1 July 1991 Pattern recognition in pulmonary computerized tomography images using Markovian modeling
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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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Francoise J. Preteux, Michel Moubarak, Philippe Grenier, "Pattern recognition in pulmonary computerized tomography images using Markovian modeling", Proc. SPIE 1450, Biomedical Image Processing II, (1 July 1991); doi: 10.1117/12.44286; https://doi.org/10.1117/12.44286

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