (SOM) neural network; Tissue classification; Weighted probabilistic neural network (WPNN)

Indexed keywords

ALGORITHMS; BRAIN MODELS; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; PROBABILITY DENSITY FUNCTION; SELF ORGANIZING MAPS; STATISTICAL METHODS;

PARTIAL VOLUME EFFECT; TISSUE CLASSIFICATION; WEIGHTED PROBABILISTIC NEURAL NETWORK (WPNN);

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 34548620807     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.891635     Document Type: Article

References (33)

1. J. C. Bezdek, L. O. Hall, and L. P. Clark, "Review of MR image segmentation techniques using pattern recognition," Med. Phys., vol. 20, no. 4, pp. 1033-1048, 1993.
2. L. P. Clarke and L. O. Hall, "MRI segmentation: Methods and applications," Magn. Reson. Imag., vol. 13, no. 3, pp. 343-368, 1995.
3. D. L. Pham, C. Y. Xu, and J. L. Prince, "A survey of current methods in medical image segmentation, annual review of biomedical engineering," Annu. Rev., vol. 2, pp. 315-337, 2000.
4. P. K. Sahoo, S. Soltani, and A. K. C. Wong, "A survey of thresholding techniques," Comput. Vis. Graph. Image Process., vol. 41, pp. 233-260, 1988.
5. P. Gibbs, D. L. Buckley, S. J. Blackband, and A. Horsman, "Tumour volume detection from MR images by morphological segmentation," Phys. Med. Biol., vol. 41, pp. 2437-2446, 1996.
6. T. McInerney and D. Terzopoulos, "Deformable models in medical image analysis: A survey," Med. Image Anal., vol. 1, pp. 91-108, 1996.
7. L. O. Hall and J. C. Bezdek, "A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 672-681, Sep. 1992.
8. E. Gllenbe and Y. Feng, "Neural network methods for volumetric magnetic resonance imaging of the human brain," Proc. IEEE, vol. 84, no. 10, pp. 1488-1496, Oct. 1996.
9. V. A. Magnotta and N. C. Andreasen, "Measurement of brain structures with artificial neural networks: Two and three dimensional applications," Radiology, vol. 211, no. 3, pp. 781-790, 1999.
10. J. Alirezaie, M. E. Jernigan, and C. Nahmias, "Neural network based segmentation of magnetic resonance images of the brain," IEEE Trans. Nucl. Sci., vol. 44, no. 2, pp. 194-198, Apr. 1997.
11. S. Dickson, "Investigation of the use of neural networks for computerized medical image analysis," Ph.D. dissertation, Dept. Comput. Sci., Univ. Bristol, Bristol, U.K., 1998.
12. H. S. Choi, D. R. Haynor, and Y. M. Kim, "Partial volume tissue classification of multichannel magnetic resonance images - A mixel model," IEEE Trans. Med. Imag., vol. 10, no. 3, pp. 395-407, 1991.
13. D. H. Laidlaw, K. W. Fleischer, and A. H. Barr, "Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms," IEEE Trans Med. Imag., vol. 17, no. 1, pp. 74-86, Jan. 1998.
14. M. Morrison and Y. Attikiouzel, "A probabilistic neural network based image segmentation network for magnetic resonance images," in Proc. Int. Joint Conf. Neural Netw., 1992, vol. 3, pp. 60-65.
15. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
16. A. P. Zijdenobos and B. M. Dawant, "Brain segmentation and white matter lesion detection in MR images," Critical Rev. Biomed. Eng., vol. 22, no. 5-6, pp. 401-465, 1994.
17. "BrainWeb: Simulated brain database,"H[Online]. Available: http://www.bic.mni.mcgill.ca/brainweb/
18. "FMRIB software library," [Online]. Available: http://www.fmrib.ox.ac.uk/fsl/
19. S. M. Bhandarkar and P. Nammalwar, "Segmentation of multispectral MR images using a hierarchical self-organizing map," in Proc. 14th IEEE Symp. Comp.-Based Med. Syst. (CMBS), 2001, pp. 294-299.
20. T. Kohonen, Self-Organizing Maps, 3rd ed. New York: Springer-Verlag, 2000.
21. K. Mehrotra, C. K. Mohan, and S. Ranka, Element of Artificial Neural Networks. Cambridge, MA: MIT Press, 1996.
22. S. Harting and M. A. Viergever, "A multiscale approach to image segmentation using Kohonen networks," Utrecht Univ., Utrecht, The Netherlands, Tech. Rep. RUU-CS-93-06, 1993.
23. S. Theodoridis and K. Koutroumbas, Pattern Recognition. New York: Academic Press, 1999.
24. Y. Zhang and M. Brady, "Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm," IEEE Trans. Med. Imag., vol. 20, no. 1, pp. 45-57, Jan. 2001.
25. D. F. Specht, "Probabilistic neural networks," Neural Netw., vol. 3, pp. 109-118, 1990.
26. T. Hoya, "On the capability of accommodating new classes within probabilistic neural networks," IEEE Trans. Neural Netw., vol. 14, no. 2, pp. 450-453, Mar. 2003.
27. T. Masters, Advanced Algorithms for Neural Networks: A C++ Sourcebook New York: Wiley, 1995.
28. B. Dawant and S. Hartmann, "Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, methodology and validation on normal subjects," IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 909-916, Oct. 1999.
29. V. Magnotta and J. H. Bockholt, "Subcortical, cerebellar, and magnetic resonance based consistent brain image registration," Neuroimage, vol. 19, no. 2, pt. 1, pp. 233-245, 2003.
30. D. Shattuck and S. Sandor-Leahy, "Magnetic resonance image tissue classification using a partial volume model," Neuroimage, vol. 13, pp. 856-876, 2001.
31. K. Farrell, S. Kosonocky, and R. Mammone, "Neural tree network/vector quantization probability estimators for speaker recognation," in Proc. Workshop Neural Netw. Signal Process., 1994, pp. 279-288.
32. "Statistical parametric mapping," Univ. College London, London, U.K. [Online]. Available: http://www.fil.ion.ucl.ac.uk/spm/
33. K. V. Leemput, F. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based bias field correction of MR images of the brain," IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 885-896, Oct. 1999.