  Region extraction is necessary in a wide range of applications, from object
detection in autonomous driving to analysis of subcellular morphology in cell
biology. There exist two main approaches: convex hull extraction, for which
exact and efficient algorithms exist and concave hulls, which are better at
capturing real-world shapes but do not have a single solution. Especially in
the context of a uniform grid, concave hull algorithms are largely approximate,
sacrificing region integrity for spatial and temporal efficiency. In this
study, we present a novel algorithm that can provide vertex-minimized concave
hulls with maximal (i.e. pixel-perfect) resolution and is tunable for
speed-efficiency tradeoffs. Our method provides advantages in multiple
downstream applications including data compression, retrieval, visualization,
and analysis. To demonstrate the practical utility of our approach, we focus on
image compression. We demonstrate significant improvements through
context-dependent compression on disparate regions within a single image
(entropy encoding for noisy and predictive encoding for the structured
regions). We show that these improvements range from biomedical images to
natural images. Beyond image compression, our algorithm can be applied more
broadly to aid in a wide range of practical applications for data retrieval,
visualization, and analysis.
