Cell microscopic segmentation with spiking neuron networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6352 LNCS, Issue PART 1, 2010, Pages 117-126

  Meftah, Boudjelal ^a,b   Lezoray, Olivier ^b   Lecluse, Michel ^c   Benyettou, Abdelkader ^d  

^a UNIVERSITÉ DE MASCARA   (Algeria)

^b UNIVERSITÉ DE CAEN   (France)

^c Centre Hospitalier Public du Cotentin   (France)

^d UNIVERSITÉ DES SCIENCES ET DE LA TECHNOLOGIE D ORAN MOHAMED BOUDIAF   (Algeria)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR IMAGES; COMPUTATIONAL POWER; MICROSCOPIC SEGMENTATION; NEW DIMENSIONS; PROCESSING ABILITY; QUANTITATIVE MEASURES; SPIKING NEURON NETWORKS;

IMAGE SEGMENTATION;

NEURAL NETWORKS;

EID: 78049393605     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15819-3_16     Document Type: Conference Paper

References (29)

1. Knesek Jr., E. A.: Roche Image Analysis System. Acta Cytologica 40(1), 60-66 (1996)
2. Patten Jr., S. F., Lee, S. S. J., Nelson, A. C.: Neopath Autopap 300 Automatic pap Screener System. Acta Cytologica 40(1), 45-52 (1996)
3. Di Rubeto, C., Dempster, A., Khan, S., Jarra, B.: Segmentation of Blood Image using Morphological Operators. In: Proc. of the 15th Int. Conference on Pattern Recognition, Barcelona, Spain, September 3-8, vol. 3, pp. 397-400 (2000)
4. Anoraganingrum, D.: Cell Segmentation with Median Filter and Mathematical Morphology Operation. In: Proc. of the International Conference on Image Analysis and Processing, Venice, Italy, September 27-29, pp. 1043-1046 (1999)
5. Lezoray, O., Cardot, H.: Cooperation of Pixel Classification Schemes and Color Watershed: a Study for Microscopical Images. IEEE Transactions on Images Processing 11(7), 738-789 (2002)
6. Lin, G., Adiga, U., Olson, K., Guzowski, J. F., Barnes, C. A., Roysam, B.: A hybrid 3D Watershed Algorithm Incorporating Gradient Cues and Object Models for Automatic Segmentation of Nuclei in Confocal Image Stacks. Cytometry A 56(1), 23-36 (2003)
7. Adiga, U. P. S., Chaudhuri, B. B.: An Efficient Method based on Watershed and Rulebased Merging for Segmentation of 3-D Histopathological Images. Pattern Recognition 34(7), 1449-1458 (2001)
8. Mouroutis, T., Roberts, S. J., Bharath, A. A.: Robust Cell Nuclei Segmentation Using Statistical Modeling. BioImaging 6, 79-91 (1998)
9. Wu, H. S., Barba, J., Gil, J.: Iterative Thresholding for Segmentation of Cells from Noisy Images. J. Microsc. 197, 296-304 (2000)
10. Karlosson, A., Strahlen, K., Heyden, A.: Segmentation of Histological Section using Snakes. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 595-602. Springer, Heidelberg (2003)
11. Murashov, D.: Two-Level Method for Segmentation of Cytological Images using Active Contour Model. In: Proc. of the 7th Int. Conference on Pattern Recognition and Image Analysis, PRIA-7, St. Petersburg, Russian Federation, October 18-23, vol. III, pp. 814-817 (2004)
12. Papanicolaou, G. N.: A new procedure for staining vaginal smears. Science 95, 432 (1942)
13. Borsotti, M., Campadelli, P., Schettini, R.: Quantitative Evaluation of Color Image Segmentation Results. Pattern Recognition Letters 19, 741-747 (1998)
14. Glory, E., Meas-Yedid, V., Pinset, C., Olivo-Marin, J. C., Stamon, G.: A Quantitative Criterion to Evaluate Color Segmentation Application to Cytological Imges. In: Blanc-Talon, J., Philips, W., Popescu, D. C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 227-234. Springer, Heidelberg (2005)
15. Maass, W.: Networks of spiking neurons: The third generation of neural network models. Neural Networks 10(9), 1659-1671 (1997)
16. Paugam-Moisy, H., Bohte, S. M.: Computing with Spiking Neuron Networks. In: Kok, J., Heskes, T. (eds.) Handbook of Natural Computing, 40p. Springer, Heidelberg (2009) (to appear)
17. Thorpe, S. J., Delorme, A., VanRullen, R.: Spike-based strategies for rapid processing. Neural Networks 14 (6-7), 715-726 (2001)
18. Gupta, A., Long, L. N.: Hebbian learning with winner take all for spiking neural networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), Atlanta, Gerogia, pp. 1189-1195 (2009)
19. Wu, B. J.: Introduction to neural dynamics and signal transmission delay. Walther de Gruyter, Berlin (2001)
20. Maass, W.: On the relevance neural networks. MIT-Press, London (2001)
21. Gerstner, W., Kistler, W. M.: Spiking neuron models, 1st edn. Cambridge University Press, Cambridge (2002)
22. Maass, W.: Fast sigmoidal networks via spiking neurons. Neural Computation 9, 279-304 (1997)
23. Gerstner, W.: Time structure of the activity in neural network models. Phys. Rev. E 51, 738-758 (1995)
24. NatschlNager, T., Ruf, B.: Spatial and Temporal Pattern Analysis via Spiking Neurons Network. Comp. Neural Systems 9(3), 319-332 (1998)
25. Bohte, S. M., La Poutre, H., Kok, J. N.: Unsupervised clustering with spiking neurons by sparse temporal coding and Multi-Layer RBF Networks. IEEE Transactions on Neural Networks 13(2), 426-435 (2002)
26. Ludemir, B. T. B., De Carvalho, C. P.: Artificial neural networks - theory and applications, 1st edn. LTC Editora, Rio de Janeiro (2000)
27. Oster, M., Liu, S. C.: A winner-take-all spiking network with spiking inputs. In: Proceedings of the 11th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2004), vol. 11, pp. 203-206 (2004)
28. De Berredo, R. C.: A review of spiking neuron models and applications. M. Sc. Dissertation, University of Minas Gerais (2005)
29. Meurie, C., Lezoray, O., Carrier, C., Elmoataz, A.: Combination of Multiple Pixel Classifiers for Microscopic Image Segmentation. International Jornal of Robotic and Automation, Special Issue on Color Image and Analysis for Machine Vision 20(2), 63-69 (2005)