Difference between revisions of "Team:Cambridge-JIC/MicroMaps"
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<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> | <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> | ||
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+ | <center><img src="//2015.igem.org/wiki/images/e/ef/CamJIC-Software-ImageRec.jpg" style="width:400px;margin:10px"><p><i><b>Figure 4</b>: Sample recognition working on Petri dish with Marchantia gemma. The program highlights the samples it finds in red. Note that the agar dent is not included in the final output. This was achieved using the color detection algorithm.</i></p></center> | ||
<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> | <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> | ||
<p>However, we have not implemented sample recognition into MicroMaps Alpha, mostly due to lack of time and difficulties for coping with multicolour images. Still, the script for image recognition is in the <a href="" class="blue">software package</a> for you to try out (and improve). | <p>However, we have not implemented sample recognition into MicroMaps Alpha, mostly due to lack of time and difficulties for coping with multicolour images. Still, the script for image recognition is in the <a href="" class="blue">software package</a> for you to try out (and improve). |
Revision as of 15:06, 18 September 2015