Grain Sizing application examples
Automatic study of Ceramics material
Development of a vertical application for the study of ceramics
ADCIS and the LERMAT, a research laboratory specialized in Material Science jointly developed automatic tools to analyze ceramics using Image Processing techniques. During the course of this 2 year project, two types of ceramics were analyzed, a ceric oxide, used for sintered analysis, and alumine-zircone, a two phase material used for grain detection and analysis.
A scanning electron microscope was used to acquire black and white, very high resolution images. Some filtering operators were applied to the images to enhance their quality by removing visible noise. Segmentation of the images involved automatic thresholding techniques to detect grains and pores, and various morphological operators such as top-hat, skeleton by zone of influence, watershed with constrained markers, and other functions looking at the size distribution of particles.
The images below present an example of image processing algorithms applied to a ceric oxide sintered at a temperature of 1200 degrees during a 5 hour process.
The research work also involved an in-depth analysis of the sample to measure characteristics such as granulometry, evolution of the volumic fraction versus the temperature, kinetics laws, pore dispersion, etc. The following chart displays the evolution of the granulometry.
Finally, some probabilistic models were studied to determine a model for the micro-structure.
This comprehensive work was performed within the scope of a Ph.D. thesis, and was partially funded by an ITIC grant from the Normandy region in France.
All image processing and analysis functions were developed using Aphelion™ and are now available as a set of macros written in Basic Script.
Grain boundary detection
Grain boundary detection example
This software solves a common problem in materials science, namely the detection of grain boundaries. For example, ASTM standards refer to this detection.
The user interface appears as a combination of several windows with buttons calling some functions of image processing and mathematical morphology. The judicious combination of these features can automatically detect the grains and their boundaries in calling functions segmentation and analysis of size and shape. The user has very few parameters to be specified before the launch detection and the output image of the algorithm contains boundaries thinned and thick one pixel.
This software is absolutely expectations metallurgists who wish to automate the detection of grain material and quantify the number of phases and inclusions in steel. The software is based on a stand-alone program that takes an image in TIFF format as input and generates an image at the same output format. All measurements are exported to a file in Microsoft Excel format. The demo version is an executable Visual Basic.
The advantages of the detection software grain boundaries are:
- Standalone software can be used from any Windows PC
- Solves the problem non-trivial detection prior to any extent ASTM
- No learning curve - Can be used by non expert technician in image processing
- Automatic 99%. Very few parameters must be specified by the user
- Fully compatible with all hardware supported by the acquisition of Aphelion™, as an optical microscope or digital microscope
- Outputs a binary image and measures exported into a spreadsheet
The algorithm includes edge detection, two types of transformations Top Hat Form to solve the problem of non-uniform illumination, an edge detection, filtering, operations erosion and dilation to clean contours, a line algorithm Watershed and skeleton by influence zone.
Extract volume fraction of zircone grains in aluminum
Below is an application that was developed by an Aphelion™ users in the field of Material Science. The image is courtesy of ESRF Grenoble, INSA de Lyon, GEMPPM, and Ecole des Mines de Paris.
The goal of the application is to extract volume fraction of zircone grains in aluminum, to compute grain sizing and determine neighbor distribution, working on the 3D volume.
In the past, most of the 3D analysis were performed using the set of 2D sections of a 3D volume. Nowadays, thanks the processing power of computers, and the quality of sensors, it is possible to deal directly with 3D images.
Below is the description of a very innovative technique based on 3D Morphology and 3D Image Understanding to compute grain sizing and neighbor distribution, two analysis which can only be performed on the 3D data.
- The image is acquired using a X-Ray micro-tomograph with an advanced sensor.
- The computation of the mean value of the binary image gives the zircone volumic fraction.
- Since the contrast between the two phases is good, a simple threshold is applied to extract the zircone phase.
- Since zircone particles are almost spherical, they all appear in the image as a stack of
spheres touching each other. The use of the ClustersSplitConvex operator will help to segment
all the spheres which are actually convex particles. The operator is based on the 3D
implementation of the watershed, a morphological operator.
3D label displayed as a volume
3D label displayed as 3 orthogonal sections
- To avoid biased measurements, a BorderKill operator is applied to remove all particles intersecting the edge of the image. A Miles-Lantuéjoul correction could also be applied to actually get unbiased measurements taking into account the size of the volume, and the operators involved in the process.
- The binary image is now converted into a 3D ObjectSet based on the 26-Connectivity. Note
that cubic and cubic centered face grids are supported in Aphelion 3D. All spheres are now
perfectly individualized. A set of measurements based on the shape are computed, such as the
sphericity, and the intercept numbers in the main directions of the grid. The size
distribution of the particles is computed and displayed as an histogram, as shown in the
- The last step of the application involves the computation of the number of neighbors for each zircone spherical grain. This number can only be accessed from the 3D data, using the notion of 3D ObjectSets. The classical method is based on the extraction of each sphere in the volume. Each sphere is dilated, and then intersected with the binary image. A geodesic reconstruction is then performed, and the number of components is computed. It has to be done for each grain, and it takes several minutes to run. We are proposing to use another technique, much faster, and based on the Aphelion ObjectSets.
With the help of ObjectSets, the computation is no longer performed on pixels, but on 3D objects. Since objects are already individualized in an ObjectSet, and available as rasters, we perform a dilation on the Objects, with the condition that grains remain individualized even if they overlap after the dilation. The final result gives the number of overlaps for each grain, and is displayed in the standard Aphelion grid as a new attribute. This computation is very quickly performed since no pixel information is required, and it really proves Aphelion has superior capability than any other software when dealing with 3D images.
The following chart gives the neighbor distribution for the Zircone image, and the grid shows the value of the neighbor attribute. As in the 2D version of Aphelion, message passing is available between the grid and the chart.