Team:Bologna/Software

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= Visual Fluo Bacteria: a fluorescence image analisys software =
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= Visual Fluo Bacteria: a software for the analysis of bacteria fluorescence images =
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=== Visual Fluo Bacteria: a fluorescence image analisys software ===
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__FORCETOC__
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Image of fluorescence bacteria is commonly used to visualize the activity of genetically engineered bacteria (see Figure 1).  
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Promoter activity can be revealed using fluorescent proteins as reporters.
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An image of fluorescence bacteria can be obtain thanks to a microscopy system and a video camera ( next figure shows a bacterial image).  
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[[Image:field.jpg|200px|thumbnail|Fluorescence field|center]]
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[[Image:field.jpg|200px|thumbnail|Figure 1. Fluorescence field|center]]
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Bacteria fluorescence imagines can be obtained by optical microscopy with a ccd camera. We develop a matlab tool to analyze such kind of bacterium imagine that can be acquired in different format (jpg, bpm, tiff). The software initially segments  bacteria and then computed their number. For each segmented bacteria the software computes the size in pixel, the mean and standard deviation of 
intensity florescence. The use of the software is easy and intuitive (see user interface in Figure 2).
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In this image we can achieve bacteria's morphology informations, size and the number of bacteria in a single view. Additionally we can assess the promoter activation dynamic in terms of fluorescence mean value per bacterium and standard deviation.
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The software we created is easy and intuitive and it based on an algorithm that avail two parameters. In the Figure 2 it is possible contemplate the main software frame.
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<br>
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[[Image:Screenshot.jpg|thumbnail|500px|Main frame|center]]
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[[Image:Screenshot.jpg|thumbnail|500px|Figure 2. Main frame|center]]
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The algorithm reads image fluorescence and converts it into a "black and white" one that is then filtered by Top Hat filter to correct uneven illumination when the background is dark. The following step is needed to compute the global threshold in order to convert an intensity image to a binary image using Otsu’s method. The image is then ready to be scanned, pixel by pixel, to detect bacteria (cluster  of pixels) and obtain final informations about their area, fluorescence mean (in RGB channel: R for RFP, G for GFP, B for CFP) and standard deviation. All the data are processed with area and focus efficiency parameters to estimate the population fluorescence mean, standard deviation, median, minimal and maximal fluorescence levels.
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The algorithm reads fluorescence images and converts it into a "black and white" one. Then the image is filtered by Top Hat filter to correct uneven illumination when the background is dark. In the next step the global threshold is computed in order to convert an intensity image to a binary image using Otsu’s method. The image is then ready to be scanned, pixel by pixel, to detect bacteria (cluster  of pixels) and obtain informations about their area, fluorescence mean and standard deviation. Fluorescence is read from R channel for RFP, G for GFP and B for CFP. The software allows to filter imagine by neglecting the bacteria that have morphologic parameters out of a prefixed range. The user can set two indicators:<br>
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Our software consists in a filtering of bacteria that are on the acquired image. In particular the operator can set two indicators:
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* Area dimension range ( definite by a superior and an inferior limit): This range defers to selected the bacteria that have similar size.
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* Area dimension range ( definite by a superior and an inferior limit)
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* Fluorescence intensity ( definite by the std\mean ratio referred to each bacteria): this values consent to throw away the bacteria that lie on another focal layer or that aren’t focused correctly.
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* Fluorescence intensity ( definite by the std\mean ratio)
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At this point, the filter based on these values discard the bacteria that have a std\mean higher than threeshold and the bacteria that have a dimension out the range. The ratio standard deviation / mean fluorescence referred for each bacteria is  used to throw away during the final analysis the bacteria that lie on another focal layer and they aren’t focused correctly.
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At this point, the filter based on these values discard the bacteria that have a std\mean higher than the threeshold and the bacteria that have a dimension out of the range.  
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Bacterial area, the selected population size and the chance to discard bacteria with higher standard deviation and mean fluorescence values, are all together settings that can be fine tuned with the selected software. These parameters are relevant in order to select always the bacteria population in the same physiological state and to discard not- bacterial segmented clusters.
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<div style="text-align:center">  
<div style="text-align:center">  
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Example of analisys with very selective parameters (low ratio std/mean and narrow range of area dimensions)
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Example of filtering with very selective parameters (low ratio std/mean and narrow range of area dimensions)
</div>
</div>
{| align=center
{| align=center
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|[[Image:before.jpg|200px|thumbnail|Clustered image before analisys]]
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|[[Image:before.jpg|200px|thumbnail|Figure 3. Image before filtering]]
|[[Image:freccia.jpg|200px]]
|[[Image:freccia.jpg|200px]]
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|[[Image:after.jpg|200px|thumbnail|Clustered image after analisys]]
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|[[Image:after.jpg|200px|thumbnail|Figure 4. Image after filtering]]
|}
|}
<br>
<br>
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The ratio standard deviation / mean fluorescence referred for each bacteria is  used to throw away during the final analysis the bacteria that lie on another focal layer and they aren’t focused correctly.
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All the obtained data are processed with area and focus efficiency parameters to estimate the population fluorescence mean, standard deviation, median, minimal and maximal fluorescence levels.<br>
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+
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The algorithm reads  image fluorescence and converts it into a "black and white" one that is then filtered by Top Hat filter to correct uneven illumination when the background is dark. The following step is needed to compute the global threshold in order to convert an intensity image to a binary image using Otsu’s method. The image is then ready to be scanned, pixel by pixel, to detect clusters (bacteria) and obtain final informations about their area, fluorescence mean (in RGB channel: R for RFP, G for GFP, B for CFP) and standard deviation. All the data are processed with area and focus efficiency parameters to estimate the population fluorescence mean, standard deviation, median, minimal and maximal fluorescence levels.  
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The software allows an original photo analysis with “white colored bacteria” in order to underly exactly which bacteria the data are coming from.
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[[Image:adobe.jpg|30px]][[Media:UserManual.pdf|User Manual]]
[[Image:adobe.jpg|30px]][[Media:UserManual.pdf|User Manual]]
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== Microscopy system for fluorescence image analysis ==
 
