A robust classification method for five types of leukocytes in peripheral blood based on mean-shift clustering_Journal of Biomedical Engineering

Authors：

LI Xiaoshun ,  CAO Yiping , WANG Yapin

Department of Optical Electronics, Sichuan University, Chengdu 610064, P.R.China;

Corresponding author：

CAO Yiping, Email: ypcao@scu.edu.cn

Keywords：

leukocyte texture; leukocyte classification; mean-shift; high robustness

DOI：

10.7507/1001-5515.201609067

Video：

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Abstract Full text Figures/Tables Video References Cited by

A new leukocyte classification method for recognition of five types of human peripheral blood smear based on mean-shift clustering is proposed. The key idea of the proposed method is to extract the texture features of leukocytes in a visual manner which can benefit from human eyes. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift. Secondly, these feature points are used as seeds of the region growing to expand feature regions which can express texture in visual mode to a certain extent. Finally, a parameter vector of these regions is extracted as the texture feature. Combing the vector with the geometric features of the leukocyte, the five typical classes of leukocytes can be recognized successfully using artificial neural network (ANN). A total number of 1 310 leukocyte images have been tested and the accurate rate of recognition for neutrophil, eosinophil, basophil, lymphocyte and monocyte are 95.4%, 93.8%, 100%, 93.1% and 92.4%, respectively, which shows the feasibility and high robustness of the proposed method.

Citation： LI Xiaoshun, CAO Yiping, WANG Yapin. A robust classification method for five types of leukocytes in peripheral blood based on mean-shift clustering. Journal of Biomedical Engineering, 2018, 35(5): 761-766. doi: 10.7507/1001-5515.201609067 Copy

1.	Sabino D M U, Costa L D F, Rizzatti E G, et al. A texture approach to leukocyte recognition. Real-Time Imaging, 2004, 10(4): 205-216.
2.	Neugebauer U, Clement J H, Bocklitz T, et al. Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging. J Biophotonics, 2010, 3(8/9): 579-587.
3.	张时民. 五分类法血细胞分析仪测定原理和散点图特征. 中国医疗器械信息, 2008, 14(12): 1-9, 44.
4.	Scotti F. Robust segmentation and measurements techniques of white cells in blood microscope images// 2006 IEEE Instrumentation and Measurement Technology Conference (IMTC). Sorrento, Italy: IEEE, 2006: 43-48.
5.	Mohammed E A, Mohamed M M A, Far B H, et al. Peripheral blood smear image analysis: A comprehensive review. J Pathol Inform, 2014, 5(1): 9.
6.	Pavlova P E, Cyrrilov K P, Moumdjiev I N. Application of HSV colour system in identification by colour of biological objects on the basis of microscopic images. Comput Med Imaging Graph, 1997, 20(5): 357-364.
7.	Pan Chen, Park D S, Yoon S, et al. Leukocyte image segmentation using simulated visual attention. Expert Syst Appl, 2012, 39(8): 7479-7494.
8.	Ko B C, Gim J W, Nam J Y. Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron, 2011, 42(7): 695-705.
9.	Mohammed E A, Far B H, Naugler C, et al. Application of support vector machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation// 2013 IEEE International Conference on E-Health Networking, Applications and Services. Lisbon, Portugal: IEEE, 2013: 622-626.
10.	Piuri V, Scotti F. Morphological classification of blood leucocytes by microscope images// 2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). Boston, USA: IEEE, 2004: 103-108.
11.	Huang D C, Hung K D, Chan Y K. A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. J Syst Software, 2012, 85(9): 2104-2118.
12.	Hiremath P S, Bannigidad P, Geeta S. Automated identification and classification of white blood cells (leukocytes) in digital microscopic images. Int J Comput Appl, 2010, 37(2): 59-63.
13.	Habibzadeh M, Krzyżak A, Fevens T. White blood cell differential counts using convolutional neural networks for low resolution images// 2013 International Conference on Artificial Intelligence and Soft Computing (ICAISC). Zakopane, Poland: Springer Berlin Heidelberg, 2013: 263-274.
14.	Lina, Chris A, Mulyawan B. Focused color intersection for leukocyte detection and recognition system. International Journal of Information and Electronics Engineering, 2013, 3(5): 498-501.
15.	Fatichah C, Tangel M L, Widyanto M R, et al. Parameter optimization of local fuzzy patterns based on fuzzy contrast measure for white blood cell texture feature extraction. Journal of Advanced Computational Intelligence & Intelligent Informatics, 2012, 16(3): 412-419.
16.	Haralick R M. Statistical and structural approaches to texture. Proceedings of the IEEE, 1979, 67(5): 786-804.
17.	Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell, 2002, 24(7): 971-987.
18.	Habibzadeh M, Krzyżak A, Fevens T. Analysis of white blood cell differential counts using dual-tree complex wavelet transform and support vector machine classifier// 2012 International Conference on Computer Vision and Graphics (ICCVG). Warsaw, Poland: Springer Berlin Heidelberg, 2012: 414-422.
19.	Rezatofighi S H, Khaksari K, Soltanian-Zadeh H. Automatic recognition of five types of white blood cells in peripheral blood// 2010 International Conference on Image Analysis and Recognition (ICIAR). Póvoa de Varzim, Portugal: Springer Berlin Heidelberg, 2010: 161-172.
20.	Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell, 2002, 24(5): 603-619.
21.	Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern, 1979, 9(1): 62-66.

