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Standardized Micro-Scale Mixing Evaluation

Standardized Micro-Scale Mixing Evaluation. Guillermo González-Fernández Benjamin Yang. Outline. Introduction and Motivation Project Goal and Success Measure Raw Images Image enhancement Edge detection Evaluation Results Custom area mixing evaluation. Introduction and Motivation.

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Standardized Micro-Scale Mixing Evaluation

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  1. Standardized Micro-Scale Mixing Evaluation Guillermo González-Fernández Benjamin Yang

  2. Outline • Introduction and Motivation • Project Goal and Success Measure • Raw Images • Image enhancement • Edge detection • Evaluation • Results • Custom area mixing evaluation

  3. Introduction and Motivation • Micro scale mixing crucial to success of many fields • No standardized comparison • Characteristics in microfluidics • Low Reynolds number • Laminar Flow • Diffusion Limited Image: A.D. Strook et al., “Chaotic Mixer for Microchannels,” Science, vol 295, pp. 647-651, 2002

  4. Project Goal and Success Measure • Evaluate the extent of mixing in standardized experimental images • Success will be based on correlation to existing simulation results Standard Y-channel Square-wave channel Compartment channel

  5. Raw Images A B E F G C D

  6. Image enhancement (I) • Why image enhancement? Better edge detection • Study the images: histogram, 2D-DFT… • Apply Matlab filters • Design new filters: • Gaussian • Sobel • Prewitt • Laplacian • Log • Unsharp • Min, med, max • Contraharmonic • New gaussian filter (gaussian iterations)

  7. Image enhancement (II) Min Max Med Contraharmonic Gaussian iterations

  8. Image enhancement (III) • Cascade different combinations of the filters • Final image obtained by averaging best results : • Average-gaussian-average • Average-gaussian-gaussian • Average-gaussian iterations-gaussian • Average-gaussian iterations-average

  9. Edge detection • We don’t know the real edge!! • Matlab edge detection strategies • New strategies • Y channel: Perfect boundary • Square channel: General method

  10. Edge detection • We don’t know the real edge!! • Matlab edge detection strategies • New strategies • Y channel: Perfect boundary • Square channel: General method

  11. Evaluation • Extract part of the image we will evaluate • Estimate mixing percentage

  12. Results

  13. Custom area mixing evaluation • User defines the image to evaluate • User defines area (rectangle) to evaluate • Obtain mixing efficiency

  14. References • [1] Bertsch, S. Heimgartner, P. Cousseau, and P. Renoud. “Static micromixers based on large-scale industrial mixer geometry”, Lab on a Chip, Vol 1, pp 56-60, 2001. • [2] A.D. Strook et al., “Chaotic Mixer for Microchannels,” Science, vol 295, pp. 647-651, 2002 • [3] R. H. Liu, M. A. Stremler, K. V. Sharp, M. G. Olsen, J. G. Santiago, R. J. Adrian, H Aref, and D. J. Beebe. "Passive mixing in a three-dimensional serpentine microchannel", J. of MEMS, Vol 9, No. 2, pp 190-196, 2000. • [4] V. Mengeaud, J. Josserand, and H. H. Girault. “Mixing processes in a zigzag microchannel: finite element simulations and optical study”, Analytical Chemistry, Vol 74, pp 4279-4286, 2002. • [5] R. Gonzalez, R. Woods. Digital Image Processing, 2nd Edition. Prentice Hall, Upper Saddle River, N.J, 2002. • [6] Kai Kang, R. Chevray. “Visualization of fluid mixing in microchannels”, IEEE Computer Graphics and Applications, Vol 25, Issue 6, pp 16-20, 2005. • [7] Leming Shi, Weida Tong, Zhenqiang Su, et al. “Microarry scanner calibration curves: characteristics and implications”, BMC Bioinformatics, Vol 6, pp. 1-14, 2005 • [8] Peter A. C. ‘t Hoen, Rolf Turk, Judith M. Boer, et al. “Intensity-based analysis of two color microarrays”, Nucleic Acids Research, Vol 32, No 4, pp. e41-e47, 2004

  15. Questions?

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