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To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order...
This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT) scans. The proposed methodology consists of four components: (i) elastic image registration; (ii) segmentation of the lung fields; (iii) extraction of functional and texture features; and (iv) classification of the lung tissues. The...
The segmentation of the lung tissues in chest Computed Tomography (CT) images is an important step for developing any Computer-Aided Diagnostic (CAD) system for lung cancer and other pulmonary diseases. In this paper, we introduce a new framework to generate 3D realistic synthetic phantoms to validate our developed Joint Markov-Gibbs based lung segmentation approach from CT data. Our framework is...
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