To enable a versatile applicability of HTPheno to different high-throughput phenotyping setups, we decided to follow a modular approach: several configuration files allow the adjustment of the plugin to the users' needs. Images from such a manual and low cost system can also be the source for computation of phenotypic parameters by HTPheno. This setup enables the user to record images from different plants manually. For an adequate illumination it is recommended to install a light sources above and on both sides of the plant. In our experiments light blue showed a good colour choice as it is easily separable from colours in the plants. The plant should be arranged on a table which is in front of a unicolour wall. Depending on the lens of the camera and its resolution the perspective distortion could also be reduced by a preprocessing step in the image analysis pipeline. Such a manual image acquisition consists of a commercially available standard camera with a camera tripod which is positioned in some distance to the plant to reduce perspective distortion. These features make EzColocalization well-suited for experiments with low reporter signal, complex patterns of localization, and heterogeneous populations of cells and organisms. Features of EzColocalization include: (i) tools to select individual cells and organisms from images (ii) filters to select specific types of cells and organisms based on physical parameters and signal intensity (iii) heat maps and scatterplots to visualize the localization patterns of reporters (iv) multiple metrics to measure colocalization for two or three reporters (v) metric matrices to systematically measure colocalization at multiple combinations of signal intensity thresholds and (vi) data tables that provide detailed information on each cell in a sample. EzColocalization is designed to be easy to use and customize for researchers with minimal experience in quantitative microscopy and computer programming. Here we describe an open source plugin for ImageJ called EzColocalization to visualize and measure colocalization in microscopy images. Insight into the function and regulation of biological molecules can often be obtained by determining which cell structures and other molecules they localize with ( i.e. Version 1.15 the sRGB to Lab conversion now matches the colour space converter plugin in ImageJ.ġ.16 fixed Lab space rescaling into 8 bits container. Fixed bug related to the name of files created with the image calculator command, solved bug in macro generated code for IJ v1.43h onwards.ġ.11 background according to Binary>Options, thanks to Michael Schmid for several tips.ġ.12 fixed the Selection button to return the proper ROI rather than the inverse of it, launch recorder if not active when pressing, added batch mode to the macro code. The zip file also includes RGB2YUV and RGB2Lab plugins which are necessary for that macro (note that these plugins convert an RGB image to YUV and CIE Lab colour spaces but with values mapped into an 8-bit space).ġ.8 added a warning and commented lines for back/foreground colours.ġ.9 solved the problem of not applying the threshold if the original was being displayed.ġ.10 added Select button. Close the Threshold dialog window without clicking any of the buttons.1.7 Added a button that generates a macro and sends it to the Recorder window (if active) based on the current plugin settings. Adjust the sliders so that the dark, low-value pixels that represent water turn red, but those that represent land don't change. The Threshold dialog window allows you to highlight pixels in an image that have values within a range you define. This action converts each pixel's color information into a brightness measurement. Choose Image > Type > 8-bit to convert the image to grayscale.NOTE: the techniques described here do not work as well on the MODIS images that you used in the previous section, as there is not enough contrast in the MODIS images. As the ranges of pixel values that represent land and water are unique, you can manipulate ImageJ to highlight the pixels that represent water in each image, and measure the area it covers. If the color information for each pixel is converted to grayscale, the dark pixels representing water will have low values and the lighter pixels that represent land will have higher values. In these images, pixels that represent water are much darker than pixels that represent land.
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