Procedure for Particle Size and Shape Analysis from Optical Microscopy 2015-12-29

Procedure for Particle Size and Shape
Analysis from Optical Microscope Images
Blaise Mibeck
The size distribution and shape parameters of mineral grains (ideally quartz) are determined from
petrographic thin sections using optical microscopy. This data is used in the interpretation of
depositional environment. Grain size, kurtosis, skew and particle shape parameters such as roundness,
circularity can be indicators of high or low energy environments. Transitional environments can be also
be identified separated using these metrics.
These procedures apply to standard petrographic thin sections of 30 to 35um thickness of sand stone.
Summary of Method
The following steps are then performed on each image acquired:
Using a random fast-forest classifier each image is separated into three classes
A binary image of the grain class is created.
Water shed function is applied to the binary image.
Size and shape of each grain is measured automatically.
Statistical properties of the resulting distributions are then calculated.
Quality Control
Quality Control measures such as calibration of the microscope and camera settings are detailed
elsewhere. For this procedure QC mainly involves manually evaluating some image segmentation for
Segmentation: A division of the pixels of an image into disjoint groups called classes.
Ground Truth: In machine learning, the term "ground truth" refers to the accuracy of the training set's
classification for supervised learning techniques.
Interactive segmentation: Regions based on user constraints are modeled, the resulting hypothesis is
used by the segmentation algorithm to obtain a discrete labeling.
Training: When the user iteratively corrects the segmentation by providing additional constraints (tags).
Tags: Regions that are identified and marked by the user to be of a specific phase.
The procedure has three steps:
Train the Classifier.
Run the classifier on Images in Batch Mode
Process the segmented Images in ImageJ
Report Grain Size Distribution using GradStat.
Training the Classifier (using Ilastik):
1. Open the program Ilastik.
2. Create New Project  Pixel Classification. (figure 1)
3. Input Data  Add File… : navigate and select two or three images from your set for training the
classifyer. (figures 2 and 3)
4. Feature Selection  Select Features: Select all features (Color/Intensity, Edge and Texture) at all
scales (0.3 to 10). (figure 4)
5. Training  Add Label: Create three Labels. (figure 5)
6. Name each label: Pores, Grains and Other, by double clicking on the Label name and typing the
new name. (figure 5)
7. Using the brush to tag the image according to these three classes. (figure 6)
8. When roughly equal amounts of each class are tagged, click on Live update. (figure 7)
9. Under Group Visibility choose uncertainty to determine areas that need more tags, or turn
on/off each class determine misidentified pixels. (figures 8 and 9)
10. Continue to tag pixels until the majority of the three images are properly classified. In other
words when the uncertainty is low or confined only to edges. (figures 10 and 11)
11. When Training is finished turn off Live Update.
12. Save the Project.
Processing new images in batch mode (using Ilastik).
1. Prediction Export Source  Probability Maps.
2. Prediction Export  Choose Export Image Settings…
3. Image Export Options:
a. Choose Convert to unsigned 8-bit
b. Renormalize from 0 -1 to 0-255
c. Output File Format Info: Format set to tif
d. File  select and navigate to the location you would like to save the segmentations.
e. Click OK
4. Batch Processing  Select Raw Data Files… navigate and select files for processing
5. Batch Processing  Process All Files
Processing the segmented images (using ImageJ):
1. Open ImageJ.
2. Run the Macro using the Batch Macro function.
3. The macro performs the following steps:
a. Read file name and scale the image according to the magnification that is displayed in
the file name.
b. Split the color channels.
c. Select the green channel
d. Gaussian Smooth with a sigma of 2
e. Use Otsu method to threshold the image.
f. Repeat Gaussian Smooth with a sigma of 2
g. Repeat Otsu method to threshold the image
h. Run Watershed Irregular Features using an erosion of 1 and convexity threshold of 0.95
i. Analyze Particles, ignoring particles on the edges, and generating a mask.
4. When the macro finishes, save the Results and Summary Files.
Report Grain Size Distribution using Excel
Open Results file (in .csv format) in Excel.
Calculate the Equivalent Circular Diameter (ECD) from Area
Calculate Phi from ECD
Calculate Phi Rounded modulo 0.25.
Use a pivot table to sort data by Phi Round and display in terms of ECD and Area
Raw Images are .tif formatted RGB color images (8-bit per channel). The file name is in the following
format: <StarNumber>_<Objective Magnification>x_xy<Scene>.tif
The Ilastic Project file has the extension .ilp.
ImageJ result and summary files are in the .csv (comma separated values) format.
The final Report for each sample is an Excel file formatted by using the GradiStat version 8 Spreadsheet
developed by Simon Blott.
Figure 1Creating a new project in Ilastik.
Figure 2 Adding data (images) to the project for training.
Figure 3Open images are listed under the Raw Data tab.
Figure 4
Figure 5
Figure 6 Tags for each class are drawn on the image.
Figure 7 Live update causes the classifier to be trained on the current tags.
Figure 8 Probability map after initial training.
Figure 9 Segmentation after initial training.
Figure 10 Uncertainty in light blue.
Figure 11Notice uncertainty decrease after adding one tag.
Figure 12
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