White blood cell image segmentation using on-line trained neural network

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 7 VOLS, Issue , 2005, Pages 6476-6479

  Fang, Yi ^a,b   Zheng, Chongxun ^a,b   Pan, Chen ^b   Liu, Li ^b  

^a IEEE   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MEAN SHIFT ALGORITHM; PARTICLE SWARM OPTIMIZATION; WHITE BLOOD CELL;

ALGORITHMS; CELLS; INFORMATION ANALYSIS; MEDICAL IMAGING; NEURAL NETWORKS; OPTIMIZATION; PERSONNEL TRAINING;

IMAGE SEGMENTATION;

EID: 33846907226     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. H.D. Cheng, X.H. Jiang, Y. Sun, IL. Wang, "Color Image Segmentation: Advances and Prospects," Pattern Recognition, 34(12), pp. 2259-2281, Dec. 2001
2. G.M. Foody, A. Mathur, "Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification," Remote Sensing of Environment, Vol. 93, No. 1-2, pp. 107-117 October 30, 2004
3. F. Kurugollu, B. Sankur. A.B. Harmanci, "Color image segmentation using histogram multithresholding and fusion," Image and vision computing 19, pp. 915-928, 2001
4. Robert, P.W. Duin, "Classifiers in Almost Empty Spaces," Proceedings of ICPR'00 Conference, Spain, p. 2001, 2000
5. Ying Wu, Thomas S. Huang, "Nonstationary Color Tracking for Vision-Based Human-Computer Interaction," IEEE Irans. On neural networks, vol. 13(4), July 2002
6. F. Morchen, "Analysis of Speedup as Function of Block Size and Cluster Size for Parallel Feed-Forward Neural Networks on a Beowulf Cluster Neural Networks," IEEE Transactions on, Vol. 15, Issue: 2, pp. 515-527, March 2004
7. V. Nedeljkovic, "A novel multilayer neural networks training algorithm that minimizes the probability of classification error," Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings, 11th IAPR International Conference on, 30 Aug.-3 Sept. 1992 pp. 13-16
8. M.C.M. Teixcira, D. Lamas, "Neural network training using the constructivism paradigms," Circuits and Systems, 1995, Proceedings. Proceedings of the 38th Midwest Symposium on Volume 1, 13-16 pp. 546-549 vol.1, Aug. 1995
9. Tae-Hoon Kim, Jiang Li, M.T. Manry, "Evaluation and improvement of two training algorithms," Signals, Systems and Computers, 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Volume 2, 3-6, pp. 1019-1023 vol.2, Nov. 2002
10. B. Irie, S. Miyake, "Capability of three-layered perceptions," Proceedings of IEEE International Conference on Neural Networks, San Diego, USA, pp. 641-648, July 1988
11. W.Z. Lu, H.Y. Fan, S.M. Lo, "Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong," Neurocomputing 51, pp. 387-400, 2003
12. J. Kennedy, R.C. Eberhart, "Particle swarm optimization," Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, December 1995, pp. 1942-1948
13. K. Fukunaga, L.D. Hostetler, "The estimation of the gradient of a density function, with applications in pattern recognition," IEEE Trans. Information Theory, vol. 21, pp. 32-40, 1975
14. D. Comaniciu, P. Meer. "Mean shift: A Robust Approach towards Feature Space Analysis," IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(5), pp. 603-619, 2002