Edge Detection
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Given a picture projected on the bacteria which is very dark on the left hand side, and bright on the right hand side, with a very steep gradient in the middle (i.e. a sharp edge), the bacteria on the left will produce no or little A, whereas the bacteria on the right hand side produce much A. The concentration of A will of course not have a sharp edge on the boundary, but will be blurred due to diffusion. So there is a band with medium concentration of A, resulting in bacteria producing GFP marking an edge. | Given a picture projected on the bacteria which is very dark on the left hand side, and bright on the right hand side, with a very steep gradient in the middle (i.e. a sharp edge), the bacteria on the left will produce no or little A, whereas the bacteria on the right hand side produce much A. The concentration of A will of course not have a sharp edge on the boundary, but will be blurred due to diffusion. So there is a band with medium concentration of A, resulting in bacteria producing GFP marking an edge. | ||
| - | == | + | ==Pro's and Con's== |
''to come...'' | ''to come...'' | ||
==Extensions== | ==Extensions== | ||
''to come...'' | ''to come...'' | ||
Revision as of 15:16, 29 July 2005
Contents |
Intro
Independently of the group in Texas, we also came to this idea in a discussion during lunch: the classical Edge Detection problem. Rather simple in computer science, but hopefully new to the biology community (well, it seems it isn't).
Principle
In computer vision you typically want to make out the contours of an object or region. At the contour or edge something changes significantly, i.e. there is a strong gradient of color or lighting (e.g. the red car standing in front of the blue garage will have both). Edge Detection algorithms make it possible to find those changes and to draw a line corresponding to existing contours.
The living-organism approach
Basic Ideas
The "AND" approach
One could imagine a population to react to light so that some product A is produced while another possible product B is suppressed, and vice versa in darkness. Also, one could imagine that those products, A or B, do not diffuse very far (or are quickly degenerating). Thus, when a pattern is projected on a population there will be sharp gradients between the lighted area and the one in darkness. Now let's assume that the presence of both products, A and B, are needed, to trigger a third product C, e.g. green fluorescent protein (GFP). Then only the edge will show a change as a fluorescent thin line and biological Edge Detection has been achieved.
The "medium concentration" approach
In this approach, we are only using one messager A. A is expressed strongly if there is much light shining on a bacterium, and is weakly expressed if the bacterium is not much irradiated. The fluorescent product C, to stick to the notation above, is only produced when the concentration of A is in a medium range.
Given a picture projected on the bacteria which is very dark on the left hand side, and bright on the right hand side, with a very steep gradient in the middle (i.e. a sharp edge), the bacteria on the left will produce no or little A, whereas the bacteria on the right hand side produce much A. The concentration of A will of course not have a sharp edge on the boundary, but will be blurred due to diffusion. So there is a band with medium concentration of A, resulting in bacteria producing GFP marking an edge.
Pro's and Con's
to come...
Extensions
to come...
