selecting cells using threshold in imagej

Select the skeletonized image and run the AnalyzeSkeleton plugin by clicking Plugins, Skeleton, Analyze Skeleton, and checking the branch information box. Next, remove outliers by clicking Process, Noise, Remove Outliers.Īfter saving the image as a separate file for future use, skeletonize the image using the toolbar, by clicking Process, Binary, Skeletonize. Then apply the Close function by clicking Process, Binary, Close, to connect dark pixels separated by a maximum of two pixels. Then apply the Despeckle function to the binary image to remove any remaining single pixel noise. Convert the image to binary by selecting Image, Adjust, Threshold. Use the toolbar and click Process, Noise, Despeckle. Perform a despeckle step to remove salt and pepper noise generated by the Unsharp Mask. Next, run an Unsharp Mask filter to further increase contrast, by clicking Process, Filters, Unsharp Mask. Adjusting the brightness produces the most variability in data analysis, and is therefore the most critical step. If the image is too dim to visualize the process of the microglia, use the toolbar and click Image, Adjust, Brightness/Contrast.Īdjust the minimum or maximum sliders as needed, up to the edges of the histogram but no further. Click Process, FFT, Bandpass Filter, and then convert to gray scale. If using a DAB bright field photograph, first use the FFT Bandpass filter plugin with the default settings. Use the toolbar and click Image, Lookup Tables, Grays. If using a fluorescence photomicrograph, ensure the image is 8-bit, and convert to gray scale to best visualize all positive staining. Then open a 30 micron Z stack showing Iba1 immunostained brain. Before beginning, ensure that the AnalayzeSkeleton plugin has been downloaded to ImageJ or Fiji. Demonstrating the procedure will be Kimberly Young, a technician from my laboratory. The methods described here do not require proprietary software, and it may be used for screening of brain regions or a cell-by cell analysis. The main advantage of this technique is that it quantifies microglia morphology using continuous rather than categorical variables. This method can help answer key questions in the neuroinflammmatory field, by defining the nuances of microglia form and function during health and disease. For other applications, I’ve seen people additionally convert their images in ImageJ into 32-bit or 8-bit formats prior to analyzing them.The overall goal of these skeleton and fractal analysis methods is to measure microglia morphology from IHC-prepared tissue, using quantitative methods that are both high-throughput in nature and sensitive to detect small differences in cell shapes. tif format for the analysis in order to prevent compression. oif format, and I was going to export them into a. Q3: How do I control for background fluorescence? Is there a way to “invert” the threshold that I set to measure the integrated density of the area surrounding the fluorescent protein so that I can subtract it from the integrated density of the fluorescent protein? Q2: If I manually threshold the image, do I need to make sure that I use the exact same settings for each image to eliminate any bias in thresholding? If I do, I’m assuming these settings would be based upon a control, such as wild-type, untreated cells. Will this help me to get the measurements that I need? In brief, I was going to duplicate the image, manually threshold to select only the green fluorescence, perform a watershed separation, and then analyze the particles to measure the integrated density, making sure to redirect the threshold I set to the original image.

Q1: I was planning on using the steps outlined on the “Particle Analysis” page of the ImageJ cookbook to measure the integrated density. I’d like to measure the fluorescence intensity of the entire image and then divide it by the number of cells in the image (which I will manually count) in order to get an average intensity per cell. There are around 20-30 cells in each image. I’ve included a sample image below of the fluorescence I’m hoping to measure. I’ve read several ImageJ articles and forum posts about this topic, but I’m having difficulty connecting what I’ve read to create an analysis protocol for my experiment. Hi everyone! I’m a new user of ImageJ, and I’m interested in measuring and comparing the fluorescence intensity of antibody-stained cells across a variety of conditions (wild-type vs.

Selecting cells using threshold in imagej