def analyze_homogeneity(image_title): IJ.selectWindow(image_title) raw_imp = IJ.getImage() IJ.run(raw_imp, "Duplicate...", "title=Homogeneity duplicate") IJ.selectWindow('Homogeneity') hg_imp = IJ.getImage() # Get a 2D image if hg_imp.getNSlices() > 1: IJ.run(hg_imp, "Z Project...", "projection=[Average Intensity]") hg_imp.close() IJ.selectWindow('MAX_Homogeneity') hg_imp = IJ.getImage() hg_imp.setTitle('Homogeneity') # Blur and BG correct the image IJ.run(hg_imp, 'Gaussian Blur...', 'sigma=' + str(HOMOGENEITY_RADIUS) + ' stack') # Detect the spots IJ.setAutoThreshold(hg_imp, HOMOGENEITY_THRESHOLD + " dark") rm = RoiManager(True) table = ResultsTable() pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES, Measurements.AREA, # measurements table, # Output table 0, # MinSize 500, # MaxSize 0.0, # minCirc 1.0) # maxCirc pa.setHideOutputImage(True) pa.analyze(hg_imp) areas = table.getColumn(table.getHeadings().index('Area')) median_areas = compute_median(areas) st_dev_areas = compute_std_dev(areas, median_areas) thresholds_areas = (median_areas - (2 * st_dev_areas), median_areas + (2 * st_dev_areas)) roi_measurements = {'integrated_density': [], 'max': [], 'area': []} IJ.setForegroundColor(0, 0, 0) for roi in rm.getRoisAsArray(): hg_imp.setRoi(roi) if REMOVE_CROSS and hg_imp.getStatistics().AREA > thresholds_areas[1]: rm.runCommand('Fill') else: roi_measurements['integrated_density'].append(hg_imp.getStatistics().INTEGRATED_DENSITY) roi_measurements['max'].append(hg_imp.getStatistics().MIN_MAX) roi_measurements['integrated_densities'].append(hg_imp.getStatistics().AREA) rm.runCommand('Delete') measuremnts = {'mean_integrated_density': compute_mean(roi_measurements['integrated_density']), 'median_integrated_density': compute_median(roi_measurements['integrated_density']), 'std_dev_integrated_density': compute_std_dev(roi_measurements['integrated_density']), 'mean_max': compute_mean(roi_measurements['max']), 'median_max': compute_median(roi_measurements['max']), 'std_dev_max': compute_std_dev(roi_measurements['max']), 'mean_area': compute_mean(roi_measurements['max']), 'median_area': compute_median(roi_measurements['max']), 'std_dev_area': compute_std_dev(roi_measurements['max']), } # generate homogeinity image # calculate interpoint distance in pixels nr_point_columns = int(sqrt(len(measuremnts['mean_max']))) # TODO: This is a rough estimation that does not take into account margins or rectangular FOVs inter_point_dist = hg_imp.getWidth() / nr_point_columns IJ.run(hg_imp, "Maximum...", "radius="+(inter_point_dist*1.22)) # Normalize to 100 IJ.run(hg_imp, "Divide...", "value=" + max(roi_measurements['max'] / 100)) IJ.run(hg_imp, "Gaussian Blur...", "sigma=" + (inter_point_dist/2)) hg_imp.getProcessor.setMinAndMax(0, 255) # Create a LUT based on a predefined threshold red = zeros(256, 'b') green = zeros(256, 'b') blue = zeros(256, 'b') acceptance_threshold = HOMOGENEITY_ACCEPTANCE_THRESHOLD * 256 / 100 for i in range(256): red[i] = (i - acceptance_threshold) green[i] = (i) homogeneity_LUT = LUT(red, green, blue) hg_imp.setLut(homogeneity_LUT) return hg_imp, measuremnts