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<br>
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[https://2008.igem.org/Team:Bologna/Software ''Up'']
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[[Image:Microscopy1.jpg|center|thumbnail|700px|Acquisition system]]
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== Microscopy system for fluorescence image acquisition ==
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[[Image:Microscopy1.jpg|right|thumbnail|450px|Figure 5. Acquisition system]]
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The illumination system is composed of a 75 Watt Xenon arc lamp connected to a Photon Technology Instruments DeltaRAM X monochromator, which breaks up a single polychromatic light beam into several monochromatic light beams (with only one wavelength each). Only the selected wavelength can pass through the output port and reach the microscope.
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The system’s core is a Nikon Eclipse TE2000-U inverted fluorescence microscope. For GFP image acquisition we used a B-2A filter by Nikon with an excitation band between 450 and 490 nm and the optimal emission placed at 520 nm.
The system’s core is a Nikon Eclipse TE2000-U inverted fluorescence microscope. For GFP image acquisition we used a B-2A filter by Nikon with an excitation band between 450 and 490 nm and the optimal emission placed at 520 nm.
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The camera used to acquire images and film segments is a Nikon DS-5m with a DS-U1 controller. This one receives the acquired signal form the camera through a serial connection and sends it to the PC through an USB slot. Nikon also supplied an interface software for image acquisition and elaboration.
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The illumination system is composed of a 75 Watt Xenon arc lamp connected to a Photon Technology Instruments DeltaRAM X monochromator. The home-made control of monochromator  is implemented in a Labview and permits the regulation of the excitation wavelength and the calibration of the system.
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The control software is implemented in a Labview environment and permits the regulation of the excitation wavelength and the calibration of the system. It also pictures the output signal from the photomultiplier, which can be memorized and elaborated.
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The camera used to acquire images  is a Nikon DS-5m with a DS-U1 controller. This one receives the acquired signal form the camera through a serial connection and sends it to the PC through an USB slot. Nikon also supplied an software for image acquisition (X-Data).
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<br><br><br><br><br><br><br><br><br>
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[https://2008.igem.org/Team:Bologna/Software ''Up'']
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[https://2008.igem.org/Team:Bologna/Project ''Up'']
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= A.R.Q.(Automatic Registry Query) Software=
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The idea from which was born this application is to realize a database registry like, in order to share with all
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the faculty of the bologna university parts and interesting project. The devolpment starts from the
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registry, which is well structured and an important source of information, we want so to create a tool
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that permits to query automatically the web page of the registry to achieve the data of a part and
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compare with ours. We start to develop a tools that scans the registry page to find three
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parameters:the registry code, the partial description of the parts and the sequence.<br>
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There are three research modality included in this tools:<br>
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* INPUT :code OUTPUT:sequence and partial description;
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* INPUT:parts or subparts of the name OUTPUT:all the parts which share that name;
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* INPUT:sequence OUTPUT:all tha parts that match that sequence;
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It's possible to imit the search to a selected categories of the registry or to extends to all with a drop
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down menù.<br>
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The software was developed in java language to give the possibility to run it on all the type of
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machine provided thath you have a java virtual machine.<br>
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The user's interface is friendly an very simple:
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[[Image:Findpart.jpg|center|thumbnail|500px]]
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<br><br>
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the REGISTRY CODE PARTS requires the standard registy code BBa_XXXXX,
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the PARTIAL DESCRIPTION is a free text field, where can be writted any keywords of the
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research
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the TYPE PARTS is a drop down menù with twelve option:
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*measurement
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*generator
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*composite
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*rna
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*dna
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*conjugation
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*reporters
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*signalling
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*rbs
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*regulatory
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*terminator
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*all<br>
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to limit only in a restricted arts of the registry the search.<br>
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the SEQUENCE in a text area in which insert the sequence of the parts without space.<br>
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When we use the parameters partial description and sequence the search will not be unique so the
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resutl will show in a new window teh result page:<br>
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[[Image:finestra scelta.jpg|center|thumbnail|450px]]
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<br>
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this page show a list of name of the registry parts and when we query the tools with a sequence is
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possible through a tooltip to view in order :<br>
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*the name of the parts
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*the first and the last index of the alignment between the query and the answer sequence
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*the length of the answer sequence
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*the group of the registry parts(regulatory,conjugation,signalling....)
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<br>
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[[Image:tooltip.jpg|center|thumbnail|450px]]
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When a part is selected, by a double mouse double click, the program automatically update the windows loading the empty fields with the part's data. The sequence field show the match between the parts changing to upper case the shared base.
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[[Image:sequencematch.jpg|center|thumbnail|450px]]
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Latest revision as of 11:10, 27 April 2016

