A comparison of hopfield neural network and boltzmann machine in segmenting mr images of the brain

IEEE Transactions on Nuclear Science

Volumn 43, Issue 6 PART 2, 1996, Pages 3361-3369

  Sammouda, Rachid ^a   Niki, Noboru ^a  

^a UNIVERSITY OF TOKUSHIMA   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; IMAGE SEGMENTATION; MEDICAL IMAGING; NEURAL NETWORKS;

BOLTZMANN MACHINE; BRAIN TUMORS; HOPFIELD NEURAL NETWORK;

MAGNETIC RESONANCE IMAGING;

EID: 0030374184     PISSN: 00189499     EISSN: None     Source Type: Journal    
DOI: 10.1109/23.552753     Document Type: Article

References (16)

1. M. W. Vannier, R. L. Butterfield, D. L Rickman, D. Z. M. Jordan, W. A. Murphy, R. G. Levit, and G. Mohktar, "Multispectral analysis of magnetic resonance images," Radiology, vol. 154, no. 1, pp. 221-224, 1985.
2. M. I. Kohn, N. K. Tanna, G. T. Herman et al, "Analysis brain and cerebrospinal fluid volumes with MR imaging: Part I-Methods, reliability, and validation," Radiology, vol. 178, pp. 115-122, 1991.
3. E. Herkovits, "A hybrid classifier for automated radiological diagnosis: Preliminary results and clinical applications," Computer Methods and Programs in Biomedicine, vol. 32, pp. 45-52, May 1990.
4. M. Ozkan, B. M. Dawant, and R. J. Macinas, "Neural network-based segmentation of multi-modal medical images: A comparative and prospective study," IEEE Trans. Med. Imag., vol. 12, pp. 534-544, Sept. 1993.
5. S. C. Armatur, D. Piraino, and Y. Takefuji, "Optimization neural networks for the segmentation of magnetic resonance images," IEEE Trans. Med. Imag., vol. 11, no. 2, pp. 215-220, 1992.
6. R. A. Jacobs, "Increased rates of convergence through learning rate adoption," Neural Networks, vol. 1, pp. 295-307, 1988.
7. Z. R. Novakovic, "Solving systems of nonlinear equations using the Lyapunor direct method," Computer Math. Applicat., vol. 20, no. 12, pp. 19-23, 1990.
8. S. Schaffler and H. Warsitz, "A trajectory-following method for unconstrained optimization," J. Opt. Theory Appl., vol. 67, pp. 133-138, Oct. 1990.
9. F. Aluffi-Pentini, V. Parsi, and F. Zirilli, "Global optimization and stochastic differential equations," J. Optim. Theory Appl., vol. 47, pp. 1-16, 1985.
10. S. Geman and C. R. Hwang, "Diffusions for global optimization" SIAM J. Contr. Optim., vol. 24, no. 5, pp. 1031-1043, 1986.
11. R. Sammouda, N. Niki, and H. Nishitani, "Multichannel segmentation of magnetic resonance cerebral images based on neural networks," in IEEE Int. Conf. Image Processing, Washington, D.C, Oct. 1995, pp. 484-487.
12. E. Aart and J. Korst, Simulated Annealing and Boltzmann Machines. New York: Wiley, 1989.
13. G. E. Hinton, R. J. Sejnowski, and D. H. Ackley, "Boltzmann machines: Contraints satisfaction networks that learn," Tech. Rep. CMU-CS-84-119, Carnegie-Mellon Univ., Dept. of Computer Science, 1984.
14. S. Kirkpatrik, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, pp. 671-680, 1983.
15. G. H. Ball and D. J. Hall, "A clustering technique for summarizing multivariate data," Behavioral Science, vol. 12, pp. 153-155, 1973.
16. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.