Commit a49e22ba authored by Christian Tischer's avatar Christian Tischer

EMBL course

parent 0b9e73ae
......@@ -30,7 +30,7 @@
### Formative assessment
Which statements about images are true (multiple answers)?
True or false?
- Pixel coordinates are always integer values.
- Changing the image calibration changes the pixel values.
......@@ -47,9 +47,9 @@ Which statements about images are true (multiple answers)?
shift [fontcolor=white,color=white];
lookup_table -> color;
lookup_table -> brightness;
lookup_table <- LUT_min;
lookup_table <- LUT_max;
lookup_table <- pixel_value;
LUT_min -> lookup_table;
LUT_max -> lookup_table;
pixel_value -> lookup_table;
}
'/>
......@@ -68,6 +68,15 @@ contrast = LUT_max - LUT_min
### Formative Assessment
Fill in the blanks:
decrease, larger_than, increase, smaller_than
- Pixels with values _____ the LUT_max will appear saturated.
- Decreasing the LUT_max while keeping the LUT_min constant will _____ the contrast.
- Decreasing both LUT_max and LUT_min will _____ the overall brightness.
- Pixels with values _____ the LUT_min will appear black, when using a grayscale LUT.
## Image math and pixel data types
......@@ -76,13 +85,13 @@ contrast = LUT_max - LUT_min
shift [fontcolor=white,color=white];
image_math -> pixel_values [label=" changes"];
image_math -> pixel_data_type [label=" does not change"];
pixel_data_type -> _8_bit_unsigned_integer
_8_bit_unsigned_integer -> _0_255
_16_bit_unsigned_integer -> _0_65535
_N_bit_unsigned_integer -> _0_2powerN_minus1
pixel_data_type -> _16_bit_unsigned_integer
pixel_data_type -> _32_bit_float
image_math -> wrong_pixel_values [label = " can yield"]
pixel_data_type -> _8_bit_unsigned_integer;
_8_bit_unsigned_integer -> _0_255;
_16_bit_unsigned_integer -> _0_65535;
_N_bit_unsigned_integer -> _0_2powerN_minus1;
pixel_data_type -> _16_bit_unsigned_integer;
pixel_data_type -> _32_bit_float;
image_math -> wrong_pixel_values [label = " can yield"];
}
'/>
......@@ -128,14 +137,13 @@ Repeat above activity, but:
### Formative Assessment
Regarding image math operations, which of below statements are correct
(multiple answers)?
True or false?
- Subtracting 100 from a 8-bit pixel of value 50 will result in -50.
- Adding 1 to a 8-bit pixel of value 255 will result in 256.
- Subtracting 10.1 from a floating point pixel with value 10.0 will result in -0.1
- Adding 1.0 to a floating point pixel of value 255.0 will result in 256.0
- Adding 1000.0 to a floatin point pixel of value 1000000000.0 will result in 1000001000.0
- Subtracting 100 from 50 in a 8-bit image will result in -50.
- Adding 1 to 255 in a 8-bit image will result in 256.
- Subtracting 10.1 from 10.0 in a float image will result in -0.1
- Adding 1.0 to 255.0 in a float image will result in 256.0
- Adding 1000.0 to 1000000000.0 in a float image will result in 1000001000.0
### Learn more
......@@ -147,7 +155,7 @@ Regarding image math operations, which of below statements are correct
digraph G {
shift [fontcolor=white,color=white];
pixel_type_conversion -> pixel_values [label=" can change"];
pixel_type_conversion -> pixel_value_range [label" changes"];
pixel_type_conversion -> pixel_value_range [label=" changes"];
}
'/>
......@@ -172,12 +180,12 @@ What are good reasons to change the pixel data type of an image?
### Formative Assessment
Which statements are true?
True or false?
1. Changing pixel data type never changes pixel values.
2. Converting from 16-bit unsigned integer to float never changes the pixel values.
3. Changing from float to 16-bit unsigned integer never changes the pixel values.
1. Changing the pixel data type does not change pixel values.
2. Changing from 16-bit unsigned integer to float never changes the pixel values.
3. Chaning from float to 16-bit unsigned integer never changes the pixel values.
## Image segmentation overview
......@@ -187,18 +195,18 @@ Which statements are true?
shift [fontcolor=white,color=white];
intensity_image -> binary_image -> label_image;
binary_image <- background_value;
background_value <- 0;
foreground_value <- 1;
foreground_value <- 255;
binary_image <- foreground_value;
label_image <- object_indices;
_0_ -> background_value;
_1_ -> foreground_value;
_255_ -> foreground_value;
foreground_value -> binary_image;
object_indices -> label_image;
}
'/>
## Thresholding
In order to find objects in a image, the first step is to determine whether a pixel is part of an object or of the image background. In fluorescence microscopy this often can be done by thresholding.
In order to find objects in a image, the first step often is to determine whether a pixel is part of an object (foreground) or of the image background. In fluorescence microscopy this often can be achieved by thresholding.
<img src='https://g.gravizo.com/svg?
digraph G {
......@@ -218,14 +226,12 @@ In order to find objects in a image, the first step is to determine whether a pi
## Formative assessment
Which statements are true?
True or false? Discuss with your neighbor!
- For each image there is only one correct threshold value.
- The result of thresholding is a binary image.
- A binary image can have three values: -1,0,+1
- Values below the threshold are set to 1.
- Values below the threshold are always set to 1.
## Connected components analysis
......@@ -233,7 +239,7 @@ Which statements are true?
digraph G {
shift [fontcolor=white,color=white];
intensity_image -> connected_component_analysis -> label_image;
connected_component_analysis <- connectivity
connectivity -> connected_component_analysis;
}
'/>
......@@ -254,17 +260,17 @@ Repeat above activity but use a 3D image:
### Formative assessment
Which statements are true?
Fill in the blanks:
less, more, 8, 255, 4, more.
- For a given input image there is only one correct connectivity.
- Choosing the connectivity can affect the result of conected components analysis.
- 8-connected connectivity results in more objects than 4-connected connectivity.
- Label images have two values: background and foreground.
- Label images are always 8-bit.
- Label images can suffer from limitations of pixel data type.
- The pixel value for background in label images typically is 0.
- The maximum value in an label image often corresponds to the number of objects.
- The maximum value in an label image always corresponds to the number of objects.
- In 3D, pixels have _____ neighbors than in 2D.
- 8-connected connectivity results in _____ objects than 4-connected connectivity.
- In 3D, pixels have ____ non-diagonal neighbors.
- In 2D, pixels have ____ non-diagonal neighbors.
- A 8-bit label image can maximally have _____ objects.
- The maximum value in a label image is equal to or _____ than the number of objects.
## Shape measurements
......@@ -273,8 +279,8 @@ Which statements are true?
digraph G {
shift [fontcolor=white,color=white];
label_image -> shape_analysis -> table;
rows__objects -> table;
columns__features -> table;
object_rows -> table;
feature_columns -> table;
}
'/>
......@@ -290,7 +296,7 @@ Which statements are true?
### Formative assessment
Which statements are true?
Which statements are true? Discuss with your neighbor!
- Circularity is independent of image calibration.
- Area is independent of image calibration.
......@@ -388,3 +394,17 @@ Which statements are true?
## Recap
Discuss with your neighbor!
- 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
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