DocumentCode :
1588985
Title :
White Blood Cell Image Segmentation Using On-line Trained Neural Network
Author :
Yi, Fang ; Chongxun, Zheng ; Chen, Pan ; Li, Liu
Author_Institution :
Key Lab. of Biomed. Inf. Eng. of Educ. Minist., Xi´´an Jiaotong Univ.
fYear :
2006
Firstpage :
6476
Lastpage :
6479
Abstract :
This paper addresses a fast white blood cell (WBC) image segmentation scheme implemented by on-line trained neural network. A pre-selecting technique, based on mean shift algorithm and uniform sampling, is utilized as an initialization tool to largely reduce the training set while preserving the most valuable distribution information. Furthermore, particle swarm optimization (PSO) is adopted to train the network for a faster convergence and escaping from a local optimum. Experiment results show that under the compatible image segmentation accuracy, the training set and running time can be reduced significantly, compared with traditional training methods
Keywords :
blood; cellular biophysics; image segmentation; medical image processing; neural nets; optimisation; mean shift algorithm; on-line trained neural network; particle swarm optimization; preselecting technique; uniform sampling; white blood cell image segmentation; Acceleration; Bandwidth; Biomedical engineering; Engineering in medicine and biology; Equations; Image segmentation; Kernel; Laboratories; Neural networks; White blood cells;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
Type :
conf
DOI :
10.1109/IEMBS.2005.1615982
Filename :
1615982
Link To Document :
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