Difference between revisions of "Team:Cambridge-JIC/MicroMaps"
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<p><b>The Method:</b> We tested our image processing software on some images of <i>Marchantia</i> gemma on a Petri dish with agar, This was intended to be a step towards our <a href="https://2015.igem.org/Team:Cambridge-JIC/Stretch_Goals" class="blue">Stretch Goal</a> - an automated screening desktop system. To write the software, the <a href="http://opencv.org/" class="blue">OpenCV</a> library was used. Two types of image processing algorithms were implemented:</p> | <p><b>The Method:</b> We tested our image processing software on some images of <i>Marchantia</i> gemma on a Petri dish with agar, This was intended to be a step towards our <a href="https://2015.igem.org/Team:Cambridge-JIC/Stretch_Goals" class="blue">Stretch Goal</a> - an automated screening desktop system. To write the software, the <a href="http://opencv.org/" class="blue">OpenCV</a> library was used. Two types of image processing algorithms were implemented:</p> | ||
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− | <li><p><b>Standard thresholding</b><br>This makes an image grey-scale and searches for the dark areas. We started off with a basic contrast increase to isolate the darker areas of the image, which we assume would correspond to samples. Rajiv then followed the steps in a paper | + | <li><p><b>Standard thresholding</b><br>This makes an image grey-scale and searches for the dark areas. We started off with a basic contrast increase to isolate the darker areas of the image, which we assume would correspond to samples. Rajiv then followed the steps in a paper [2] which was supposed to yield much better sample isolation for samples which look faint, and are hard to distinguish from their background. This ended up detecting dents in the agar gel along with the samples. To resolve this issue we came up with the next idea...</p></li> |
<center><img src="https://static.igem.org/mediawiki/2015/c/c1/CamJIC-bdct.png" style="height:250px;margin:10px"></center> | <center><img src="https://static.igem.org/mediawiki/2015/c/c1/CamJIC-bdct.png" style="height:250px;margin:10px"></center> | ||
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+ | <p style="font-size:80%">References: <br> [2] Chen, L., Chien, C. and Nguyen, X. (2013). An effective image segmentation method for noisy low-contrast unbalanced background in Mura defects using balanced discrete-cosine-transfer (BDCT). <i>Precision Engineering</i>, 37(2), pp.336-344.</p> | ||
Revision as of 18:55, 18 September 2015