Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection

Author(s): Xiaogen Zhou, Zuoyong Li, Huosheng Xie*, Ting Feng, Yan Lu, Chuansheng Wang, Rongyan Chen

Journal Name: Current Bioinformatics

Volume 15 , Issue 3 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Aims: The proposed method falls into the category of medical image processing.

Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis.

Objective: This paper aims to present a leukocyte segmentation method, which improves segmentation accuracy under rapid and standard staining conditions.

Methods: The proposed method first localizes leukocytes by color component combination and Adaptive Histogram Thresholding (AHT), and crops sub-image corresponding to each leukocyte. Then, the proposed method employs AHT to extract the nucleus of leukocyte and utilizes image color features to remove image backgrounds such as red blood cells and dyeing impurities. Finally, Canny edge detection is performed to extract the entire leukocyte. Accordingly, cytoplasm is obtained by subtracting nucleus with leukocyte.

Results: Experimental results on two datasets containing 160 leukocyte images show that the proposed method obtains more accurate segmentation results than their counterparts.

Conclusion: The proposed method obtains more accurate segmentation results than their counterparts under rapid and standard staining conditions.

Keywords: Leukocyte segmentation, leukocyte localization, color component combination, adaptive histogram thresholding, edge detection, morphological operation.

Zheng X, Wang Y, Wang G, Chen Z. A novel algorithm based on visual saliency attention for localization and segmentation in rapidly-stained leukocyte images. Micron 2014; 56: 17-28.
[http://dx.doi.org/10.1016/j.micron.2013.09.006] [PMID: 24148877]
Duan J, Yu L. A WBC segmentation method based on HSI color space. Proceedings of the 4th IEEE International Conference on Broadband Network and Multimedia Technology. 2011 Oct 28-30; Shenzhen, China: IEEE 2012.
Sahoo P, Soltani S, Wong A. A survey of thresholding techniques. Comput Vis Graph Image Process 1988; 41(2): 233-60.
Huang D, Hung K, Chan Y. A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. J Syst Software 2012; 85(9): 2104-18.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979; 9(1): 62-6.
Lim HN, Mashor MY, Hassan R. White blood cell segmentation for acute leukemia bone marrow images. Proceedings of the 2012 IEEE International Conference on Biomedical Engineering (ICoBE). 2012 Feb 27-28; Penang, Malaysia.
Jiang K, Liao QM, Xiong Y. A novel white blood cell segmentation scheme based on feature space clustering. Soft Comput 2006; 10(1): 12-9.
Arslan S, Ozyurek E, Gunduz-Demir C. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytometry A 2014; 85(6): 480-90.
[http://dx.doi.org/10.1002/cyto.a.22457] [PMID: 24623453]
Zheng X, Wang Y, Wang G, Liu J. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 2018; 107: 55-71.
[http://dx.doi.org/10.1016/j.micron.2018.01.010] [PMID: 29425969]
Zheng X, Wang Y, Wang G. White blood cell segmentation using expectation-maximization and automatic support vector machine learning. J Data Acquisition Proc 2013; 28(5): 217-31.
Wei L, Hu J, Li F, Song J, Su R, Zou Q. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Brief Bioinform 2018; 1-14.
[http://dx.doi.org/10.1093/bib/bby107] [PMID: 30383239]
Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics 2018; 34(23): 4007-16.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID: 29868903]
Pan C, Fang Y, Yan X, Zheng C. Robust segmentation for low quality cell images from blood and bone marrow. Int J Control Autom Syst 2006; 4(5): 637-44.
Ruberto C, Loddo A, Putzu L. A leukocytes count system from blood smear images: segmentation and counting of white blood cells based on learning by sampling. Mach Vis Appl 2016; 27(8): 1151-60.
Theera-Umpon N. White blood cell segmentation and classification in microscopic bone marrow images. Proceedings of the second International Conference on Fuzzy Systems and Knowledge Discovery. August 27-29; Changsha, China: Springer-Verlag 2005.
Zhang C, Xiao X, Li X, et al. White blood cell segmentation by color-space-based k-means clustering. Sensors (Basel) 2014; 14(9): 16128-47.
[http://dx.doi.org/10.3390/s140916128] [PMID: 25256107]
Ko BC, Gim JW, Nam JY. Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 2011; 42(7): 695-705.
[http://dx.doi.org/10.1016/j.micron.2011.03.009] [PMID: 21530280]
Sadeghian F, Seman Z, Ramli AR, Abdul Kahar BH, Saripan MI. A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 2009; 11(1): 196-206.
[http://dx.doi.org/10.1007/s12575-009-9011-2] [PMID: 19517206]
Hamghalam M, Motameni M, Kelishomi A. Leukocyte segmentation in giemsa-stained image of peripheral blood smears based on active contour. Proceedings of the International Conference on Signal Processing Systems. May 15-17; Singapore, Singapore: IEEE 2009.
Zamani F, Safabakhsh R. An unsupervised GVF snake approach for white blood cell segmentation based on nucleus. Proceedings of the 8th International Conference on Signal Processing. 2006 Nov 16-20; Beijing, China: IEEE 2007.
Osowski S, Siroic R, Markiewicz T, Siwek K. Application of support vector machine and genetic algorithm for improved blood cell recognition. IEEE Trans Instrum Meas 2009; 58(7): 2159-68.
Liu GH, Yang JY. Exploiting color volume and color difference for salient region detection. IEEE Trans Image Process 2019; 28(1): 6-16.
[http://dx.doi.org/10.1109/TIP.2018.2847422] [PMID: 29994257]
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8(6): 679-98.
[http://dx.doi.org/10.1109/TPAMI.1986.4767851] [PMID: 21869365]
Gu G, Cui D. Flexible combination segmentation algorithm for leukocyte images. Yiqi Yibiao Xuebao 2008; 29(9): 1977-81.
Yasnoff WA, Mui JK, Bacus JW. Error measures for scene segmentation. Pattern Recognit 1977; 9(4): 217-23.
Fawcelt T. An introduction to ROC analysis. Pattern Recognit Lett 2006; 27(8): 861-74.
Fleiss JL, Cohen J, Everitt BS. Large sample standard errors of kappa and weighted kappa. Psychol Bull 1969; 72(5): 323-7.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [187 - 195]
Pages: 9
DOI: 10.2174/1574893614666190723115832
Price: $65

Article Metrics

PDF: 17
PRC: 1