Algorithm for Drop Breakup Characterization
Algorithm for Drop Breakup Characterization
Output
Microfluidics is a research area that focuses on studying fluids in the length scales of 100 µm or less.
Recently, there has been interest in using drops as vessels to study chemical reactions and single cell
studies
. However, some channel designs could cause drops to break unintentionally.We would like to study the physical parameters that cause drops to breakup in a constriction. Our channel of interest, a tapered channel seems to promote squeezing between drops which consequentlycauses drops to break at the constriction. A single layer of monodisperse drops (drops that are the same volume) will be used to flow through the microfluidic channel. Furthermore, these monodisperse drops are packed together to produce a high volume fraction (high number of drops per volume). For the image processing algorithm, we want to characterize the drop pair (drop A and B) prior to flowing into the constriction (ie. Centroid of each drops, drop morphology, drop major and minor axisdrop orientation). We also want the algorithm to detect drops that break (break = 1 or no break =0). This
algorithm will enable us to find the physical parameters that are important for breakup.Image processing techniques learned from class will be utilized in this project. Furthermore, image processing techniques that are published in research literatures will be utilized as well. The biggest
challenge here is to be able to detect edge of the drops when it is flowing through the channel. Due to
the high volume fraction of the droplets together with how close the drops are to the channel wall. It ishard to detect the edges of some of the drops. It would be crucial for the algorithm to detect all the
drops as we want the physical parameters of all the drops that flow through the channel. Fortunately, apublished algorithm was found to be able to determine edges of clustered objects2,3 . Deblurring images will also be attempted for this project . Finally, some common imaging techniques to detect drops in amicrofluidic channel will be reviewed and possible implemented as well5.
Matlab will be used. Android devices are not required.
References
1. Teh, S.-Y., Lin, R., Hung, L.-H. & Lee, A. P. Droplet microfluidics. Lab Chip 8, 198–220 (2008).
2. Malpica, N., De Sol??rzano, C. O., Vaquero, J. J., Santos, A., Vallcorba, I., Garc??a-Sagredo, J. M. &Del Pozo, F. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry28, 289–297 (1997).
3. Chan, T. F. & Vese, L. A. Active contours without edges. IEEE Trans. Image Process. 10, 266–277(2001).
4. Yan, R. & Shao, L. Blind image blur estimation via deep learning. IEEE Trans. Image Process. 25,1910–1921 (2016).
5. Weil, W., Guo, S., Wu, F., Ji, Y. & Wang, Y. Image processing-based measurement of volume fordroplets in the microfluidic system. 2013 ICME Int. Conf. Complex Med. Eng. C. 2013 m, 518–522(2013).
FOR BASE PAPER PLEASE MAIL US
DOWNLOAD SOURCE CODE CLICK HERE
Output
Microfluidics is a research area that focuses on studying fluids in the length scales of 100 µm or less.
Recently, there has been interest in using drops as vessels to study chemical reactions and single cell
studies
. However, some channel designs could cause drops to break unintentionally.We would like to study the physical parameters that cause drops to breakup in a constriction. Our channel of interest, a tapered channel seems to promote squeezing between drops which consequentlycauses drops to break at the constriction. A single layer of monodisperse drops (drops that are the same volume) will be used to flow through the microfluidic channel. Furthermore, these monodisperse drops are packed together to produce a high volume fraction (high number of drops per volume). For the image processing algorithm, we want to characterize the drop pair (drop A and B) prior to flowing into the constriction (ie. Centroid of each drops, drop morphology, drop major and minor axisdrop orientation). We also want the algorithm to detect drops that break (break = 1 or no break =0). This
algorithm will enable us to find the physical parameters that are important for breakup.Image processing techniques learned from class will be utilized in this project. Furthermore, image processing techniques that are published in research literatures will be utilized as well. The biggest
challenge here is to be able to detect edge of the drops when it is flowing through the channel. Due to
the high volume fraction of the droplets together with how close the drops are to the channel wall. It ishard to detect the edges of some of the drops. It would be crucial for the algorithm to detect all the
drops as we want the physical parameters of all the drops that flow through the channel. Fortunately, apublished algorithm was found to be able to determine edges of clustered objects2,3 . Deblurring images will also be attempted for this project . Finally, some common imaging techniques to detect drops in amicrofluidic channel will be reviewed and possible implemented as well5.
Matlab will be used. Android devices are not required.
References
1. Teh, S.-Y., Lin, R., Hung, L.-H. & Lee, A. P. Droplet microfluidics. Lab Chip 8, 198–220 (2008).
2. Malpica, N., De Sol??rzano, C. O., Vaquero, J. J., Santos, A., Vallcorba, I., Garc??a-Sagredo, J. M. &Del Pozo, F. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry28, 289–297 (1997).
3. Chan, T. F. & Vese, L. A. Active contours without edges. IEEE Trans. Image Process. 10, 266–277(2001).
4. Yan, R. & Shao, L. Blind image blur estimation via deep learning. IEEE Trans. Image Process. 25,1910–1921 (2016).
5. Weil, W., Guo, S., Wu, F., Ji, Y. & Wang, Y. Image processing-based measurement of volume fordroplets in the microfluidic system. 2013 ICME Int. Conf. Complex Med. Eng. C. 2013 m, 518–522(2013).
FOR BASE PAPER PLEASE MAIL US
DOWNLOAD SOURCE CODE CLICK HERE
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