Segmentation Method of Colour White Blood Cell Image Based on HSI Modified Space Information Fusion_Journal of Biomedical Engineering

Authors：

MIAOHuisi ¹ , LIANGGuangming ² ,  LIURenren ¹ , DINGJianwen ³

1. College of Information Engineering, Xiangtan University, Xiangtan 411105, China;
2. School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410075, China;
3. R & D Center, AVE Technology Corp. Ltd, Changsha 410013, China;

Corresponding author：

LIURenren, Email: lrr863@163.com

Keywords：

white blood cell; image segmentation; HSI space; information fusion; region growing

DOI：

10.7507/1001-5515.20160051

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Abstract Full text Figures/Tables Video References Cited by

This paper presents a kind of automatic segmentation method for white blood cell based on HSI corrected space information fusion. Firstly, the original cell image is transformed to HSI colour space conversion. Because the transformation formulas of H component piecewise function was discontinuous, the uniformity of uniform visual cytoplasm area in the original image was lead to become lower in this channel. We then modified formulas, and then fetched information of nucleus, cytoplasm, red blood cells and background region according to distribution characteristics of the H, S and I-channel, using the theory and method of information fusion to build fusion imageⅠand fusion imageⅡ, which only contained cytoplasm and a small amount of interference, and fetched nucleus and cytoplasm respectively. Finally, we marked the nucleus and cytoplasm region and obtained the final result of segmentation. The simulation results showed that the new algorithm of image segmentation for white blood cell had high accuracy, robustness and universality.

Citation： MIAOHuisi, LIANGGuangming, LIURenren, DINGJianwen. Segmentation Method of Colour White Blood Cell Image Based on HSI Modified Space Information Fusion. Journal of Biomedical Engineering, 2016, 33(2): 303-307. doi: 10.7507/1001-5515.20160051 Copy

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1. PARK C, HUANG J Z, JI J X, et al. Segmentation, inference, and classification of partially overlapping nanoparticles[J]. IEEE Trans Pattern Anal Mach Intell, 2013, 35(3):669-681.
2. QI Xin, XING Fuyong, FORAN D J, et al. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set[J]. IEEE Trans Biomed Eng, 2012, 59(3):754-765.
3. GUAN P P, YAN Hong. Blood cell image segmentation based on the Hough transform and fuzzy curve tracing[C]//2011 International Conference on Machine Learning and Cybernetics. Guilin:2011, 4:1696-1701.
4. PAN Kangyu, KOKARAM A, GILMORE K, et al. Cell snake:A new active contour technique for cell/fibre segmentation[C]//201118th IEEE International Conference on Image Processing. Brussels:2011:2153-2156.
5. LI Shu, WU Lei, SUN Yang. Design and implementation of an improved overlapped cell image segmentation method[C]//2011 International Conference on Robot, Vision and Signal Processing. Kaohsiung:2011, 1:204-207.
6. SHARIF J M, MISWAN M F, NGADI M A, et al. Red blood cell segmentation using masking and watershed algorithm:A preliminary study[C]//2012 International Conference on Biomedical Engineering. Penang:2012, 1:258-262.
7. 顾广华, 崔冬.白细胞图像的自动分割算法[J].仪器仪表学报, 2009, 30(9):1874-1879.
8. 王康, 李晓春.基于多空间图像融合的白细胞自动分割[J].计算机仿真, 2015, 32(3):258-262.
9. MOUELHI A, SAYADI M, FNAIECH F. Automatic segmentation of clustered breast cancer cells using watershed and concave vertex graph[C]//2011 International Conference on Communications, Computing and Control Applications (CCCA). Hammamet:2011:1-6.
10. KO B C, GIM J W, NAM J Y. Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake[J]. Micron, 2011, 42(7):695-705.

Journal of Biomedical Engineering

Segmentation Method of Colour White Blood Cell Image Based on HSI Modified Space Information Fusion

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