This section provides an exhaustive description about how to use OpenCFU. If you are using it for the first time, you should maybe have a look at the video tutorial section. Questions and feedback are more than welcome, so feel free to contact me. In this manual, I will use the example of bacterial colonies, but you can obviously use the same instructions for whatever circular objects you wish to count.
Simply run and download the setup.
Linux users can compile OpenCFU for their machine. Information is available in the developers' corner.
OpenCFU is not available for Mac OSX yet.
The top of the panel shows the result as “X/Y”, where X is the number of valid colonies and Y is the total number of objects including excluded ones. The result can be set as “NA” by clicking on the “Set as NA box”. “Show objects” and “Line width” can be modified to change how the objects are represented on the display without changing the results.
The bottom part of the panel allow the user to change processing parameters.
The processed image is annotated with yellow and blue rectangles for each valid colony or red and black rectangles for invalid colonies. Scrolling up and down with the mouse will zoom in and out. Left-clicking on an colony will select it while right-clicking will change its state (e.g. valid -> invalid).
The results of the processing are shown below the display. They can be saved and altered (see Results).
Each time a processing variable is modified in the panel, the program will automatically re-analyse the image accordingly.
OpenCFU accepts usual types of images (JPEG, TIFF, PNG, BMP...). The images can be either in colour or grey-scale. It is possible to add several files at the same time or to drag-and-drop files from a folder. After loading the first files, one can browse the list of files using the “<<”, “<”, “>” and “>>” buttons.
The threshold can be one of three types: “regular”, “inverted” or “bilateral” according to the relative darkness of the colonies compared to the background.
The value of threshold is a number defining how stringent the analysis will be. The higher the threshold the more likely colonies will be missed. Conversly, a very low threshold could result in false positives. Usual values are between 3 and 30. It is also possible to ask OpenCFU to find a value of threshold automatically (“Auto”).
This parameter allows the user to constrain the analysis to objects of a certain size (between “Min” and “Max”). The size is in pixel. The minimal radius is an important parameter as it allows the user to exclude very small particles/noise that otherwise would be interpreted as colonies. By default, the maximal radius is automatically calculated from the image dimensions (“Auto-max”), but can be manually specified.
In OpenCFU, it is possible to define one or several regions of the image where the colonies lie. This is useful when the goal is to enumerate colonies in different areas. Different methods allow users to apply a mask:
It is often possible to use colour information in order to improve colonies discrimination or to exclude unwanted objects. OpenCFU provides two variables to play with:
OpenCFU can also automatically find objects that are different from the average objects and exclude them. In order to do this, the software will first calculate the “average object colour” and then exclude colonies that are too different from this value. If there are too few (less than 10) clearly defined objects, the filter will not work.
For each image, a summary result is given. It contains the following information:
In addition to the summary result, a list of objects is displayed. When clicking on a row of the list, the corresponding object will be highlighted on the display. Conversely, when left-clicking on an object in the display, the corresponding row will be selected in the list. Objects are can be sorted according to several criteria by clicking on the name of one of the features:
In order to save results, one simply needs to click on the “Save all” button, at the bottom of the result panel. A dialogue then will ask what kind of output is wanted. A summary output corresponds to the “Per image” result representation while a detailed output is similar to the “Per object” list (but for all images). In both cases, the result must be a saved as a CSV file.