Improved Workflow for Unsupervised Multiphase Image Segmentation

TL;DR

This paper introduces an improved image segmentation workflow that effectively handles ambiguous transition regions and noise in multiphase images, enhancing accuracy in classifying complex systems like moist granular media.

Contribution

The paper presents a novel workflow with three methodologies for identifying and classifying transition pixels, significantly improving segmentation accuracy in noisy multiphase images.

Findings

Misclassification errors range from 0.69-1.48%.

Area differences between true and segmented images are 0.01-0.74%.

Effective segmentation demonstrated on x-ray microtomography images.

Abstract

Quantitative image analysis often depends on accurate classification of pixels through a segmentation process. However, imaging artifacts such as the partial volume effect and sensor noise complicate the classification process. These effects increase the pixel intensity variance of each constituent class, causing intensities from one class to overlap with another. This increased variance makes threshold based segmentation methods insufficient due to ambiguous overlap regions in the pixel intensity distributions. The class ambiguity becomes even more complex for systems with more than two constituents, such as unsaturated moist granular media. In this paper, we propose an image processing workflow that improves segmentation accuracy for multiphase systems. First, the ambiguous transition regions between classes are identified and removed, which allows for global thresholding of single-class regions. Then the transition regions are classified using a distance function, and finally both segmentations are combined into one classified image. This workflow includes three methodologies for identifying transition pixels and we demonstrate on a variety of synthetic images that these approaches are able to accurately separate the ambiguous transition pixels from the single-class regions. For situations with typical amounts of image noise, misclassification errors and area differences calculated between each class of the synthetic images and the resultant segmented images range from 0.69-1.48% and 0.01-0.74%, respectively, showing the segmentation accuracy of this approach. We demonstrate that we are able to accurately segment x-ray microtomography images of moist granular media using these computationally efficient methodologies.