In diagnosing lung diseases, it is highly desirable to be able to segment the lung into physiological structures, such as the intra-thoracic airway tree and the pulmonary structure. Providing an in-vivo and non-invasive tool for 3D reconstruction of anatomical tree structures such as the bronchial tree from 2D and 3D data acquisitions is a challenging issue for computer vision in medical imaging. Due to the complexity of the tracheobronchial tree, the segmentation task is non trivial. This paper describes a 3D adaptive region growing algorithm incorporating gain calculation for segmenting the primary airway tree using a stack of 2D CT slices. The algorithm uses an entropy-based measure known as information gain as a heuristic for selecting the voxels that are most likely to represent the airway regions. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Singh, H., Crawford, M., Curtin, J., & Zwiggelaar, R. (2004). Automated 3D segmentation of the lung airway tree using gain-based region growing approach. In Lecture Notes in Computer Science (Vol. 3217, pp. 975–982). Springer Verlag. https://doi.org/10.1007/978-3-540-30136-3_118
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