Updated notebooks

c1b6b099 · Marvin Albert · 54571fd0 · c1b6b099 · c1b6b099 · c1b6b099
Commit c1b6b099 authored 9 months ago by Marvin Albert
--- a/notebooks/01_image_handling_basics.ipynb
+++ b/notebooks/01_image_handling_basics.ipynb
--- a/notebooks/02_image_filtering.ipynb
+++ b/notebooks/02_image_filtering.ipynb
--- a/notebooks/exercise_01_image_handling_basics.ipynb
+++ b/notebooks/exercise_01_image_handling_basics.ipynb
--- a/notebooks/exercise_01_thresholding.ipynb
+++ b/notebooks/exercise_01_thresholding.ipynb
@@ -55,6 +55,23 @@
   "source": [
    "7. There are other algorithms for thresholding than the one of Otsu. Try to find one in the [Scikit-image documentation](http://scikit-image.org/docs/dev/api/skimage.filters.html) and to use it"
   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "8. Extra: Try to load several images from the same dataset and explore whether\n",
+    "- one threshold is good for all\n",
+    "- each image requires a different threshold\n",
+    "- if so, is there an automated thresholding algorithm which works for all images?\n",
+    "\n",
+    "    Images:\n",
+    "\n",
+    "    - https://cildata.crbs.ucsd.edu/media/images/13585/13585.tif\n",
+    "    - https://cildata.crbs.ucsd.edu/media/images/13586/13586.tif\n",
+    "    - https://cildata.crbs.ucsd.edu/media/images/13587/13587.tif\n",
+    "    - https://cildata.crbs.ucsd.edu/media/images/13588/13588.tif\n"
+   ]
  }
 ],
 "metadata": {

 %% Cell type:markdown id: tags:

 # Exercise 2: Segmentation

 %% Cell type:markdown id: tags:

 1. Import the relevant packages: numpy, matplotlib and skimage

 %% Cell type:markdown id: tags:

 2. Import the image from this url address: https://cildata.crbs.ucsd.edu/media/images/13585/13585.tif.

 %% Cell type:markdown id: tags:

 3. Create a new variable called ```image_green``` that contains only the green channel by slicing the array

 %% Cell type:markdown id: tags:

 4. Plot this image

 %% Cell type:markdown id: tags:

 5. Plot a histogram of the image

 %% Cell type:markdown id: tags:

 6. Try to determine an appropriate threshold to recover the large structures in the image, create the corresponding mask and plot it

 %% Cell type:markdown id: tags:

 7. There are other algorithms for thresholding than the one of Otsu. Try to find one in the [Scikit-image documentation](http://scikit-image.org/docs/dev/api/skimage.filters.html) and to use it
+
+%% Cell type:markdown id: tags:
+
+8. Extra: Try to load several images from the same dataset and explore whether
+- one threshold is good for all
+- each image requires a different threshold
+- if so, is there an automated thresholding algorithm which works for all images?
+
+    Images:
+
+    - https://cildata.crbs.ucsd.edu/media/images/13585/13585.tif
+    - https://cildata.crbs.ucsd.edu/media/images/13586/13586.tif
+    - https://cildata.crbs.ucsd.edu/media/images/13587/13587.tif
+    - https://cildata.crbs.ucsd.edu/media/images/13588/13588.tif