Image analysis of GFP-tagged protein localization bursts

I'm reading an article (full text here) that analyze the dynamics of localization of a GFP-tagged transcription factor (Crz1) over the time at the single-cell level, by taking movies in a fluorescent microscope.

In Methods section they say:

Fluorescence cell images were segmented using a Hough transformation algorithm in Matlab, provided by Sharad Ramanathan. Localization score was determined by the difference between the mean intensity of the 5 brightest pixels in the cell and mean intensity of the rest of the pixels in the cell.

The segmentation process here seems to be the identification of cells over the background. They then calculate a localization score, for each frame of the video, for every cell. Now there's the part that I can't understand:

Bursts were identified by thresholding traces at >1 standard deviations above background noise, estimated from the lowest 20% of values.

I searched some definitions of "background noise", but I can't figure out what does it mean in this particular context. Moreover, "lowest 20% of values" of what?

Is it plausible that they define it for the lowest 20% of values of localization scores over the time, at the cell each time considered?

Maybe can be useful a screenshot of a single cell in a photogram of the video:

-

Yep, the Hough Transform is a way to pick out shapes you're interested in, in this case they probably have it set to find circles, and they use that to segment the image.

I think that you have interpreted their methods correctly. For each cell they make a trace of localization score vs. time, localization score defined in arbitrary units as the difference between the mean of the five brightest pixels and the mean of the remaining pixels in the cell. I think the lowest 20% refers to the frames in the video that have the lowest 20% of localization scores. They take the lowest 20% of localization scores, calculate a standard deviation, and then for any frame that has a localization score that is more than 1 standard deviation above the mean of those 20%, you say that frame exhibits a burst of localization. If I'm understanding this correctly, this process would be repeated on every individual cell.

My interpretation is that the 20% doesn't have anything to do with the signal intensity from the background pixels, and it comes from analyzing the series over time, not a single image.

-

I don't know a whole lot about signal processing but I am a little familiar with artificial intelligence. Perhaps this wiki would be helpful http://en.wikipedia.org/wiki/Image_segmentation. I am familiar with k-means clustering discussed there and as a trivial segmentation method it would indeed identify the cell versus the background of the photo. The Hough transformation would be more sophisticated and probably more useful for this application but if you want to get a sense of the process the naive k-means algorithm may be helpful.

I interpret the "lowest 20% of values" to refer to the localization score values, the lowest being those who are darkest in your image. That is, the background the cell is imaged on. The background noise refers to the fact that a signal processing algorithm without any preprocessing may try to identify patterns from the background of the image rather than the cell subject matter, which is the part the researchers are after. This is why they perform the segmenting.

For instance, imagine a k-nearest neighbors algorithm http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm for computing a "localization score" for image brightness. Your paper is interested in the intensity of GFP for a cell. In the image, pixels on the border of the cell with its background will have artificially low score values because of the background: the adjacent pixels that are in the background and not part of the cell are dark, but that doesn't mean anything related to the biology of the problem. This is the noise.

The lowest 20% of values, then, should refer to a single image, not to a series over time.

-