  One well established method of interactive image segmentation is the random
walker algorithm. Considerable research on this family of segmentation methods
has been continuously conducted in recent years with numerous applications.
These methods are common in using a simple Gaussian weight function which
depends on a parameter that strongly influences the segmentation performance.
In this work we propose a general framework of deriving weight functions based
on probabilistic modeling. This framework can be concretized to cope with
virtually any well-defined noise model. It eliminates the critical parameter
and thus avoids time-consuming parameter search. We derive the specific weight
functions for common noise types and show their superior performance on
synthetic data as well as different biomedical image data (MRI images from the
NYU fastMRI dataset, larvae images acquired with the FIM technique). Our
framework can also be used in multiple other applications, e.g., the graph cut
algorithm and its extensions.
