Identification and segmentation of structures of interest are necessary steps in the computer-based analysis of medical images. Computer-aided diagnostic (CAD) systems utilize segmentation algorithms to isolate specific structures (represented by 2D or 3D regions in an image or set of images, respectively); conversely, to remove extraneous structures that may introduce errors in the computerized analysis. This step increases both the specificity and sensitivity of the CAD system and decreases computation time by focusing analysis on smaller regions representing the structures of interest. Segmentation of the lung parenchyma is often the first step when computerized analysis focuses on the thorax. High contrast, central positioning, relatively large size in comparison to other thoracic structures, and contiguous to other critical structures (e.g., heart) render the lungs useful as both a target for primary analysis and a reliable starting point for the analysis of other thoracic structures.
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Sensakovic, W.F., Armato, S.G. (2008). Magnetic Resonance Imaging of the Lung: Automated Segmentation Methods. In: Hayat, M.A. (eds) General Methods and Overviews, Lung Carcinoma and Prostate Carcinoma. Methods of Cancer Diagnosis, Therapy, and Prognosis, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8442-3_14
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