Skip to content

GitLab

I
image-analysis-training-resources
Commit a10542ed authored by M. Julius Hossain's avatar M. Julius Hossain
Browse files
Options

Merge branch 'moduleRefactoring' into 'master' 

Module refactoring

See merge request !16
parents ccbd36e1 aff8dc89
Pipeline #9801 passed with stages
      in 1 minute and 6 seconds
      Hide whitespace changes
      Inline Side-by-side
      Showing with 497 additions and 548 deletions
      modules/basic_image_analysis_workflow.md 0 → 100644
      View file @ a10542ed
      ---
      title: Basic image analysis workflow
      layout: page
      ---
      ## Typical image analysis workflow
      ![image](/uploads/b4bdce17515908f40d858b35d5e9256e/image.png)
      modules/bioimage-analysis-fundamentals.md deleted 100644 → 0
      View file @ ccbd36e1
      # Bioimage analysis fundamentals
      ## Pixel values and indices
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      image -> pixel [label=" has many"];
      pixel -> value;
      pixel -> indices;
      pixel -> voxel [label=" 3D"];
      }
      '/>
      ### Activity: Explore pixel values and indices
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Explore different ways to inspect pixel values and indices
      * Check where the lowest pixel indices are in the displayed image:
      * Most commonly: Upper left corner, which is different to conventional coordinate systems.
      TODO: add animated-histogram.gif
      ## Image calibration
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      pixel -> indices;
      pixel -> coordinates;
      indices -> calibration;
      calibration -> coordinates;
      calibration -> anisotropic [label=" can be"];
      image -> calibration [label=" can have"];
      }
      '/>
      ### Activity: Explore image calibration
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Add image calibration
      * Explore whether and how this affects image display and measurements (e.g. distance between two points)
      ### Activity: Explore anisotropic 3D image data
      * Open image: xy_8bit_calibrated_anisotropic__mri_stack.tif
      * Appreciate that the pixels are anisotropic
      ### Formative assessment
      True or false?
      * Changing the image calibration changes the pixel values.
      * Pixel coordinates depend on image calibration.
      * The lowest pixel index of a 2D image always is `[1,1]`.
      * When looking at a 2D image, the lowest pixel indices are always in the lower left corner.
      &nbsp;
      &nbsp;
      &nbsp;
      <div style="page-break-after: always;"></div>
      ## Image display
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      LUT -> color;
      LUT -> brightness;
      min -> LUT;
      max -> LUT;
      value -> LUT;
      }
      '/>
      ```
      brightness = ( value - min ) / ( max - min )
      0 <= brightness <= 1
      contrast = max - min
      ```
      TODO: add animated image contrast demo from twitter
      ### Activity
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Explore different LUTs and LUT settings
      * Appreciate that LUT settings do not affect image content.
      ### Formative Assessment
      Fill in the blanks, using those words: decrease, larger than, increase, smaller than
      1. Pixels with values _____ `max` will appear saturated.
      2. Decreasing `max` while keeping `min` constant will _____ the contrast.
      3. Decreasing both `max` and `min` will _____ the overall brightness.
      4. Pixels with values _____ the `min` will appear black, when using a grayscale LUT.
      &nbsp;
      &nbsp;
      &nbsp;
      ## Image math and pixel data types
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      "data type" -> "pixel values" [label=" restricts"];
      "image math" -> "pixel values" [label=" changes"];
      "N-bit unsigned integer" -> "0, 1, ..., 2^N-1";
      "data type" -> float -> "..., -1031.0, ..., 10.5, ...";
      "data type" -> "...";
      "data type" -> "N-bit unsigned integer";
      }
      '/>
      ### Activity: Pixel based background subtraction
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Appreciate the significant background intensity
      * Measure pixel values at `[ 28, 35 ]` and `[ 28, 39 ]`
      * Measure the image background intensity in this region:
      * upper left corner at `[ 20, 35 ]`
      * width = 10
      * height = 10
      * Subtract the measured background intensity from each pixel.
      * Measure the pixel values again.
      * Observe that the resuls are incorrect.
      Repeat above activity, but:
      * After opening the image, convert its data type to floating point.
      ### Activity: Explore the limitations of `float` data type
      * Create an empty image
      * Set all pixel values to 1000000000.0
      * Add 1.0 to all pixel values
      * Be shocked...
      ...it turns out that from 16777216 on you cannot represent all integers anymore within a float.
      ### Formative Assessment
      True or false?
      * 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
      * [Limitations of float](https://randomascii.wordpress.com/2012/02/13/dont-store-that-in-a-float/)
      &nbsp;
      &nbsp;
      &nbsp;
      ## Pixel data type conversions
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      "data type conversion" -> "values" [label=" can change"];
      "data type conversion" -> "value range" [label=" changes"];
      }
      '/>
      ### Activity: 16-bit to 8-bit conversion
      * Open image: xy_16bit__two_values.tif