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HOME PROJECT TEAM SOFTWARE MODELING WET LAB LAB-BOOK SUBMITTED PARTS BIOSAFETY AND PROTOCOLS


Contents

Visual Fluo Bacteria: a software for the analysis of bacteria fluorescence images

Image of fluorescence bacteria is commonly used to visualize the activity of genetically engineered bacteria (see Figure 1).


Figure 1. Fluorescence field


Bacteria fluorescence imagines can be obtained by optical microscopy with a ccd camera. We develop a matlab tool to analyze such kind of bacterium imagine that can be acquired in different format (jpg, bpm, tiff). The software initially segments bacteria and then computed their number. For each segmented bacteria the software computes the size in pixel, the mean and standard deviation of 
intensity florescence. The use of the software is easy and intuitive (see user interface in Figure 2).


Figure 2. Main frame


The algorithm reads fluorescence images and converts it into a "black and white" one. Then the image is filtered by Top Hat filter to correct uneven illumination when the background is dark. In the next step the global threshold is computed in order to convert an intensity image to a binary image using Otsu’s method. The image is then ready to be scanned, pixel by pixel, to detect bacteria (cluster of pixels) and obtain informations about their area, fluorescence mean and standard deviation. Fluorescence is read from R channel for RFP, G for GFP and B for CFP. The software allows to filter imagine by neglecting the bacteria that have morphologic parameters out of a prefixed range. The user can set two indicators:

  • Area dimension range ( definite by a superior and an inferior limit): This range defers to selected the bacteria that have similar size.
  • Fluorescence intensity ( definite by the std\mean ratio referred to each bacteria): this values consent to throw away the bacteria that lie on another focal layer or that aren’t focused correctly.

At this point, the filter based on these values discard the bacteria that have a std\mean higher than the threeshold and the bacteria that have a dimension out of the range.



Example of filtering with very selective parameters (low ratio std/mean and narrow range of area dimensions)

Figure 3. Image before filtering
Freccia.jpg
Figure 4. Image after filtering


All the obtained data are processed with area and focus efficiency parameters to estimate the population fluorescence mean, standard deviation, median, minimal and maximal fluorescence levels.


To download the program and relative user manual, you can click on the following icons.

Winrar.jpgVisual Fluo Bacteria 1.0
Note: after decompacting file, execute and run classificazione.m
Adobe.jpgUser Manual


Up

Microscopy system for fluorescence image acquisition

Figure 5. Acquisition system

The system’s core is a Nikon Eclipse TE2000-U inverted fluorescence microscope. For GFP image acquisition we used a B-2A filter by Nikon with an excitation band between 450 and 490 nm and the optimal emission placed at 520 nm.

The illumination system is composed of a 75 Watt Xenon arc lamp connected to a Photon Technology Instruments DeltaRAM X monochromator. The home-made control of monochromator is implemented in a Labview and permits the regulation of the excitation wavelength and the calibration of the system.

The camera used to acquire images is a Nikon DS-5m with a DS-U1 controller. This one receives the acquired signal form the camera through a serial connection and sends it to the PC through an USB slot. Nikon also supplied an software for image acquisition (X-Data).









Up

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