Abstract
This paper describes a lung field segmentation method, working on digital Postero-Anterior chest radiographs. The lung border is detected by integrating the results obtained by two simple and classical edge detectors, thus exploiting their complementary advantages. The method makes no assumption regarding the chest position, size and orientation; it has been tested on a non-trivial set of real life cases, composed of 412 radiographs belonging to two different databases. The obtained results and the comparison with more complicate techniques presented in the literature, prove the robustness of the algorithm and demonstrate that rather simple and general methods, properly combined to fit the requirements of a specific application, can provide better results.
Work partially financed by CIMAINA and PRIN 2004: “Novel clustering techniques in biomedical image segmentation”.
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Campadelli, P., Casiraghi, E. (2005). Lung Field Segmentation in Digital Postero-Anterior Chest Radiographs. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_81
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DOI: https://doi.org/10.1007/11552499_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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