Difference between revisions of "Team:Oxford/Measurement"

 
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<h1>Measurement</h1>
  
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<center><img class="img-responsive" width="200px;" src="https://static.igem.org/mediawiki/2017/2/29/T--oxford--measurementlogo.png"></center><br>
  
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<p>We have developed a lightweight, user-friendly MatLab tool that is able to extract relative quantitative data from microscopy images. Current systems include software such as ImageJ which although more comprehensive, require further training for effective use. Specifically, most software suites only focus on cell segmentation in phase contrast images, with very little support for differential interference contrast (DIC) images. We have developed a MATLAB tool that can quickly and automatically pick out cells in a DIC image. We have tested this system with our microscopy data, and has saved us a large amount of time in data analysis.</p>
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<p>There are a lot of exciting parts in the Registry, but many parts have still not been characterized. Synthetic Biology needs great measurement approaches for characterizing new parts, and efficient new methods for characterizing many parts at once. If you've done something exciting in the area of Measurement, describe it here!</p>
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                    <div class="col-md-11"><center>MATLAB Code for Measurement Device<center></div><div class="col-md-1"><i class="fa fa-arrow-down" aria-hidden="true"></i></div>
 
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<h3>Best Innovation in Measurement Special Prize</h3>
 
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<p>If you've done excellent work in measurement, you should consider nominating your team for this special prize. Designing great measurement approaches for characterizing new parts or developing and implementing an efficient new method for characterizing thousands of parts are good examples.
 
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To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Innovation in Measurement prize</a>, please describe your work on this page and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>.
 
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You must also delete the message box on the top of this page to be eligible for this prize.
 
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<h6>Figure 1. An Example of the Measurement Program Applied to One of Our Microscope Images</h6>
  
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<h5>Inspiration</h5>
 
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<p>You can look at what other teams did to get some inspiration! <br />
 
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Here are a few examples:</p>
 
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<li><a href="https://2016.igem.org/Team:Stanford-Brown">2016 Stanford-Brown</a></li>
 
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<li><a href="https://2016.igem.org/Team:Genspace">2016 Genspace</a></li>
 
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<li><a href="https://2015.igem.org/Team:William_and_Mary">2015 William and Mary</a></li>
 
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<li><a href="https://2014.igem.org/Team:Aachen">2014 Aachen  </a></li>
 
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Latest revision as of 03:48, 2 November 2017

Measurement


We have developed a lightweight, user-friendly MatLab tool that is able to extract relative quantitative data from microscopy images. Current systems include software such as ImageJ which although more comprehensive, require further training for effective use. Specifically, most software suites only focus on cell segmentation in phase contrast images, with very little support for differential interference contrast (DIC) images. We have developed a MATLAB tool that can quickly and automatically pick out cells in a DIC image. We have tested this system with our microscopy data, and has saved us a large amount of time in data analysis.



Figure 1. An Example of the Measurement Program Applied to One of Our Microscope Images