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. 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. Two types of image processing algorithms were implemented:</p> | ||
<ul> | <ul> | ||
− | <li><p><b>Standard thresholding | + | <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 by ……… 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> |
− | <li><p><b>Colour detection | + | <li><p><b>Colour detection</b><br>An eye dropper was added to select the upper and lower colour darknesses to search for (the user would click to select these colours). These colours correspond to areas of the sample with better and worse illumination respectively.Also, a slider that allows you to change the 'darkness' of the sample colour was added. This generally varies depending on room lighting conditions. With this implementation, the program performed much better, detecting the <i>Marchantia</i> gemma before the agar dents.</p></li> |
</ul> | </ul> | ||
− | <p><b>Microscopic image processing:</b> | + | <p><b>Microscopic image processing:</b> The colour detection used above can theoretically be easily adapted to work with fluorescent samples – this would prove useful for sample counting and detection of, for example, samples that successfully express a specific fluorescent protein. A similar strategy can be applied to stained samples with interesting coloured features: for example to recognize stained nuclei (eg. with toluidine blue) and in this way distinguish eukaryotic cells.</p> |
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− | The colour detection used above | + | |
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− | </p> | + | |
Revision as of 14:39, 18 September 2015