A new approach to segment pleurae from CT data with high precision is introduced. This approach is developed in the
segmentation's framework of an image analysis system to automatically detect pleural thickenings. The new technique to
carry out the 3D segmentation of lung pleura is based on supervised range-constrained thresholding and a Gibbs-Markov
random field model. An initial segmentation is done using the 3D histogram by supervised range-constrained
thresholding. 3D connected component labelling is then applied to find the thorax. In order to detect and remove trachea
and bronchi therein, the 3D histogram of connected pulmonary organs is modelled as a finite mixture of Gaussian
distributions. Parameters are estimated using the Expectation-Maximization algorithm, which leads to the classification
of that pulmonary region. As consequence left and right lungs are separated. Finally we apply a Gibbs-Markov random
field model to our initial segmentation in order to achieve a high accuracy segmentation of lung pleura. The Gibbs-
Markov random field is combined with maximum a posteriori estimation to estimate optimal pleural contours. With
these procedures, a new segmentation strategy is developed in order to improve the reliability and accuracy of the
detection of pleural contours and to achieve a better assessment performance of pleural thickenings.
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