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.
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.
