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14 March 2011 Boundary detection by linear programming with application to lung fields segmentation
Bulat Ibragimov; Boštjan Likar; Franjo Pernuš
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796231 (2011) https://doi.org/10.1117/12.877705
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
Medical image segmentation is typically used to locate boundaries of anatomical structures in images acquired by different modalities. As segmentation is of utmost importance for quantitative measurements and analysis of anatomical structures, tracking anatomical changes over time, building anatomical atlases and visualization of medical images, a huge amount of methods have been developed and tested on a wide range of applications in the past. Deformable or parametric shape models are a class of methods that have been widely used for segmentation. A drawback of deformable model approaches it that they require initialization near the final solution. In this paper, we present a segmentation algorithm that incorporates prior knowledge and is composed of two steps. First, reference points on the boundary of an anatomical structure are found by linear programming incorporating prior knowledge. Second, paths between reference points, representing boundary segments, are searched for by optimal control. The segmentation method has been applied to chest radiographs from the publicly available SCR database.
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Bulat Ibragimov, Boštjan Likar, and Franjo Pernuš "Boundary detection by linear programming with application to lung fields segmentation", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796231 (14 March 2011); https://doi.org/10.1117/12.877705
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