# Analyze FRAP movies with a Jython script

Here is a Jython script that does the analysis of a FRAP movie. It was developed during the Image Processing School Pilsen 2009, and updated to modern Fiji.

Once the user has loaded a good FRAP movie, well aligned with no drift, and has specified the ROI for the FRAP zone and another for the control zone, it should be possible to automate the analysis of the FRAP curve. This is what this script aims to do:

- Load a movie
- Draw a ROI for the FRAP zone, and store it as the
**first**ROI in the ROI manager (by pressing the`T`key) - Do the same for a control zone, out of the FRAP zone

Then load this script in the Script Editor, choose * Language › Python*, and run it. It will measure the FRAP intensity for all frames, try to find the FRAP frame (by finding the one with the minimal FRAP ROI intensity), and fit the FRAP curve by an increasing exponential. The parameters of the fit can be then read in the log window, and the FRAP curve and its fit are plotted. Careful:the background is taken as the intensity in the FRAP region just after the FRAP pulse.

This script uses only ImageJ functions for everything, but could be tuned to use more fancy Fiji-included plotting library, such as JFreeChart.

import java.awt.Color as Color from ij import WindowManager as WindowManager from ij.plugin.frame import RoiManager as RoiManager from ij.process import ImageStatistics as ImageStatistics from ij.measure import Measurements as Measurements from ij import IJ as IJ from ij.measure import CurveFitter as CurveFitter from ij.gui import Plot as Plot from ij.gui import PlotWindow as PlotWindow import math # Get ROIs roi_manager = RoiManager.getInstance() roi_list = roi_manager.getRoisAsArray() # We assume first one is FRAP roi, the 2nd one is normalizing roi. roi_FRAP = roi_list[0]; roi_norm = roi_list[1]; # Specify up to what frame to fit and plot. n_slices = 30 # Get current image plus and image processor current_imp = WindowManager.getCurrentImage() stack = current_imp.getImageStack() calibration = current_imp.getCalibration() ############################################# # Collect intensity values # Create empty lists of number If = [] # Frap values In = [] # Norm values # Loop over each slice of the stack for i in range(0, n_slices): # Get the current slice ip = stack.getProcessor(i+1) # Put the ROI on it ip.setRoi(roi_FRAP) # Make a measurement in it stats = ImageStatistics.getStatistics(ip, Measurements.MEAN, calibration); mean = stats.mean # Store the measurement in the list If.append( mean ) # Do the same for non-FRAPed area ip.setRoi(roi_norm) stats = ImageStatistics.getStatistics(ip, Measurements.MEAN, calibration); mean = stats.mean In.append( mean ) # Gather image parameters frame_interval = calibration.frameInterval time_units = calibration.getTimeUnit() IJ.log('For image ' + current_imp.getTitle() ) IJ.log('Time interval is ' + str(frame_interval) + ' ' + time_units) # Find minimal intensity value in FRAP and bleach frame min_intensity = min( If ) bleach_frame = If.index( min_intensity ) IJ.log('FRAP frame is ' + str(bleach_frame+1) + ' at t = ' + str(bleach_frame * frame_interval) + ' ' + time_units ) # Compute mean pre-bleach intensity mean_If = 0.0 mean_In = 0.0 for i in range(bleach_frame): # will loop until the bleach time mean_If = mean_If + If[i] mean_In = mean_In + In[i] mean_If = mean_If / bleach_frame mean_In = mean_In / bleach_frame # Calculate normalized curve normalized_curve = [] for i in range(n_slices): normalized_curve.append( (If[i] - min_intensity) / (mean_If - min_intensity) * mean_In / In[i] ) x = [i * frame_interval for i in range( n_slices ) ] y = normalized_curve xtofit = [ i * frame_interval for i in range( n_slices - bleach_frame ) ] ytofit = normalized_curve[ bleach_frame : n_slices ] # Fitter fitter = CurveFitter(xtofit, ytofit) fitter.doFit(CurveFitter.EXP_RECOVERY_NOOFFSET) IJ.log("Fit FRAP curve by " + fitter.getFormula() ) param_values = fitter.getParams() IJ.log( fitter.getResultString() ) # Overlay fit curve, with oversampling (for plot) xfit = [ (t / 10.0 + bleach_frame) * frame_interval for t in range(10 * len(xtofit) ) ] yfit = [] for xt in xfit: yfit.append( fitter.f( fitter.getParams(), xt - xfit[0]) ) plot = Plot("Normalized FRAP curve for " + current_imp.getTitle(), "Time ("+time_units+')', "NU", [], []) plot.setLimits(0, max(x), 0, 1.2 ); plot.setLineWidth(2) plot.setColor(Color.BLACK) plot.addPoints(x, y, Plot.LINE) plot.addPoints(x,y,PlotWindow.X); plot.setColor(Color.RED) plot.addPoints(xfit, yfit, Plot.LINE) plot.setColor(Color.black); plot_window = plot.show() # Output FRAP parameters thalf = math.log(2) / param_values[1] mobile_fraction = param_values[0] str1 = ('Half-recovery time = %.2f ' + time_units) % thalf IJ.log( str1 ) str2 = "Mobile fraction = %.1f %%" % (100 * mobile_fraction) IJ.log( str2 )