EMBL Course

workshops/bioimage-analysis-fundamentals.md

@@ -292,10 +292,11 @@ Fill in the blanks, using these words: less, more, 8, 255, 4, more.
 
 <img src='https://g.gravizo.com/svg?
  digraph G {
-    shift [fontcolor=white,color=white];
-    "label image" -> shape_analysis -> table;
-    table -> object_rows;
-    table -> feature_columns;  
+	shift [fontcolor=white,color=white];
+	"label image" -> shape_analysis -> table;
+	table -> object_rows;
+	table -> feature_columns;
+	table -> visualisation;  
 }
 '/>
 
@@ -304,11 +305,11 @@ Fill in the blanks, using these words: less, more, 8, 255, 4, more.
 
 * Open image: xy_8bit_labels__four_objects.tif
 * Perform shape measurements and discuss their meanings.
-* Color objects by their measurement values.
+* Explore results visualisation
+	* Color objects by their measurement values.
 * Add a calibration to the image and check which shape measurements are affected.
 * Draw a test image to understand the shape measurements even better.
 
-
 ### Formative assessment
 
 True or false? Discuss with your neighbour!
@@ -475,17 +476,5 @@ Fill in the blanks, using these words: integrated, mean, number_of_pixels, decre
 &nbsp;
 
 
-## Recap
-
-(Work in pairs of two)
-
-* Take one A4 paper
-* Draw a typical workflow: From intensity image to objects shape table.
-* Write down what you remember (max. 3 facts) about:
-	* Intensity measurements
-	* Object shape measurements
-	* Label image
-	* Pixel data types
-
 
 

workshops/course-preamble.md

+# Course preamble
+
+<img src='https://g.gravizo.com/svg?
+ digraph G {
+        shift [fontcolor=white,color=white];
+        "learn" -> "concepts";
+	"concepts" -> "software independent" [label="  are"];
+}
+'/>
+
+
+The focus of this course it **not** to learn a specific image analysis software.
+
+In fact, one could probably teach most concepts without a computer. 
+

workshops/distance-transform.md

+# Distance transform
+
+<img src='https://g.gravizo.com/svg?
+ digraph G {
+        shift [fontcolor=white,color=white];
+        "binary image" -> "distance transform" -> "distance map";
+	"distance map" -> "values are distances";
+}
+'/>
+
+## Activity: Explore distance transform 
+
+- Open image: xy_8bit_binary__two_objects.tif
+- Learn:
+	- It matters what is foreground and what is background.
+	- The image data type limits the possible distance values.
+	- There is a difference between calibrated vs. pixel-based distance transforms.
+
+
+## Actvity: Use distance map for automated distance measurements
+
+- Open reference object image: xy_8bit_binary__single_object.tif
+- Compute distance map 
+- Open label image: xy_8bit_labels__two_spots.tif
+- Measure "intensity" of label image objects in distance map 
+	- intensity is distance
+
+## Activity: Use distance map for automated region selection
+
+- Open reference object image: xy_8bit_binary__single_object.tif
+- Compute distance map
+- Threshold distance map to select regions
+ 
+### Formative Assessment
+
+TODO
+
+### Learn more
+
+TODO

workshops/image-feature-enhancement.md

 # Image feature enhancement
 
+
 <img src='https://g.gravizo.com/svg?
  digraph G {
-    shift [fontcolor=white,color=white];
-    image -> filter -> "enhanced image";
-    "enhanced image" -> "feature" [label="  aka"];
-    filter -> "feature enhancement" [label=" aka"];   
+	shift [fontcolor=white,color=white];
+	image -> filter -> "enhanced image";
+	node [shape=box, color=grey, fontcolor=grey];
+	"enhanced image" -> "feature" [label=" aka", style=dashed, color=grey, fontcolor=grey, fontsize=10];
+	node [shape=box, color=grey, fontcolor=grey];
+	filter -> "feature enhancement" [label=" aka", style=dashed, color=grey, fontcolor=grey, fontsize=10];
 }
 '/>
 
 
-
 ## Difference of Gaussian (DoG) for spot enhancement
 
 <img src='https://g.gravizo.com/svg?
@@ -18,8 +20,8 @@
 	shift [fontcolor=white,color=white];
 	image -> "small blur";
 	image -> "large blur";
-	"small blur" -> "remove noise";
-	"large blur" -> "estimate local background";
+	"small blur" -> "noise filtered";
+	"large blur" -> "local background";
 	"small blur" -> "DoG = small blur - large blur";
 	"large blur" -> "DoG = small blur - large blur";
 }
@@ -29,9 +31,10 @@
 
