A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation
This work addresses a critical bottleneck in interactive image segmentation for biomedical imaging, offering a more robust and efficient method, though it is incremental as it builds on existing random walker algorithms.
The authors tackled the problem of parameter sensitivity in random walker image segmentation by proposing a general framework for deriving weight functions based on probabilistic modeling, which eliminates the need for parameter tuning and shows superior performance on synthetic and biomedical image data.
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.