      * Convert to 8-bit
      * Understand the mathematics underlying the conversion from 16-bit to 8-bit.
      ### Activity: 16-bit to float conversion
      * Open image: xy_16bit__two_values.tif
      * Convert to float
      ### Formative Assessment
      True or false? Discuss with your neighbor!
      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.
      4. There is only one correct way to convert from 16-bit to 8-bit.
      &nbsp;
      &nbsp;
      &nbsp;
      ## 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 {
      shift [fontcolor=white,color=white];
      "intensity image" -> threshold;
      threshold -> "binary image";
      "binary image" -> "mask" [label=" aka"];
      "binary image" -> "background value";
      "binary image" -> "foreground value";
      "background value" -> "0";
      "foreground value" -> "1";
      "foreground value" -> "255";
      "pixel value" -> ">= threshold" -> foreground;
      "pixel value" -> "< threshold" -> background;
      }
      '/>
      ### Activity: Threshold an image
      * Open image: xy_8bit__two_cells.tif
      * Convert the image to a binary image by means of thresholding.
      ### Formative assessment
      True or false? Discuss with your neighbour!
      * 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 always set to `1`.
      &nbsp;
      &nbsp;
      &nbsp;
      ## Connected components analysis
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      "intensity image" -> "connected component analysis" -> "label image";
      connectivity -> "connected component analysis";
      }
      '/>
      ### Activity: 2D connected components analysis
      * Open image: xy_8bit_binary__nuclei.tif
      * Perform connected components analysis
      * Explore multi-color LUTs for object labelling
      * Explore removing and joining labels
      ### Activity: 3D connected components analysis
      Repeat above activity but use a 3D image:
      * Open image: xyz_8bit_binary__spots.tif
      ### Formative assessment
      Fill in the blanks, using these words: less, more, 8, 255, 4, more.
      1. In 3D, pixels have _____ neighbors than in 2D.
      2. 8-connected connectivity results in _____ objects than 4-connected connectivity.
      3. In 3D, pixels have ____ non-diagonal neighbors.
      4. In 2D, pixels have ____ non-diagonal neighbors.
      5. A 8-bit label image can maximally have _____ objects.
      6. The maximum value in a label image is equal to or _____ than the number of objects.
      &nbsp;
      &nbsp;
      &nbsp;
      ## Shape measurements
      <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;
      table -> visualisation;
      }
      '/>
      ### Activity: Measure object shape parameters
      * Open image: xy_8bit_labels__four_objects.tif
      * Perform shape measurements and discuss their meanings.
      * 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.
      ### Activity: Explore sampling limits
      * Draw a square (=circle) of 2x2 pixels (paper, whiteboard, ...)
      * Measure area, perimeter and circularity
      * Discuss the results
      * Discuss the England's coastline paradox
      ### Formative assessment
      True or false? Discuss with your neighbour!
      * Circularity is independent of image calibration.
      * Area is independent of image calibration.
      * Perimeter can strongly depend on spatial sampling.
      * Volume can strongly depend on spatial sampling.
      * Drawing test images to check how certain shape parameters behave is a good idea.
      ### Learn more
      * Especially surface and perimeter measurements are affected by sampling and resolution, see for example:
      * https://en.wikipedia.org/wiki/Coastline_paradox).
      * Results visualisation:
      * https://imagej.net/MorphoLibJ#Grayscale_morphological_filters: **Label visualization in 3D viewer**
      &nbsp;
      &nbsp;
      &nbsp;
      ## Object shape measurement workflow
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      "intensity image" -> "binary image" [label=" threshold"];
      "binary image" -> "label image" [label=" connected components"];
      "label image" -> table [label=" measure_shape"];
      }
      '/>
      ### Activity: Segment objects and measure shapes
      * Open image: xy_8bit__two_cells.tif
      * Segment the cells and measure their shapes.
      * Devise code to automate the workflow.
      ### Formative assessment
      Fill in below blanks, using these words: equal_to, larger_than, smaller_than, binary, connected_component_analysis, thresholding
      1. A label image is the result of _____ .
      2. The number of pixels in a binary image is typically _____ the number of connected components.
      3. The number of distinct values in a label image is _____ the number of objects (minus one).
      4. Converting an intensity image to a _____ image can be achieved by _____ .
      5. The number of connected components can be _____ the maximal label.
      &nbsp;
      &nbsp;
      &nbsp;
      ## Intensity measurements
      ### Activity: Measure intensities in image regions
      * Open image: xy_float__h2b_bg_corr.tif
      * Measure for both nuclei:
      * Maximum intensity
      * Average intensity
      * Median intensity
      * Sum intensity
      * Discuss the interpretation!
      * Discuss where to measure!
      ### Activity: Intensity measurements without pixel based background correction
      #### Motivation
      There are several good reasons not to subtract the background from each pixel in an image:
