Pattern recognition in pulmonary computerized tomography images using Markovian modeling

摘要

Abstract: The authors propose a nonstationary Markovian model with deterministic relaxation for segmenting the hyper-attenuated areas in pulmonary computerized tomography. Their contribution lies in the definition of a local energy as the weighted combination of four components: density function, the Geman-Graffigne gradient function, the local maxima function concerning cliques of order one and the attraction-repulsion function as an Ising model dealing with cliques of order two. This potential is deduced from pre-processing and a priori knowledge. Spatial interactions are modeled on a hexagonal lattice. The 6-connectivity neighborhood system is defined by morphological dilations. An important aspect of this model is that it considers, in addition to the two classes normally used (hype-rattenuated and non- hyper-attenuated), a third class for non-identifiable pixels. Results of this automatic segmentation perfectly match the areas interactively selected by the radiologists.!