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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 9514707, 12 pages
Research Article

Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method

Yan Li,1,2 Rui Zhu,1 Lei Mi,1 Yihui Cao,1,2 and Di Yao3

1State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi’an 710119, China
2University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China
3Shenzhen Vivolight Medical Device and Technology Co., Ltd., Shenzhen 518000, China

Received 30 December 2015; Revised 7 April 2016; Accepted 21 April 2016

Academic Editor: Jayaram K. Udupa

Copyright © 2016 Yan Li 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.


We propose a dual-threshold method based on a strategic combination of RGB and HSV color space for white blood cell (WBC) segmentation. The proposed method consists of three main parts: preprocessing, threshold segmentation, and postprocessing. In the preprocessing part, we get two images for further processing: one contrast-stretched gray image and one H component image from transformed HSV color space. In the threshold segmentation part, a dual-threshold method is proposed for improving the conventional single-threshold approaches and a golden section search method is used for determining the optimal thresholds. For the postprocessing part, mathematical morphology and median filtering are utilized to denoise and remove incomplete WBCs. The proposed method was tested in segmenting the lymphoblasts on a public Acute Lymphoblastic Leukemia (ALL) image dataset. The results show that the performance of the proposed method is better than single-threshold approach independently performed in RGB and HSV color space and the overall single WBC segmentation accuracy reaches 97.85%, showing a good prospect in subsequent lymphoblast classification and ALL diagnosis.