add solutions for exercises 07

07_examples/imagej/BlobImagesSegmentation_exercise.ijm

+// blob segmentation of images in folders
+// 
+// This script analyses folders full of image with blobs and measures their average size
+// 
+// Author: Robert Haase, rhaase@mpi-cbg.de
+// May 2019
+// 
+///////////////////////////////////
+
+folder = "C:/structure/teaching/lecture_applied_bioimage_analysis/07_examples/example_images/"
+
+function main() {
+	run("Clear Results");
+
+	processFolder(folder + "condition_A/");
+	saveAs("Results", folder + "ResultsA.xls");
+	run("Clear Results");
+
+	processFolder(folder + "condition_B/");
+	saveAs("Results", folder + "ResultsB.xls");
+	run("Clear Results");
+}
+
+function processFolder(foldername) {
+
+	filelist = getFileList(foldername);
+	number_of_files_in_folder = lengthOf(filelist);
+
+	// go through all images, segment them and collect results in a table
+	for (i = 0; i < number_of_files_in_folder; i++) {
+		open(foldername + filelist[i]);
+	
+		processImage();
+		
+		close();
+	}
+}
+
+function processImage() {
+	// remoive noise
+	run("Gaussian Blur...", "sigma=5");
+
+	// segment
+	setAutoThreshold("Huang dark");
+	setOption("BlackBackground", true);
+	run("Convert to Mask");
+	
+	// analyze
+	run("Set Measurements...", "area redirect=None decimal=3");
+	run("Analyze Particles...", "display");
+}
+
+main() {
+		
+}
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07_examples/python/BlobsSegmentation_Exercise.py

+# blobs segmentation
+#
+# This script processes a folder with images containing blobs.
+# It measures the area of all blobs and save them to disc.
+#
+# Author: Robert Haase, rhaase@mpi-cbg.de
+#         May 2019
+#
+#################################33
+
+import os
+from tifffile import imread
+from skimage import filters
+from skimage import measure
+import pandas as pd
+
+# set which directory should be processed
+directory = 'C:/structure/teaching/lecture_applied_bioimage_analysis/07_examples/example_images/'
+
+blur_sigma = 5
+
+# setup an empty table
+table = [[], [], [], [], []]
+
+def main():
+    processFolder(directory + "condition_A/");
+    processFolder(directory + "condition_B/");
+
+    saveResults(table, directory + "output_py.csv")
+
+# go through all tif files in a given folder and analyse them
+def processFolder(folder):
+    file_list = os.listdir(folder)
+
+    for filename in file_list:
+        if filename.endswith(".tif"):
+            processImage(folder + filename)
+
+# open a given image and process it
+def processImage(filename):
+
+    # open image
+    image = imread(filename)
+    print(filename)
+
+    # Gaussian blur
+    blurred_image = filters.gaussian(image, blur_sigma)
+
+    # Thresholding
+    threshold = filters.threshold_otsu(blurred_image)
+    print(threshold)
+
+    thresholded_image = blurred_image >= threshold
+
+    # run connected components analysis
+    label_image = measure.label(thresholded_image)
+
+
+    # analyse objects
+    properties = measure.regionprops(label_image)
+    count = 1
+
+    # go through all regions and save results in a table
+    for property in properties:
+        y, x = property.centroid
+
+        table[0].extend([filename])
+        table[1].extend([count])
+        table[2].extend([x])
+        table[3].extend([y])
+        table[4].extend([property.area])
+
+        count = count + 1
+
+# save a table as CSV file to disc
+def saveResults(table, filename):
+    data_frame = pd.DataFrame(table, ["Filename", "Number", "X", "Y", "Area"])
+    data_frame = data_frame.transpose()
+    data_frame.to_csv(filename)
+
+
+main()
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