Preprocessing of Separating Leukocytes Based on Setting Parameters of Lightness Transformation

Jianyong Cai, Lili Luo, Rongtai Cai, Lijin Lin, Juan Cai

.

DOI: 10.4236/jsip.2013.44051                

Abstract

This paper proposed a new algorithm to separate leukocytes from cytological image by setting parameters of lightness transformation based on the RGB color space, which can make the targets’ color in different areas. In our procedure, an operator is employed in using color features. According to their histogram distribution of hue component in HSL color space after enhancing the contrast of image in RGB color space, the threshold of segmentation between leukocyte and erythrocyte could be achieved well. Especially, this algorithm is more efficient than monochrome for leukocyte segmentation, and the results of experiments show that it provides a good tool for cytological image, which can increase accuracy of segmentation of leukocyte.

Keywords

Parameters; Lightness Transformation; Color Features; HSL; Threshold; Leukocyte Segmentation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	B. C. Ko, J.-W. Gim and J.-Y. Nam, “Automatic White Blood Cell Segmentation Using Stepwise Merging Rules and Gradient Vector Flow Snake,” Micron, Vol. 42, No. 7, 2011, pp. 695-705. http://dx.doi.org/10.1016/j.micron.2011.03.009
[2]	D.-C. Huang and K.-D. Hung, “A Computer Assisted Method for Leukocyte Nucleus Segmentation and Recognition in Blood Smear Images,” Journal of Systems and Software, Vol. 85, No. 9, 2012, pp. 2104-2118. http://dx.doi.org/10.1016/j.jss.2012.04.012
[3]	C. Reta, et al., “Leukocytes Segmentation Using Markov Random Fields,” Software Tools and Algorithms for Biological Systems, Springer, New York, 2011, pp. 345-353.
[4]	M. Kass, A. Witkin and D. Terzopoulos, “Snakes: Active Contour Models,” International Journal of Computer Vision, Vol. 1, No. 4, 1988, pp. 321-331. http://dx.doi.org/10.1007/BF00133570
[5]	N. Malpica, Ortiz de Solorzano, et al., “Applying Watershed Algorithms to the Segmentation of Clustered Nuclei,” Cytometry, Vol. 28, No. 4, 1997, pp. 289-297.
[6]	P. Chen, et al., “Leukocyte Image Segmentation Using Simulated Visual Attention,” Expert Systems with Applications, Vol. 39, No. 8, 2012, pp. 7479-7494. http://dx.doi.org/10.1016/j.eswa.2012.01.114
[7]	N. Theera-Umpon, E. R. Dougherty and P. D. Gader, “Non-Homothetic Granulometric Mixing Theory with Application to Blood Cell Counting,” Pattern Recognition, Vol. 34, No. 12, 2001, pp. 2547-2560. http://dx.doi.org/10.1016/S0031-3203(00)00156-4
[8]	D. M. U. Sabino, L. F. Costa, E. G. Rizzatti and M. A. Zago, “Toward Leukocyte Recognition Using Morphometry, Texture and Color,” IEEE International Symposium on Biomedical Imaging: Nano to Macro, Arlington, 15-18 April 2004, pp. 121-124.
[9]	D. M. U. Sabino, et al., “A Texture Approach to Leukocyte Recognition,” Real-Time Imaging, Vol. 10, No. 4, 2004, pp. 205-216. http://dx.doi.org/10.1016/j.rti.2004.02.007
[10]	L. Olivier, et al., “Segmentation of Cytological Images Using Color and Mathematical Morphology,” Acta Stereologica, Vol. 18, 1999, pp. 1-14.
[11]	H. Ramoser, et al., “Leukocyte Segmentation and Classification in Blood-Smear Images,” 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS, Shanghai, 17-18 January 2006, pp. 3371-3374.
[12]	G. U. Guanghua and C. U. I. Dong, “Automatic Segmentation Algorithm for Leukocyte Images,” Chinese Journal of Science Instrument, Vol. 9, 2009, pp. 1874-1879.
[13]	T. Bergen, et al., “Segmentation of Leukocytes and Erythrocytes in Blood Smear Images,” 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, 20-25 August 2008, pp. 3075-3078.
[14]	G. Ercan and P. Whyte, “Digital Image Processing,” U.S. Patent No. 6240217, 2001.
[15]	M. Saraswat, K. V. Arya and H. Sharma, “Leukocyte Segmentation in Tissue Images Using Differential Evolution Algorithm,” Swarm and Evolutionary Computation, Vol. 11, 2013, pp. 46-54.