  Advances in the image-based diagnostics of complex biological and
manufacturing processes have brought unsupervised image segmentation to the
forefront of enabling automated, on the fly decision making. However, most
existing unsupervised segmentation approaches are either computationally
complex or require manual parameter selection (e.g., flow capacities in
max-flow/min-cut segmentation). In this work, we present a fully unsupervised
segmentation approach using a continuous max-flow formulation over the image
domain while optimally estimating the flow parameters from the image
characteristics. More specifically, we show that the maximum a posteriori
estimate of the image labels can be formulated as a continuous max-flow problem
given the flow capacities are known. The flow capacities are then iteratively
obtained by employing a novel Markov random field prior over the image domain.
We present theoretical results to establish the posterior consistency of the
flow capacities. We compare the performance of our approach on two real-world
case studies including brain tumor image segmentation and defect identification
in additively manufactured components using electron microscopic images.
Comparative results with several state-of-the-art supervised as well as
unsupervised methods suggest that the present method performs statistically
similar to the supervised methods, but results in more than 90% improvement in
the Dice score when compared to the state-of-the-art unsupervised methods.
