A Bayesian approach incorporating Rissanen complexity for learning Markov random field texture models

Nonparametric Markov random field (MRF) texture modeling for the purpose of segmenting electron-microscope autoradiography (EMA) images is discussed. A Bayesian approach is assumed for addressing the basic problem of learning which model among a number of nonparametric MRF models best represents an observed texture. Nonparametric MRF models are inherently quite complex, prompting inclusion of a complexity measure within the Bayesian framework. The measure adopted is the Rissanen complexity, which quite naturally incorporates into the Bayesian analysis. The new Bayesian measure referred to as the minimum description length (MDL) then allows learning the conditional probabilities for the nonparametric MRF texture models of the mitochondria and background regions of the EMA image. Experiments show the results of segmenting an EMA image using these models