Image segmentation for the demarcation of pulmonary nodules in CT images is intrinsically an arduous task. The
difficulty can be summarized into two aspects. Firstly, lung tumor can be various in terms of physical densities in
pulmonary regions, implying the different interpretation as a mixture of GGO and solid nodules. Hence, processing of
lung CT images may generally encounter tissue inhomogeneous problem. The second factor that complicates the task of
nodule demarcation is the irregular shapes that most nodules are directly connected to other structures sharing the similar
density profile. In this paper, an image segmentation framework is proposed by unifying the techniques of statistical
region merging and conditional random field (CRF) with graph cut optimization to address the difficult problem of GGO
nodules quantification in CT images. Different from traditional segmentation methods that use pixel-based approach
such as region growing and morphological constraints, we employ a hierarchical segmentation tree to alleviate the effect
of inhomogeneous attenuation. In addition to building perceptual prominent regions, we perform inference in CRF model
based on restricting the pool of segmented regions. Following that, an inference CRF model is carried out to detect and
localize individual object instances in CT images. The proposed algorithm is evaluated with four sets of manual
delineations on 77 lung CT images. Incorporating with the efficiency and accuracy of pulmonary nodules segmentation
method proposed in this paper, a computer aided system is hence feasible to develop related clinical application.
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