All Title Author
Keywords Abstract

-  2018 

基于 mean-shift 聚类的高鲁棒性白细胞五分类识别算法
A robust classification method for five types of leukocytes in peripheral blood based on mean-shift clustering

DOI: 10.7507/1001-5515.201609067

Keywords: 白细胞纹理,白细胞分类,mean-shift,高鲁棒性
leukocyte texture
,leukocyte classification,mean-shift,high robustness

Full-Text   Cite this paper   Add to My Lib


本文提出了一种新型的基于 mean-shift 聚类算法的人体外周血中白细胞五分类算法,其核心思想是用一种近似人眼的可视化模式对白细胞纹理进行提取。首先利用 mean-shift 聚类算法从白细胞灰度图像中提取一些模式点,然后用其作为区域生长算法的种子点进行区域生长,得到一系列能够在某种程度上可视化地反映纹理的区域块。最后从这些区域块中提取一组参数向量作为白细胞的纹理特征。综合该向量和白细胞形态学特征,用人工神经网络(ANN)成功地完成了对白细胞的五分类识别。用了 1 310 个白细胞图像进行测试,得到中性粒细胞、嗜酸性粒细胞、嗜碱性粒细胞、淋巴细胞、单核细胞的正确识别率分别为 95.4%、93.8%、100%、93.1%、92.4%,证明了该算法的可行性和鲁棒性。
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.


[1]  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]  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]  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]  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]  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]  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]  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]  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]  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]  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]  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]  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]  13. Habibzadeh M, Krzy?ak 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]  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]  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]  16. Haralick R M. Statistical and structural approaches to texture. Proceedings of the IEEE, 1979, 67(5): 786-804.
[17]  17. Ojala T, Pietik?inen M, M?enp?? 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]  18. Habibzadeh M, Krzy?ak 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]  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]  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]  21. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern, 1979, 9(1): 62-66.


comments powered by Disqus

Contact Us


微信:OALib Journal