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

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.