Display as: Stacks can be displayed as volume renderings, orthoslices, surfaces or surface plots.The user can select from a list of all open images. Image: The image which should be displayed in the viewer.A dialog window opens, asking for some information: Record all results in excel.After you have started the 3D viewer, click on File › Add content. Repeat steps for all images being imaged and analyzed.The percent area of signal is calculated by dividing the number of red pixels by the total number of red and green pixels, multiplied by 100.Record the number of Value 0 (red) and Value 1 (green) pixels. With the final classified image with ROI open, open the histogram tool (Analyze > Histogram) and select “list” to get pixel counts.Determine percent area of positive signal:.Crop further by making sure ROI is selected and selecting Edit > Clear Outside.Apply the newly sized ROI to the classified image.Once the classifier reaches sufficient accuracy, select “Create result” to create a classified image.Repeat steps to label additional traced areas as either class 1 or class 2 until the classifier’s segmentation reaches sufficient accuracy.After numerous traces are labeled as either class 1 or class 2, select “Train classifier.”.Trace MANY regions of positive signal and then select “Add to class 2”.Trace MANY regions of negative signal and then select “Add to class 1”.Run the Trainable Weka Segmentation plug-in (Plugins > Segmentation > Trainable Weka Segmentation):.Save the newly sized ROIs using the ROI Manager tool.Apply this ROI to each image, center ROI over tissue, then crop the image to fit the ROI using Shift-X.Save the ROI by using the Region of Interest (ROI) Manager tool (Analyze > Tools > ROI Manager). As you will need to apply this same ROI to your experimental condition image, make sure it works for both. Start with around 1500×900 pixels and adjust.

Edit > Selection > Specify to define a region that encompasses a large portion of your sample.Ensure that you are working with an 8-bit image by selecting Image > Type > 8 bit.Open control and experimental images being analyzed.Trainable Classification Plugin: This plugin is used for classifying positive and negative areas of signal in a large sample and determining the % pixels that are positive within a region of interest, a useful application for live/dead cell experiments. Now calculate corrected total cell fluorescence (CTCF) = Integrated Density – (Area of Selected Cell x Mean Fluorescence of Background readings).Calculate the mean fluorescence of background readings.Repeat for several more cells and background regions.Now select a small area of your image that has no fluorescence.A window will pop up with your measurements. Now you can analyze by going to Analyze > Measure.Make sure Area, Integrated Density and Mean Grey Value are checked. Set desired parameters by going to Analyze > Set Measurements.Outline desired cell with Freehand ROI tool.This is the total fluorescent area.įluorescence Intensity: This method determines the corrected total fluorescence by subtracting out background signal, which is useful for comparing the fluorescence intensity between cells or regions. Add areas for all fluorescent regions.This will give you the area of fluorescent regions of your image. Go to Analyze > Analyze Particles > Display results.Slide the Hue slider to match the color- so that the fluorescent areas are selected.To threshold your image, go to Image > Adjust > Color threshold.Fluorescence Area: This method can be used for a quick determination of fluorescent labeling area.

Imagej threshold to selection