A Belief Propagation algorithm for bias field estimation and image segmentation
Rui Huang, Nong Sang, Vladimir Pavlović, Dimitris Metaxas · 2011
Intensity-based image segmentation is often plagued by the spatial intensity inhomogeneities (or non-uniformities) that are caused by the imperfection of the imaging devices and the varying operating conditions, also known as the bias field. We present a graphical model representation of the joint segmentation and bias field estimation problem and propose an iterative solver based on the Belief Propagation (BP) algorithm. The intractable joint inference problem of the original graphical model is decoupled into two MRF-MAP estimation problems and solved by a discrete-valued BP and a Gaussian BP, respectively and iteratively. We validate our method using both simulated and real data and show its connection to some of the classical filtering-based approaches.