A Physical Model for Microstructural Characterization and Segmentation of 3D Tomography Data

TL;DR

This paper introduces a statistical model for analyzing 3D tomography data that quantifies microstructural parameters directly from raw images, avoiding segmentation errors and enabling automated, physics-based analysis.

Contribution

The novel model allows direct parameter estimation from unsegmented data and integrates with existing segmentation methods for improved accuracy.

Findings

Model accurately quantifies volume fractions and interface areas.

Automated fitting procedure produces reproducible results.

Reduces segmentation errors by accounting for imaging blurring.

Abstract

We present a novel method for characterizing the microstructure of a material from volumetric datasets such as 3D image data from computed tomography (CT). The method is based on a new statistical model for the distribution of voxel intensities and gradient magnitudes, incorporating prior knowledge about the physical nature of the imaging process. It allows for direct quantification of parameters of the imaged sample like volume fractions, interface areas and material density, and parameters related to the imaging process like image resolution and noise levels. Existing methods for characterization from 3D images often require segmentation of the data, a procedure where each voxel is labeled according to the best guess of which material it represents. Through our approach, the segmentation step is circumvented so that errors and computational costs related to this part of the image processing pipeline are avoided. Instead, the material parameters are quantified through their known relation to parameters of our model which is fitted directly to the raw, unsegmented data. We present an automated model fitting procedure that gives reproducible results without human bias and enables automatic analysis of large sets of tomograms. For more complex structure analysis questions, a segmentation is still beneficial. We show that our model can be used as input to existing probabilistic methods, providing a segmentation that is based on the physics of the imaged sample. Because our model accounts for mixed-material voxels stemming from blurring inherent to the imaging technique, we reduce the errors that other methods can create at interfaces between materials.