      * It is a bit tricky to do it right, because one has to convert to float to accomodate floting point and negative values.
      * If one has really big image data (TB) one would need (at least) another TB storage for the background corrected version of the image.
      #### Workflow
      * Open image: xy_calibrated_8bit__two_nuclei_high_background.tif
      * Measure for both nuclei and a background region:
      * Maximum intensity
      * Average intensity
      * Median intensity
      * Sum intensity
      * Discuss how to correct the intensities for the background
      * Appreciate that you also need the region areas for this task
      * Measure the region areas
      * Watch out: the image is calibrated!
      * Use the area for the correction.
      ### Formative assessment: Intensity measurements
      Fill in the blanks, using these words: integrated, mean, number_of_pixels, decrease, increase, sum
      1. Average intensity is just another word for _____ intensity.
      2. The _____ intensity is equal to the mean intensity times the _____ in the measured region.
      3. In an 8-bit image, increasing the size of the measurement region can only _____ the sum intensity.
      4. In a float image, increasing the size of the measurement region can _____ the sum intensity.
      &nbsp;
      &nbsp;
      &nbsp;
      ## Convolution filters
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      "intensity image" -> "convolution" -> "filtered image";
      "small image" -> size;
      "small image" -> "pixel values";
      "kernel" -> "small image" [label=" is"];
      "kernel" -> "convolution";
      }
      '/>
      ### Activity: Explore convolution filters
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Try the result of different convolution filters, e.g.
      * https://en.wikipedia.org/wiki/Kernel_(image_processing)
      * Mean filter
      * Gaussian blur
      * Edge detection
      * Appreciate that the results are (slightly) wrong within the 8-bit range of the input image.
      ### Activity: Use mean filter to facilitate image segmentation
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Appreciate that you cannot readily threshold the image
      * Apply a mean filter
      * Threshold the filtered image
      ### Formative assessment
      * Draw the kernel of a 3x3 mean filter.
      * Draw three different kernels that enhance edges.
      ### Learn more
      * https://en.wikipedia.org/wiki/Kernel_(image_processing)
      &nbsp;
      &nbsp;
      &nbsp;
      ## Typical image analysis workflow
      ![image](/uploads/b4bdce17515908f40d858b35d5e9256e/image.png)
      &nbsp;
      &nbsp;
      &nbsp;
      modules/calibration.md 0 → 100644
      View file @ a10542ed
      ---
      title: Image calibration
      layout: page
      ---
      ## Image calibration
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      pixel -> indices;
      pixel -> coordinates;
      indices -> calibration;
      calibration -> coordinates;
      calibration -> anisotropic [label=" can be"];
      image -> calibration [label=" can have"];
      }
      '/>
      ### Activity: Explore image calibration
      * Open image: xy_8bit__nuclei_noisy_different_intensity.tif
      * Add image calibration
      * Explore whether and how this affects image display and measurements (e.g. distance between two points)
      ### Activity: Explore anisotropic 3D image data
      * Open image: xy_8bit_calibrated_anisotropic__mri_stack.tif
      * Appreciate that the pixels are anisotropic
      ### Formative assessment
      True or false?
      * Changing the image calibration changes the pixel values.
      * Pixel coordinates depend on image calibration.
      modules/connected_components.md 0 → 100644
      View file @ a10542ed
      ---
      title: Connected components
      layout: page
      ---
      ## Connected components analysis
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      "intensity image" -> "connected component analysis" -> "label image";
      connectivity -> "connected component analysis";
      }
      '/>
      ### Activity: 2D connected components analysis
      * Open image: xy_8bit_binary__nuclei.tif
      * Perform connected components analysis
      * Explore multi-color LUTs for object labelling
      * Explore removing and joining labels
      ### Activity: 3D connected components analysis
      Repeat above activity but use a 3D image:
      * Open image: xyz_8bit_binary__spots.tif
      ### Formative assessment
      Fill in the blanks, using these words: less, more, 8, 255, 4, more.
      1. In 3D, pixels have _____ neighbors than in 2D.
      2. 8-connected connectivity results in _____ objects than 4-connected connectivity.
      3. In 3D, pixels have ____ non-diagonal neighbors.
      4. In 2D, pixels have ____ non-diagonal neighbors.
      5. A 8-bit label image can maximally have _____ objects.
      6. The maximum value in a label image is equal to or _____ than the number of objects.
      ## Learn next
      - shape_measurements.md
      modules/course-preamble.md deleted 100644 → 0
      View file @ ccbd36e1
      # 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.
      modules/display.md 0 → 100644
      View file @ a10542ed
      ---
      title: Image display
      layout: page
      ---
      # Image display
      <img src='https://g.gravizo.com/svg?
      digraph G {
      shift [fontcolor=white,color=white];
      LUT -> color;
      LUT -> brightness;