Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context,
the present paper proposes an unsupervised and graph-based method of image segmentation, which is
driven by an application goal, namely, the generation of image segments associated with a user-defined
and application-specific goal. A graph, together with a random grid of source elements, is defined on
top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation
algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring
the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical
modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized
through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven
segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not
require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven
predicate and a suited parametric model for the data. In the experimental validation with both magnetic
resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated
product