 - Open image: xy_8bit__two_spots_noisy_uneven_background.tif
 - Appreciate that you cannot simply threshold them
-- Copy the image and blur with a Gaussian of small radius -> gs
-- Copy the image and blur with a Gaussian of bigger radius -> gb
-- Create `DoG = gs - gb`
+- Copy image and blur with a Gaussian of small sigma -> Gs
+- Copy image and blur with a Gaussian of bigger sigma -> Gb
+	- For the official DoG: `rb = sqrt(2) * rs`
+- Create `DoG = Gs - Gb`
 - Appreciate that you can simply threshold the DoG image
  
 ### Formative Assessment

workshops/notes-for-teachers.md

+# Teaching recommendations
+
+- Try to use white-board whenever possible.
+- Make sure there is (at least) one whiteboard with sufficient paper. 

workshops/object-splitting.md

+# Object splitting
+
+## "Intensity based" watershed
+
+<img src='https://g.gravizo.com/svg?
+ digraph G {
+        shift [fontcolor=white,color=white];
+        "intensity image" -> "watershed" -> "label image";
+	"label image" -> "pond regions";
+}
+'/>
+
+
+### Activity: Explore intensity based watershed 
+
+- Open image: xy_8bit__touching_objects.tif
+- Invert image for watershed 
+- Apply watershed
+
+### Activity: Use intensity based watershed for object segmentation
+
+- Open intensity image: xy_8bit__touching_objects.tif
+- Threshold intensity image => binary image (aka "mask")
+- Invert intensity image for watershed 
+- Apply watershed, using the mask
+
+## "Shape based" watershed
+
+<img src='https://g.gravizo.com/svg?
+ digraph G {
+        shift [fontcolor=white,color=white];
+        "binary image" -> "distance map" -> "watershed" -> "label image";
+	"label image" -> "thickness ponds";
+}
+'/>
+
+### Activity: Explore shape based watershed
+
+- Open image: xy_8bit__touching_objects_same_intensity.tif
+- Threshold -> Binary image
+	- Copy binary image (we'll need it as mask later...)
+- Binary image -> Distance map
+- Distance map -> Watershed 
+
+
+### Learn more
+
+TODO
+
+### Formative Assessment
+
+TODO
+

workshops/rank-filters.md

@@ -6,19 +6,18 @@
  digraph G {
     shift [fontcolor=white,color=white];
 	"rank filters" -> awesome [label="  are"];
-	"rank filters" -> minimum [label="  e.g."] -> erosion [label="  aka"];
-	"rank filters" -> maximum [label="  e.g."] -> dilation [label="  aka"];
-	"rank filters" -> median [label="  e.g."];
+	"rank filters" -> minimum -> erosion [label="  aka"];
+	"rank filters" -> maximum -> dilation [label="  aka"];
+	"rank filters" -> median;
+	"rank filters" -> "size" [label="  have"];
   }
 '/>
 
-
 ### Activity: Explore rank filters on binary images
 
 - Open image: xy_8bit_binary__two_spots_different_size.tif
 - Explore how the structures grow and shrink when using erosion and dilation
 
-
 ### Activity: Explore rank filters on grayscale images
 
 - Open image: xy_8bit__two_noisy_squares_different_size.tif
@@ -26,12 +25,22 @@
 	- removes noise
 	- removes small structures
 	- preserves egdes
-- Compare median filter to a mean filter of same radius
+- Compare median filter to mean filter of same radius
 
 
 ### Formative assessment
 
-TODO
+True or false? Discuss with your neighbour!
+
+1. Median filter is just another name for mean filter.
+2. Small structures can completely disappear from an image when applying a median filter.
+
+Fill in the blanks, using those words: shrinks, increases, decreases, enlarges.
+
+1. An erosion _____ objects in a binary image. 
+2. An erosion in a binary image _____ the number of foreground pixels.
+3. A dilation in a grayscale image _____ the average intensity in the image.
+4. A dilation _____ objects in a binary image.
 
 
 ## Morphological opening and closing
@@ -84,6 +93,9 @@ TODO
 topHat( image ) = image - dilation( erosion( image, r), r )
 ```
 
+TODO: Add image from pdf
+
+
 ### Activity: Explore tophat filter
 
 - Open image: xy_8bit__spots_local_background.tif
@@ -125,6 +137,9 @@ TODO
 median_based_background_correction = image - median( image, r)
 ```
 
+TODO: Add image from pdf
+
+
 ### Activity: Implement median based background subtraction
 
 - Write code to implement a median based background subtraction

workshops/recap.vim

+## Recap
+
+Take few sheets of empty (A4) paper.
+Work in pairs of two. 
+
+* Draw a typical image analysis workflow: From intensity image to objects shape table.
+* Write down few (e.g., two) noteworthy facts about:
+	* Pixel data types
+        * Label images
+	* Intensity measurements
+        * Object shape measurements
+* Answer below questions:
+        * How can you split touching objects?
+        * What can you use a distance map for?
+	* What can you do to segment spots in prescence of uneven background signal?