[1] [1] ANGULO J, FLANDRIN G. Microscopic image analysis using mathematical morphology: Application to haematological cytology［J］. Science, Technology and Education of Microscopy: An Overview, 2003,21(3): 304-312.

[2] [2] DORINI L B, MINETTO R, LEITE N J. White blood cell segmentation using morphological operators and scale-space analysis［J］. Brazilian Symposium on Computer Graphics and Image Processing , 2007, 20(6): 294-304.

[3] [3] ELDAHSHAN K, YOUSSEF M, MASAMEER E, et al. Comparison of segmentation framework on digital microscope images for acute lymphoblastic leukemia diagnosis using RGB and HSV color spaces［J］. Biotechnology & Bioengineering, 2015, 2(2): 27-34.

[4] [4] PIURI V, SCOTTI F. Morphological classification of blood leucocytes by microscope images［C］// IEEE International Conference on Computational Intelligence for Measurement Systems and Applications. New York, USA: IEEE,2004: 103-108.

[5] [5] JOSHI M D, KARODE A H, SURALKAR S R. White blood cells segmentation and classification to detect acute leukemia［J］. International Journal of Emerging Trends and Technology in Computer Science,2013, 2(3): 147-151.

[6] [6] RAZZAK M I, NAZ S. Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning［C］//IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, USA: IEEE Computer Society,2017: 801-807.

[7] [7] SAJJAD M, KHAN S, SHOAIB M, et al. Computer aided system for leukocytes classification and segmentation in blood smear images［C］// International Conference on Frontiers of Information Technology.New York,USA: IEEE,2017: 99-104.

[8] [8] CAI J,BAO X D,WU L,et al.Automated segmentation of colored leukocyte images based on the active contour model［J］.Journal of Biomedical Engineering Research,2005, 24(4): 218-222 (in Chinese).

[9] [9] YANG J L,ZHENG W L,MA W L,et al.Segmentation for image of white blood cells based on improved BP neural network［J］.Microcomputer Information,2008,24(33): 294-295(in Chinese).

[10] [10] YIN Y,WANG Y,LIU D P.Improved MEANSHIFT method for blood cell image segmentation[J]. Computer Engineering and Applications, 2010, 46(6): 178-180(in Chinese).

[11] [11] WANG J,CAO Y P,CHENG X Sh.Leukocyte image segmentation algorithm based on hue component histogram transformation［J］.Optics & Optoelectronic Technology, 2013,11(2): 000074(in Chinese).

[12] [12] HOU H,SHI Y X.Application of the improved watershed algorithm based on distance transform in white blood cell segmentation［J］.Computing Technology and Automation, 2016,35(3): 81-84(in Chinese).

[13] [13] LI Y, ZHU R, MI L, et al. Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method［J］. Computational and Mathematical Methods in Medicine, 2016, 10(1): 1-12.

[14] [14] HALIM N H A, MASHOR M Y, NASIR A S A, et al. Colour image segmentation using unsupervised clustering technique for acute leukemia images［C］// American Institute of Physics Conference Series. Melville, New York,USA: AIP Publishing LLC, 2015: 243-246.

[15] [15] LI Y.Research on the techniques of dual-threshold based peripheral blood leukocyte microscope image segmentation［D］.Xi’an: The University of Chinese Academy of Sciences(Xi’an Institute of Optics & Precision Mechanics,Chinese Academy of Sciences), 2016: 1-76(in Chinese).

[16] [16] GU G H,CUI D.Automatic segmentation algorithm for leukocyte images［J］.Chinese Journal of Scientific Instrument, 2009, 30(9): 1874-1879(in Chinese).

[17] [17] DOUGHERTY E R. Electronic imaging technology［M］. Miami, USA: SPIE Optical Engineering Press, 1999: 564-605.

[18] [18] HUANG Zh,ZHAO J W,CHU J J,et al.Efficient segmentation algorithm for white blood cell[J]. Computer Engineering and Applications, 2016, 52(21): 188-194(in Chinese).

[19] [19] TOH L B, MASHOR M Y, EHKAN P, et al. Image segmentation for acute leukemia cells using color thresholding and median filter［J］. Journal of Telecommunication, Electronic and Computer Engineering, 2018,10(5): 69-74.

[20] [20] LIU J, LEONG T Y, CHEE K B, et al. Set-based cascading approaches for magnetic resonance (MR) image segmentation (SCAMIS)［J］. AMIA Annual Symposium Proceedings, 2006, 49(9): 504-508.