1. Sabino D M U, Costa L D F, Rizzatti E G, et al. A texture approach to leukocyte recognition. Real-Time Imaging, 2004, 10(4): 205-216.
2. Neugebauer U, Clement J H, Bocklitz T, et al. Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging. J Biophotonics, 2010, 3(8/9): 579-587.
3. 张时民. 五分类法血细胞分析仪测定原理和散点图特征. 中国医疗器械信息, 2008, 14(12): 1-9, 44.
4. Scotti F. Robust segmentation and measurements techniques of white cells in blood microscope images// 2006 IEEE Instrumentation and Measurement Technology Conference (IMTC). Sorrento, Italy: IEEE, 2006: 43-48.
5. Mohammed E A, Mohamed M M A, Far B H, et al. Peripheral blood smear image analysis: A comprehensive review. J Pathol Inform, 2014, 5(1): 9.
6. Pavlova P E, Cyrrilov K P, Moumdjiev I N. Application of HSV colour system in identification by colour of biological objects on the basis of microscopic images. Comput Med Imaging Graph, 1997, 20(5): 357-364.
7. Pan Chen, Park D S, Yoon S, et al. Leukocyte image segmentation using simulated visual attention. Expert Syst Appl, 2012, 39(8): 7479-7494.
8. Ko B C, Gim J W, Nam J Y. Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron, 2011, 42(7): 695-705.
9. Mohammed E A, Far B H, Naugler C, et al. Application of support vector machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation// 2013 IEEE International Conference on E-Health Networking, Applications and Services. Lisbon, Portugal: IEEE, 2013: 622-626.
10. Piuri V, Scotti F. Morphological classification of blood leucocytes by microscope images// 2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). Boston, USA: IEEE, 2004: 103-108.
11. Huang D C, Hung K D, Chan Y K. A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. J Syst Software, 2012, 85(9): 2104-2118.
12. Hiremath P S, Bannigidad P, Geeta S. Automated identification and classification of white blood cells (leukocytes) in digital microscopic images. Int J Comput Appl, 2010, 37(2): 59-63.
13. Habibzadeh M, Krzyżak A, Fevens T. White blood cell differential counts using convolutional neural networks for low resolution images// 2013 International Conference on Artificial Intelligence and Soft Computing (ICAISC). Zakopane, Poland: Springer Berlin Heidelberg, 2013: 263-274.
14. Lina, Chris A, Mulyawan B. Focused color intersection for leukocyte detection and recognition system. International Journal of Information and Electronics Engineering, 2013, 3(5): 498-501.
15. Fatichah C, Tangel M L, Widyanto M R, et al. Parameter optimization of local fuzzy patterns based on fuzzy contrast measure for white blood cell texture feature extraction. Journal of Advanced Computational Intelligence & Intelligent Informatics, 2012, 16(3): 412-419.
16. Haralick R M. Statistical and structural approaches to texture. Proceedings of the IEEE, 1979, 67(5): 786-804.
17. Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell, 2002, 24(7): 971-987.
18. Habibzadeh M, Krzyżak A, Fevens T. Analysis of white blood cell differential counts using dual-tree complex wavelet transform and support vector machine classifier// 2012 International Conference on Computer Vision and Graphics (ICCVG). Warsaw, Poland: Springer Berlin Heidelberg, 2012: 414-422.
19. Rezatofighi S H, Khaksari K, Soltanian-Zadeh H. Automatic recognition of five types of white blood cells in peripheral blood// 2010 International Conference on Image Analysis and Recognition (ICIAR). Póvoa de Varzim, Portugal: Springer Berlin Heidelberg, 2010: 161-172.
20. Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell, 2002, 24(5): 603-619.
21. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern, 1979, 9(1): 62-66.

Journal of Biomedical Engineering

A robust classification method for five types of leukocytes in peripheral blood based on mean-shift clustering

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