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The Scientific World Journal
Volume 2014, Article ID 796371, 9 pages
Research Article

A Neural-Network-Based Approach to White Blood Cell Classification

1Department of Computer Science & Information Engineering, National Central University, Jhongli 32001, Taiwan
2General Hospital, Taipei 10656, Taiwan

Received 7 August 2013; Accepted 20 October 2013; Published 30 January 2014

Academic Editors: C.-C. Liu and C. H. Yeang

Copyright © 2014 Mu-Chun Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.