  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.
