  Thresholding is the most widely used segmentation method in volumetric image
processing, and its pointwise nature makes it attractive for the fast handling
of large three-dimensional samples. However, global thresholds often do not
properly extract components in the presence of artifacts, measurement noise or
grayscale value fluctuations. This paper introduces Feature-Adaptive
Interactive Thresholding (FAITH), a thresholding technique that incorporates
(geometric) features, local processing and interactive user input to overcome
these limitations. Given a global threshold suitable for most regions, FAITH
uses interactively selected seed voxels to identify critical regions in which
that threshold will be adapted locally on the basis of features computed from
local environments around these voxels. The combination of domain expert
knowledge and a rigorous mathematical model thus enables a very exible way of
local thresholding with intuitive user interaction. A qualitative analysis
shows that the proposed model is able to overcome limitations typically
occuring in plain thresholding while staying efficient enough to also allow the
segmentation of big volumes.
