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Proceedings Paper

Pattern recognition in pulmonary computerized tomography images using Markovian modeling
Author(s): Francoise J. Preteux; Michel Moubarak; Philippe Grenier
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Paper 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.

Paper Details

Date Published: 1 July 1991
PDF: 12 pages
Proc. SPIE 1450, Biomedical Image Processing II, (1 July 1991); doi: 10.1117/12.44286
Show Author Affiliations
Francoise J. Preteux, Telecom Paris (France)
Michel Moubarak, Univ. of Ottawa (Canada)
Philippe Grenier, Hopital Salpetriere (France)


Published in SPIE Proceedings Vol. 1450:
Biomedical Image Processing II
Alan Conrad Bovik; Vyvyan Howard, Editor(s)

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