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Team:TU Eindhoven/Modeling/Bacterial Cell Counter
From 2014.igem.org
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<p>To detect the cells <i>EdgeDetect</i>, a function of Mathematica using gradient methods, is used. It is then followed by a dilation in order to make the edges clearer. The results of these actions can be seen in <a href="#Fig2">Figure 2</a>.</p> | <p>To detect the cells <i>EdgeDetect</i>, a function of Mathematica using gradient methods, is used. It is then followed by a dilation in order to make the edges clearer. The results of these actions can be seen in <a href="#Fig2">Figure 2</a>.</p> | ||
| - | <p>This results in | + | <p>This results in the clustering of black pixels. The program then looks for the clusters bigger than 2000 pixels and smaller than 6000 pixels. In <a href="#Fig3">Figure 3</a>, these clusters are visualized with blue circles.</p> |
| - | <h3>Parameters | + | <h3>Parameters for Droplets Detection</h3> |
| - | <p>The parameters of this function are chosen | + | <p>The parameters of this function are chosen in a way that prioritizes decrease in false positives over the decrease in false negatives. This is because a false positive means a non-existing droplet and thus false data, while false negative only lowers the sample size, which can be increased by analyzing more images.</p> |
<figure style="float:right;margin-right:0;"> | <figure style="float:right;margin-right:0;"> | ||
<img id='Fig3' src="https://static.igem.org/mediawiki/2014/a/aa/TU_Eindhoven_Modeling6.png" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;"> | <img id='Fig3' src="https://static.igem.org/mediawiki/2014/a/aa/TU_Eindhoven_Modeling6.png" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;"> | ||
| - | <figcaption style="font-size:18px;color:#CCCCCC;">Figure 3. | + | <figcaption style="font-size:18px;color:#CCCCCC;">Figure 3. The results of clustering shown as blue circles, layered onto the original image </figcaption> |
</figure> | </figure> | ||
Revision as of 00:45, 18 October 2014
Bacterial Cell Counter
Once photos of the microfluidics results have been made, the droplets and the cells inside the droplets have to be counted. In order to have reliable results, this has to be done on a large scale. A computer program has been designed to perform the counting on large numbers of photos. To give a better visualization of the process, a step by step analysis of the image shown in Figure 1 is given below.
The program has the following steps: firstly, to find and count the droplets, then to find and count the cells inside of the droplets and finally to create a histogram of the results. By adding up the histograms of multiple images one get a impression of how the cells are divided over the droplets.
Droplet Detection
To detect the cells EdgeDetect, a function of Mathematica using gradient methods, is used. It is then followed by a dilation in order to make the edges clearer. The results of these actions can be seen in Figure 2.
This results in the clustering of black pixels. The program then looks for the clusters bigger than 2000 pixels and smaller than 6000 pixels. In Figure 3, these clusters are visualized with blue circles.
Parameters for Droplets Detection
The parameters of this function are chosen in a way that prioritizes decrease in false positives over the decrease in false negatives. This is because a false positive means a non-existing droplet and thus false data, while false negative only lowers the sample size, which can be increased by analyzing more images.
Bacterial Cell Detection
The program is now at the point where it has to count the number of cells in the droplets. Using the droplets from the edgedetect picture, erosion with value 1 is performed (resulting with the deletion of before in closing of the image). This makes sure the cells connected to the borders of the droplet are loose, the program deletes the border components so it only finds cells. The program uses EnclosingComponentCount instead of count now because a lot of cells became empty circles to get the results.
The results for this image are 9 false positives and 11 false negatives on a total of 93 cell in 126 droplets. Because some droplets had multiple false positives or false negatives the next step to improve this number is selecting to droplets better.
Click here to download the code
