GCLAHE-Based Movement Leukocyte Recognition Research

Shu Jun Liu; Li Min Mao; Ben Lian Xu

doi:10.4028/www.scientific.net/AMM.427-429.1532

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429GCLAHE-Based Movement Leukocyte Recognition...

GCLAHE-Based Movement Leukocyte Recognition Research

Abstract:

In the analysis of the characteristics about the microscopic leukocyte image sequence, a kind of grading contrast limited adaptive histogram equalization (GCLAHE) algorithm was presented in the preprocessing. This method can effectively enhance the cell contrast with the surrounding background, and can play a better filtering to eliminate noise effects, at the same time keep better cell boundary information, and then improve the cell tracking rate. 100 frames of continuous images sequence were processed in Matlab 7, the average false detection rate is 0.48%, and the average miss detection rate is 1.20%. It is shown that the algorithm can reduce cell miss detection rate and the false detection rate; improve cell detection and tracking precision.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

1532-1536

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.1532

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Online since:

September 2013

Authors:

Shu Jun Liu, Li Min Mao, Ben Lian Xu

Keywords:

Eliminating Noise, Enhance Leukocyte Boundary, Movement Leukocyte Image, Multicellular Detection, Multicellular Tracking

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References

[1] B.S. Denney R.J. Figueiredo 2000. Optimal point target detection using adaptive auto regressive background. SPIE, 4048：46-57.

DOI: 10.1117/12.392005

Google Scholar

[2] Brox, T., Weickert, J 2006. Level set segmentation with multiple-regions. IEEETrans. Image Process. 15(10), 3213–3218.

DOI: 10.1109/tip.2006.877481

Google Scholar

[3] Cremers, D., Rousson, M 2007. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. 72 (2), 195-215.

DOI: 10.1007/s11263-006-8711-1

Google Scholar

[4] Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on System Man and Cybernetics, 9(1): 62-69.

DOI: 10.1109/tsmc.1979.4310076

Google Scholar

[5] Meyer, Fernand 1994. Topographic distance and watershed lines. Signal Processing , 38, 113-125.

DOI: 10.1016/0165-1684(94)90060-4

Google Scholar

[6] Eric Dahai Cheng, Subhash Challa. 2009. Microscopic Cell Segmentation and Dead Cell Detection Based on CFSE and PI Images by Using Distance and Watershed Transforms. accepted by the Digital Image Computing: Techniques and Applications, Australia. (DICTA 2009) Session 3 Keynote 32-39.

DOI: 10.1109/dicta.2009.16

Google Scholar